To handle late-arriving dimensions, you can use strategies like creating "unknown" or placeholder dimension records, holding fact records in a temporary table until the dimension arrives, or using a reconciliation pattern to retry failed joins, ensuring data integrity and completeness.

Here's a more detailed breakdown of common approaches:

1. "Unknown" or Placeholder Dimensions:

Concept:
When a dimension record is missing, create a placeholder or "unknown" record in the dimension table with a default value for all attributes.

Benefits:

● Prevents fact data from being lost or incomplete.

● Allows fact data to be loaded and reported on even when dimensions are missing.

● Simplifies the process of updating dimension records when they arrive.

Example:
If a product dimension is missing, create a "product_id_unknown" record with default values for other attributes.

2. Hold Fact Records in a Temporary Table:

Concept:
When a dimension record is missing, store the fact record in a temporary table until the dimension is available.

Benefits:

● Ensures that all fact data is processed eventually.

● Simplifies the process of updating fact records when the dimension arrives.

Example:
Store fact records with missing product dimensions in a temporary table, and then re-process them when the product dimension is loaded.

3. Reconciliation Pattern:

Concept:
Use a process to identify and retry failed joins when dimensions are missing.

Benefits:

● Ensures that all fact data is processed eventually.

● Can be used with both streaming and batch ETL processes.

Steps:

● Write unjoined records to a "streaming\_pipeline\_errors" table.

● Consolidate multiple failed retries for the same event into a single fact row.

● Union Kafka and retryable events.

● Transform/join this unioned data with the dimension table.

● Control how many retries are allowed by filtering out reconciliation records that exceed the configured number of retries.

●

4. Other Considerations:

● Identify Slowly Changing Dimensions: Determine how to handle changes to dimension attributes over time.

●

● Use Surrogate Keys: Employ surrogate keys for dimension tables to simplify joins and manage slowly changing dimensions.

●

● Data Deduplication: Ensure that you are not duplicating fact data when retrying joins.

●

● Schema Evolution: Plan for changes to the schema of your dimension tables.

●

● Inferred Flag: Use a flag to indicate whether a dimension record is inferred

How snowflake is different from other databases or data warehouses

1. Separate "Storage" and "Brain" Power

Snowflake keeps storage and computing (the "brain") separate, so you can store data cheaply and only pay for computing power when you are actually using it. Unlike traditional databases that force you to pay for extra "brain" power whenever you need more storage, Snowflake keeps these costs independent.

2. No "Upkeep"

● Snowflake: It is "set it and forget it." Snowflake handles all the technical chores, software updates, and performance tuning for you automatically.

● Others: Traditional databases require a technical expert to constantly manage the hardware, install updates, and manually tune the system to keep it fast.

3. Handles "Messy" Data Easily

● Snowflake: It can read both neat, organized tables and "messy" data (like information from websites or apps) equally well, meaning you don’t have to spend time cleaning it before use.

● Others: Most traditional databases only work with neat, structured tables, so you must spend extra time and effort cleaning the data before it can be stored.

4. Only Pay for What You Use

● Snowflake: Because you can turn off the "brain" when you aren't using it, you stop paying for that power immediately.

● Others: You usually pay for a fixed amount of hardware capacity; even if the system is sitting idle, you are still paying for that unused power.

Tell me your knowledge on the 3 layers of ETL

● Extract: Gathering raw data from various sources without disrupting the original systems.

● Transform: Cleaning, formatting, and organizing the raw data so it is consistent and useful.

● Load: Saving the prepared, high-quality data into a final warehouse where it is ready for business analysis.

## Data Modeling Best Practices – Headings

------------------------------------------------------------------------------

click here for full post

1. Start with Business Requirements (Not Tables)

2. Define Grain First (Critical Rule)

3. Choose the Right Model Type

4. Use Clear Naming Conventions

5. Design Clean Dimension Tables

6. Keep Fact Tables Lean

7. Enforce Data Integrity

8. Handle Dates Properly

9. Optimize for Query Performance

10. Plan for Data Growth

11. Document Everything

12. Validate with Real Data

13. Version Control Your Models

14. Avoid Over-Normalization in Warehouses

15. Design for BI & Reporting Tools

Types of Fact tables

Types of Fact tables	1. Transactional Fact Tables Transactional fact tables are designed to capture individual business events or transactions. 2. Periodic Snapshot Fact Tables Periodic snapshot fact tables provide a summarized view of metrics over regular time intervals. 3. Accumulating Snapshot Fact Tables Accumulating snapshot fact tables are designed to track the stages of a business process or workflow. 4. Factless facts
types of fact tables 51	1. Additive ( can be added like total qty , sales ) 2. Semi additive ( cannot be added but updated like account balance ) 3. Non Additive ( numerical but cannot be added like today price of scotk )
Granularity and Granule	Granularity in data refers to the level of detail > high level of granularity would have a large number of individual pieces (columns) of information, such as individual records or measurements. >Data that has a low level of granularity would have a small number of individual pieces of information, such as summary data or aggregated data. Data granularity can affect how it is used and analyzed, and can impact the accuracy and usefulness of the results.
what are 3 levels of Data modeling	1. Conceptual model list of entities and relationships , no attributes diagram only 2. logical 3. physical model

Types of Dimension tables (30, 46,48)

1) SCD (slow changing )	2) RCD (Rapid changing )
3) JUNK
4) INFERRED .	5) DEFERRED
6) CONFORMED (multiple facts )
7) DEGENERATE (invoice)
8) ROLE PLAY (same meaning , multiple names)
9) STATIC	10) DYNAMIC
10. SPARSE	11) DENSE
12) SHRUNKEN

1 Roleplay dimension

Role playing dimensions are dimensions that are used more than once in a fact table, each time with a different meaning or role.

when we want two attributes from the source entity to be connected to 1 entity as destination, that is not possible. In such cases, we can connect both the columns of one table to 2nd table, but with a new role name that is called as role play dimension something like connecting address and customer table with 2 columns bill to address and ship to address

2 Conformed dimension

Conformed dimensions are dimensions that are shared by multiple stars

3 Degenerate dimension

A column of the key column in the fact table that does not have the associated dimension table but used for reporting and analysis, such column is called degenerate dimension

For ex, we have a fact table with customer_id, product_id, branch_id, employee_id, bill_no

4 Junk dimension

This combined dimension table covers all possible set of values which might occur in fact table and is known as junk dimension.5 Shrunken dimension

5 Static dimensions

Static dimension items do not change with time; they are fixed components that are always returned in a freeform table.

The time , date , country code , status codes etc

6. Dynamic dim

which change dynamically based on values and formula

7. Deferred dims

Sometimes the fact table have to be loaded with some data, like customer id. But the dimension of customer is not loaded with that customer id. In such cases, we delay inserting such rows into fact table And later on, once the the dimension of customer is loaded with this customerId, then we add this rows into fact table.

This type of delaying the fact table is called deferred dimensions

8 Inferred Dimensions

When the fact data is available to be loaded in the source but related dimension table does not have that

eg: like the customerId is in sales orders table but not in customer table .. in such case we add an sample entry in customer table and enable 'inferred dimension' flag and later point of time we make a original entry into customer table and disable the inferred dimension

9 A slowly changing dimension (SCD)

scd is a dimension which contains relatively static data which can change slowly but unpredictably, rather than according to a regular schedule

SCD

Type 0 Ignore any changes and audit the changes.

Type 1 Overwrite the changes

Type 2 History will be added as a new row.

Type 3 History will be added as a new column.

Type 4 A new dimension will be added

Type 6 Combination of Type 2 and Type 3

10 Rapidly changing dimensions (RCD)

Rapidly changing dimension (RCD) is a dimension which has attributes where values will be getting changed often.

11) sparse dim

Some of the dimension tables are not loaded with full values

12) dense dim

Sometimes most of the columns of the dimension tables are loaded, fully loaded. So such dimensions

13) shrunken dim

Sometimes we create a replica of the original dimension, but it is with lesser, fewer number of columns than the original table

What is factless fact table

A factless Most of the columns are empty. Such dimensions are called sparse dimensions fact table is a table that contains only foreign keys to dimensions, but no numeric facts. Factless fact tables are used to capture events or situations that have no measurable outcome

What is Data Mart

Data mart is a subset of datawarehouse purpose-built to support the analytical needs of a particular department, or line of business, or geographic region.

Steps in Datawarehouse life cycle steps

Requirements gathering

Requirements analysis

High level design (HLD)

Low level design (LLD)

Development – Automating scripts

Testing

User Acceptance Testing (UAT)

Project release or deployment

Warranty support

what is ODS( operational data store)

An operational data store usually stores and processes data in real time. An ODS is connected to multiple data sources and pulls data into a central location.

ODS is a temportary data store from all sources for volatile data like resumes of interview, quotation from suppliers ,etc. which need not be stored permanently

OLTP & OLAP- differences (pg 17) 53

https://easy-data-modeling.blogspot.com/2024/03/olap-oltp-diff.html

Data model testing how

Data model can tested using the following:

Unit testing: This involves testing each component of the data model individually to ensure that it performs as expected.

Integration testing: This involves testing how different components of the data model work together

using test data

Data modeler roles

the data from data source

identify connect the data source

pick a share data set

select storage mode identify the query performance issues

use or create a data flow

profile the data

identify data anomalies

examine data structures

clean transform load data

resolve inconsistencies and unexpected values null values data quality issues

apply value replacements identify the keys for joints

identify and transform column data types

apply naming conventions two columns

perform data loading

resolve data input errors

model the data

designer data model

define the tables

configure table and column properties

flatten the parent child hierarchy

role playing dimensions

define relationships and cardinality

design the data model

resolve many to many relationships

design a common date table

define the data level granularity

Layer of Data lake

landing layer

2. curated layer

3. application data layer ( summary data)

4. Sand box layer

5. Target layer

Views and materialized views

Views and materialized views are both database objects that provide access to data, but they differ in several ways:

Storage

Views are virtual and don't store data

, while materialized views store precomputed data as a physical table.

Performance

Views are more efficient because they don't require storage or updates,

but materialized views can be faster for complex queries and aggregations.

Data governance

Data governance is the process of managing the
availability, usability, integrity and security
of the data in enterprise systems,
based on internal standards and policies
that also control data usage.

Effective data governance ensures that
data is consistent and trustworthy
and doesn't get misused.

Data governance is increasingly critical as organizations are
expanding data privacy regulations and
rely more and more on data analytics
to help optimize operations and
drive business decision-making.

https://www.thoughtspot.com/data-trends/data-governance/how-to-implement-data-governance

https://data-modeling-made-easy.blogspot.com/2024/08/how-to-implement-data-governance.html

ELT (Extract, Load, Transform) advantages

ELT is better suited for larger data sets, situations where timeliness is crucial, or when you must keep raw data available for future analysis. For instance, ELT is faster than ETL because it loads data directly into the destination system and can perform transformations in similarity.

What is a Data Lake?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is,

A data lakehouse

is a data architecture that combines a data lake and a data warehouse to provide a flexible solution for business intelligence and data science. It allows organizations to use low-cost, flexible storage for all types of data, while also benefiting from the data management, schema, and governance of a warehouse.

Data lakes and data warehouses are both storage systems for big data used by business analysts, data engineers, and data scientists. They have different architectures and serve different business needs:

Data lakes
Store raw, structured, and semi-structured data from internal and external sources. They are good for data analytics, machine learning, experimenting with data, and training AI models. Data lakes prioritize storage volume and cost over performance, and can store any type or volume of data in full fidelity. They also allow users to ingest data from any system at any speed, and process data in real time or batch mode.

Data warehouses
Store structured data from multiple sources. They are good for business intelligence (BI), data visualizations, reporting, and accessing reliable, quality data. Data warehouses are designed for the fastest query performance, and are optimized to analyze relational data coming from transactional systems and line of business application

68. When to use star schema and when to go for snowflake ?

If you have a few dimensions and low cardinality (less unique values in dims ) , but you require fast query execution, a star schema is the right choice.

However, if you have several dimensions and high cardinality, a snowflake schema will be a better scheme

features of snowflake db

Snowflake can seamlessly integrate with popular cloud providers such as AWS, Azure, and Google Cloud Platform. It can be scaled up or down automatically, ensuring efficient data loading, integration, and analysis. This allows multiple users to run numerous workloads simultaneously without concerns about resources

https://airbyte.com/data-engineering-resources/snowflake-features#:~:text=Snowflake%20can%20seamlessly%20integrate%20with,simultaneously%20without%20concerns%20about%20resources.

https://docs.snowflake.com/en/user-guide/intro-supported-features

https://staragile.com/blog/snowflake-features

A Compact List of Snowflake Features

● Decoupling of storage and compute in Snowflake

● Auto-Resume, Auto-Suspend, Auto-Scale

● Workload Separation and Concurrency

● Snowflake Administration

● Cloud Agnostic

● Semi-structured Data Storage

● Data Exchange

● Time Travel

● Cloning

● Snowpark

● Snowsight

● Security Features

● Snowflake Pricing

Data vault qsns

https://data-modeling-made-easy.blogspot.com/p/data-vault-faq.html

Erwin qsns

ERWIN qsns
Erwin - Data modeler workspace	is the space where the Tables are created and connected
Erwin - Domains	A domain is like a custom datatype an independent model object that you can use to quickly assign properties to an attribute or column
Erwin - types of relationships	Identifying relationship. Non-identifying relationship. Subtype relationship. Many-to-many relationship. Recursive relationship.
Erwin - which erwin features did you use	1. Name hardening 2. Domains for Data types 3. Naming standards( abbrevations) 4. Fwd & reverse engineering 5. Merge data models 6. complete compare 7. sub type and super type
Erwin - which feature used to merge the subtype tables to supertype
Erwin - Forward and reverse engineering steps	Forward engr steps : Actions > schema > Generate scripts Reverse engnr steps : Actions > reverse engnr > logical / physical > Database (or) script file > connect > ok
Erwin - Complete compare feature	Complete Compare is a powerful tool that lets you view and resolve the differences between two models, or a model and a database or script file Actions > complete compare wizared > source target models select > compare > final > resolve differenceces
Erwin - Naming standards	Use the Naming Standard Editor to create naming standards and develop a glossary that contains glossary words, their abbreviations, and alternate abbreviations. Access the Model Explore> open Naming Standard Editor > Define naming standards for logical and physical models> Create abbreviations
Erwin - Name hardening	Name hardening is a mechanism in Erwin Data Modeler that prevents physical model names from changing when logical names change
Erwin - querying tool
Erwin - subject areas	Click Model, Subject Areas. The Subject Area Editor opens. Click New. A new subject area
Erwin - identifying and non identifying relationships differences...........	Identifying : Represented by a solid line, the primary key of the parent migrates to the primary key area of the child entity or table eg : Book & chapter tables , Country and state tables Non-identifying : The child entity is not dependent on the parent entity for its identity and can exist without it .. eg : supplier & product list ,

ERWIN - how maintain versions

Two types of model versions exist:

Delta Version: A delta version is created when you initially save a new model or when you save changes to an existing model.

If you do not want to maintain multiple versions, you can clear the Maintain multiple versions check box. If you clear the checkbox, then erwin� Data Modeler does not create a delta version for any incremental save. erwin� Data Modeler updates the current version every time you save the model.

Named Version:

A named version is created from a delta version, or from another named version. A named version represents a milestone in the development of the model, or at least a version that you want to keep indefinitely. When you save the changes in a named version, the changes are saved in the same version.

The Based upon field in the Edit Catalog pane displays the version number from which the Named version is created.

=========================================================================

Create a Delta Version

You create a delta version when you save a model initially or when you incrementally save an existing model.

Follow these steps:

Click File, Mart, Open >
> The Open Model dialog opens. > Select a model and click OK.
> The model opens.
>Make necessary changes to the model.
>Click File, Mart, Save.
A delta version of the model is created with the incremental changes.

Create a Named Version

A named version of a model represents a milestone in the development of the model. You create a named version to keep that model version indefinitely.

Follow these steps:

Click File, Mart, Catalog Manager .
> The Catalog Manager opens. > Select a model version,
>right-click and click Mark Version.
A named version is created with a default name.
> Edit the name of the named version (new name ) and press enter.
A new named version is created in the catalog.

=========================================================================

Compare Model Versions

Compare two model versions of a data model to view the differences between them.

Follow these steps:

Click File, Mart, Catalog Manager. > The Catalog Manager dialog opens.
Hold the Ctrl key and select two model versions of a model and
> click Version Compare. >Click Compare.
The Complete Compare wizard opens. >.
The Resolve Difference dialog opens. Review the differences and use the tools on the toolbar to work with and manage the changes. > Finish > Click Close.

SQL qsns

2 . Sql questions

1. > unique key and primary key diff

2. query to find number of subjects of each student

a. ========================================================

b. (name ) , (marks )

c. student1 20,30,40,50

d. student2 30,40

e. student3 30,40,55

3. Peoplefy - How to design facts and dimensions

4. Peoplefy - sql query to find sum

a. sql query to find sum of all sales on 1-1-2024 with id 101 who have

c. orderid qty date

d. 101 150 1-1-2024

e. 101 15 1-1-2024

f. 102 22 2-1-2024

5. query to find length of each column and subtract total commas from it

5.

SELECT name,

CASE

WHEN marks = '' OR marks IS NULL THEN 0

ELSE (LENGTH(marks) - LENGTH(REPLACE(marks, ',', '')) + 1)

END AS subject_count

FROM students;

select name , (REPLACE(marks, ',', '') )from students;

select name , marks from students;

Query to find third highest ?

To find the fourth highest salary by each department, you can use a combination of SQL queries with ROW_NUMBER or DENSE_RANK functions. Here's an example:

Using ROW_NUMBER (all sequential ranks ):

select employee_id,salary from (

select e.*,row_number() over (order by salary desc)

as row_num from employee e ) a

where row_num = 3 ;

Using RANK (for duplicate ranks , next rank = prev rank + total dups + 1):

select * from

(select e.*,rank() over (order by salary desc) as rank

from emp e )

where rank = 3;

Using DENSE_RANK (for duplicate ranks , next rank = prev rank + 1):

select * from

(select e.*,dense_rank() over (order by salary desc) as rank

from emp e )

where rank = 3;

6. Data model constraints ?

Data model constraints refer to rules or limitations designed to maintain the integrity and validity of data within a database or system. Here are some common types of constraints:

1. Entity Constraints:

● Primary Key: Ensures that each row in a table is unique and identifiable (e.g., no duplicate or NULL values in the primary key column).

● Unique Constraint: Ensures all values in a column are unique, but unlike the primary key, it allows one NULL value.

2. Domain Constraints:

● Restrict the type, format, or range of values for a column (e.g., specifying data types like INTEGER, VARCHAR, or a range for a numeric field).

3. Referential Constraints:

● Foreign Key: Ensures a column’s value matches the primary key of another table, maintaining referential integrity between tables.

4. Check Constraints:

● Specifies a condition that each row must satisfy (e.g., salary > 0 or age >= 18).

5. Default Constraints:

● Assigns a default value to a column if no value is provided during insertion.

6. Not Null Constraint:

● Ensures that a column cannot have NULL values (e.g., name column must always have a value).

7. Functional Dependencies:

● Enforces relationships between attributes in a table, often used for normalization.

8. Key Constraints:

● Besides primary and unique keys, these may involve composite keys (combination of columns acting as a primary key).

9. Business Rules:

● Application-specific rules tailored to the business logic (e.g., an employee can only belong to one department).

10. Performance Constraints:

● Limitations imposed to enhance query performance, such as partitioning or indexing strategies.

These constraints ensure data reliability, prevent inconsistencies, and enforce rules for both operational and analytical use cases.

----------

=============================

28aug25 - Teksystems

=============================================================

Teksystems - in data ware house how do you update the history table with scd2 if the last updated data was corrupt and to update the data with correct values

When a corrupt record needs to be updated in a Type 2 Slowly Changing Dimension (SCD2) history table, you don't simply "update" the existing record. Instead, you follow a specific process that maintains a complete history of changes.

The Process for Correcting a Corrupt Record

The standard SCD2 method for handling changes is to mark the old record as inactive and insert a new, active record. The same principle applies here to correct a corrupt entry. Here's a step-by-step breakdown:

Identify the Corrupt Record: Find the record in the SCD2 table that contains the incorrect or corrupt data. This record will have its end_date set to a future or '9999-12-31' value, and its is_current flag (or similar) set to true.
Expire the Corrupt Record: The first step is to "end" the active life of the corrupt record. You do this by performing an UPDATE operation on the row. You will:

● Set the end_date of the corrupt record to the current date, or the date the correction is being applied.

● Set the is_current or current_flag to false.

Insert the Correct Record: After the corrupt record is marked as inactive, you must INSERT a new row into the history table. This new record will contain the correct values for all the attributes. This new row will have:

● The same primary key as the original record (often a business key).

● The new, correct data for all relevant columns.

● A start_date set to the current date (or the date of correction).

● An end_date set to a future or '9999-12-31' value.

● An is_current or current_flag set to true.

Teksystems - query : find and delete duplicate rows Two methods to find and delete duplicate rows

-=================================

1. Delete all rows other than min id value ..

(used when id column is not duplicate but other columns values are duplicate)

===========================================

Method 1. using id Column (if ID column is unique, but title is duplicate)

================================

delete films

where film_id

not in (select min(film_id) from films group by title, rel_date)

===========================================

Method 2. using CTE ( Two steps)

============================================

step 1 :

with CTE as

(select id ,name , email , row_number()

over (partition by name , email order by name , email ) rownum

from sales

step 2 :

Delete from CTE where rownum > 1

===========================================

Method 3. using ROWID

============================================

Delect from table1

where rowid not in (select min(rowid) from table 1

group by col1,col2)

========================================================================

what is inner join and outer join count of below

========================================================================

A B

1 1

2 2

null null Inner join = count of matching rows

outer join = inner join + left side unmatched

inner = 6 rows

left outer = 6 + 1 = 7 rows

=======================================================================

53 . How to write the INNER JOIN ,OUTER JOIN QUERIES

=======================================================================

-- // Create database //

use db1;

show tables;

create table t1 ( id int , name varchar(44) ) ;

create table t2 ( id int , name varchar(44) ) ;

-- // insert data into tables //

insert into t1 values( 1, "x1");

insert into t1 values( 2, "x2");

insert into t1 values( 3, "x3");

insert into t1 values( 4, "x4");

select * from t1;

insert into t2 values( 1, "x1");

insert into t2 values( 2, "x2");

insert into t2 values( 5, "x5");

insert into t2 values( 6, "x6");

select * from t2;

------------------

-- // inner join //

select * from t1 join t2 on t1.id = t2.id

-- // left join //

select * from t1 left join t2 on t1.id = t2.id

-- // Right join //

select * from t1 right join t2 on t1.id = t2.id

- // fuill outer join //

select * from t1 left join t2 on t1.id = t2.id

union

select * from t1 right join t2 on t1.id = t2.id

================================================================

54 . How to find cumulative check - if employee got hike or not

================================================================

-- --// create employee table // --

create table employee (employee_id int ,name varchar(44),

monthofsalary date,

salary int);

-- alter table employee rename column month to monthOfSalary;

-- --// insert data to employee table // --

-- truncate table employee;

insert into employee values(1,"john",'2004-01-01',13000);

insert into employee values(1,"john",'2004-02-01',13000);

insert into employee values(1,"john",'2004-03-01',13000);

insert into employee values(1,"john",'2004-04-01',13000);

insert into employee values(1,"john",'2004-05-01',13000);

insert into employee values(1,"john",'2004-06-01',13000);

insert into employee values(1,"john",'2004-07-01',14000);

insert into employee values(1,"john",'2004-08-01',15000);

insert into employee values(1,"john",'2004-09-01',15000);

-- --// show data in employee table // --

select employee_id,name,month(monthofsalary),salary from employee;

-- -------------// query to find cumulativ // ------------------------------

WITH salarychanges

AS (SELECT employee_id,

name,

monthofsalary,

salary,

Lag(salary, 1)

over (

PARTITION BY employee_id

ORDER BY monthofsalary) AS previous_salary

FROM employee)

SELECT employee_id,

name,

monthofsalary,

salary,

CASE

WHEN salary > previous_salary THEN 'yes'

ELSE 'no'

END

AS salary_hike,

SUM(salary)

over (

PARTITION BY employee_id

ORDER BY monthofsalary ROWS BETWEEN unbounded preceding AND CURRENT

ROW) AS

cumulative_salary

FROM salarychanges;

55. How to find if a single user purchased same product on different dates

create table orders( userid int ,prodid int, orderdate date);

insert into orders values ( 1,101,'2024-1-1');

insert into orders values ( 1,101,'2024-1-2');

insert into orders values ( 1,101,'2024-1-1');

insert into orders values ( 2,102,'2024-1-1');

select * from orders;

-- --------------------------------------------------------------------------------

-- // count of users who purchased same product on different dates //

-- --------------------------------------------------------------------------------

SELECT userid, prodid, COUNT(DISTINCT orderdate) AS Noofdays_purchased

FROM orders GROUP BY userid,prodid;

================================================================

SQL - show highest salary in each department

================================================================

SQL - count(*) , count(column) , count(1)

================================================================

select count(*),count(distinct deptid) from emp;

================================================================

-- BOTH COUNT(*) AND COUNT(1) ARE SAME BUT COUNT(1) IS FASTER

================================================================

===========================================================

==========================================================

================================================================

Query TASKS lists

================================================================

10.Display duplicate Records

11.Display Top 4th highest Salary

12.Find 2nd lowest salary

13.Display Top Two salaries in each department wise

14.Find each department wise sum of salaries

15.Display maximum salary department wise along with employee details

16.to display 5 to 7 rows from a table

================================================================

SQL FUNCTIONS

================================================================

SQL - CONSTRAINTS

================================================================

● Not NULL

● Unique

● Primary key

● foreign Key

● Check (check columnname > 0 )

================================================================

sql query : TO get highest ordered products in each state

==============================================================

CREATE DATABASE retail_db;

USE retail_db;

-- ----------------------------------------------

CREATE TABLE orders_table

(

order_id INT NOT NULL,

customer_id INT NOT NULL,

product_id INT NOT NULL,

quantity INT NOT NULL,

price DECIMAL NOT NULL

);

CREATE TABLE customer_table

(

customer_id INT NOT NULL,

customer_name VARCHAR(50),

state_name VARCHAR(50)

);

-- ----------------------------------------------

INSERT INTO orders_table VALUES

(

101,1001,202,4,1000

) ;

INSERT INTO orders_table VALUES

(

102,1002,203,5,2000

);

INSERT INTO orders_table VALUES

(

103,1003,204,6,3000

);

INSERT INTO orders_table VALUES

(

104,1004,205,7,4000

);

SELECT * FROM orders_table;

-- ----------------------------------------------

INSERT INTO customer_table VALUES

(

1001,

'Liam',

' Alabama'

);INSERT INTO customer_table VALUES

(

1002,

'Noah',

' Alaska'

);INSERT INTO customer_table VALUES

(

1003,

'Oliver',

' Arizona'

);INSERT INTO customer_table VALUES

(

1004,

'James',

' Arkansas'

);SELECT *

FROM customer_table;

-- ----------------------------------------------

TRUNCATE customer_table ;

-- ----------------------------------------------

SELECT c.state_name,

o.product_id,

Count(o.order_id) AS highest_order_count

FROM orders_table o

JOIN customer_table c

ON o.customer_id = c.customer_id

GROUP BY c.state_name,

o.product_id

ORDER BY c.state_name,

highest_order_count DESC

-- =========================================================

-- == OR TRY BELOW === ---

WITH ordercounts

AS (SELECT c.state_name,

o.product_id,

Count(o.order_id) AS order_count,

Rank()

OVER (

partition BY c.state_name

ORDER BY Count(o.order_id) DESC) AS ranka

FROM orders_table o

JOIN customer_table c

ON o.customer_id = c.customer_id

GROUP BY c.state_name,

o.product_id)

SELECT state_name,

product_id,

order_count

FROM ordercounts

WHERE ranka = 1;

-- ----------------------------------------------

VARCHAR AND NVARCHAR difference

In database contexts, VARCHAR and NVARCHAR are data types used to store variable-length text strings.

The key difference lies in their encoding:

VARCHAR uses single-byte character sets (like ASCII),

while NVARCHAR

uses Unicode, allowing for a wider range of characters including those from multiple languages. NVARCHAR typically requires more storage space and has a shorter maximum character length compared to VARCHAR

what is most likely reason that sql query may run slowly

Possible Reasons for Slow SQL Queries

1. Lack of Indexes: I

2. Complex Joins:

3. Large Data Volume:

4. Inefficient Query Structure:

5. Database Configuration: Configuration settings such as memory allocation, cache size, and connection limits can affect performance.

6. Server Load: High server load due to concurrent queries or insufficient resources (CPU, memory, I/O) can slow down execution times.

7. Network Latency: If the database is remote, network latency can add to the time it takes to execute a query.

8. Statistics Outdated: Database optimizers rely on statistics to create execution plans.

9. Locking and Blocking: If a query is waiting for locks held by other transactions, it can slow down execution.

Types of Joins and Their Impact on Execution Time

1. INNER JOIN: Generally efficient if both tables have indexes on the join columns.

2. LEFT JOIN (or RIGHT JOIN): These can be slower than INNER JOINS because they need to return all records f

3. FULL OUTER JOIN: Typically the slowest join type, as it combines the results o

4. CROSS JOIN: This join produces a Cartesian product, It's usually slower and should be used with caution.

5. SELF JOIN: While not inherently slow, performance

Optimization Tips

● Indexing:

● Analyze Execution Plans: Use tools provided by your database (like EXPLAIN in PostgreSQL) to

● Refactor Queries: Simplify complex queries,

●

● Monitor Server Performance:

types of indexes 53

1. NORMAL INDEX

2. BITMAP INDEX

3. UNIQUE & NON UNIQUE INDEX

4. CLUSTERED & NON CLUSTERED

5. PARTITIONED & NON PARTITIONED

6. EXPRESSION BASED

7. B- TREE

- sql query : find and delete duplicate rows

Delete all rows other than min id value ..(used when id column is not duplicate but other columns values are duplicate)

===========================================

Method 1. using id Column (if ID column is unique, but title is duplicate)

================================

delete films

where film_id

not in (select min(film_id) from films group by title, rel_date)

===========================================

Method 2. using CTE ( Two steps)

============================================

step 1 :

with CTE as

(select id ,name , email , row_number()

over (partition by name , email order by name , email ) rownum

from sales

step 2 :

Delete from CTE where rownum > 1

===========================================

Method 3. using ROWID

============================================

Delect from table1

where rowid not in (select min(rowid) from table 1

group by col1,col2)

SQL : query to show Total salary of each department

SELECT

Id, empname, deptid, Salary,

SUM(Salary) OVER(PARTITION BY deptid) AS SUM_SAL

FROM

emp ORDER BY id

===========================

# Id, empname, deptid, Salary, SUM_SAL

'1', 'john', '1', '20000', '50000'

'2', 'kem', '1', '30000', '50000'

'3', 'chan', '2', '10000', '30000'

'4', 'henry', '2', '20000', '30000'

'5', 'bill', '3', '30000', '30000'

'6', 'salt', '4', '20000', '20000'

SQL : query to sum of salary by dept and

cumulative salary by each dept

SELECT deptid,salary ,

Sum(salary)

OVER(

partition BY deptid

ORDER BY id) AS CUMulative_SAL

FROM emp

ORDER BY id

===============================

# deptid, salary, CUMulative_SAL

'1', '20000', '20000'

'1', '30000', '50000'

'2', '10000', '10000'

'2', '20000', '30000'

'3', '30000', '30000'

'4', '20000', '20000'

Optimization and Tuning

How do you do Data model optimization ?

Data model optimization involves refining a data model to improve its performance, maintainability, and scalability. Here are some essential techniques to consider:

1. Normalization and Denormalization:

● Normalization: Organize data to eliminate redundancy and dependency by splitting tables and defining relationships.

● Denormalization: Combine tables to improve query performance, especially in read-heavy systems, such as reporting systems.

2. Indexing:

● Use indexes (e.g., clustered, non-clustered, or composite) to accelerate data retrieval and improve query performance.

● Be cautious with excessive indexing, as it can impact write operations.

3. Partitioning:

● Divide large tables into smaller, more manageable chunks (e.g., by date or region).

● Enhances performance for queries targeting specific partitions.

4. Data Caching:

● Use caching mechanisms to store frequently accessed data in memory for faster retrieval.

● Tools like Redis or Memcached can reduce database load.

5. Schema Optimization:

● Simplify the schema design, ensuring it aligns with the specific requirements of the application.

● Use schemas like star or snowflake for analytical workloads.

6. Materialized Views:

● Precompute and store complex query results to improve performance for frequently executed queries.

7. Query Optimization:

● Refactor queries to eliminate unnecessary joins or subqueries.

● Avoid selecting unnecessary columns (SELECT * should be avoided).

8. Data Types:

● Choose appropriate data types for each column to reduce storage size and improve performance.

9. Archiving and Purging:

● Move historical or less-used data to archive storage, ensuring the active database operates efficiently.

10. Concurrency Control:

● Ensure your database can handle multiple simultaneous transactions without bottlenecks or conflicts.

11. Monitoring and Feedback:

● Use database performance monitoring tools to identify and address bottlenecks, such as long-running queries or underutilized indexes.

what is most likely reason that sql query may run slowly

Possible Reasons for Slow SQL Queries

1. Lack of Indexes: I

2. Complex Joins:

3. Large Data Volume:

4. Inefficient Query Structure:

5. Database Configuration: Configuration settings such as memory allocation, cache size, and connection limits can affect performance.

6. Server Load: High server load due to concurrent queries or insufficient resources (CPU, memory, I/O) can slow down execution times.

7. Network Latency: If the database is remote, network latency can add to the time it takes to execute a query.

8. Statistics Outdated: Database optimizers rely on statistics to create execution plans.

9. Locking and Blocking: If a query is waiting for locks held by other transactions, it can slow down execution.

Types of Joins and Their Impact on Execution Time

1. INNER JOIN: Generally efficient if both tables have indexes on the join columns.

2. LEFT JOIN (or RIGHT JOIN): These can be slower than INNER JOINS because they need to return all records f

3. FULL OUTER JOIN: Typically the slowest join type, as it combines the results o

4. CROSS JOIN: This join produces a Cartesian product, It's usually slower and should be used with caution.

5. SELF JOIN: While not inherently slow, performance

Optimization Tips

● Indexing:

● Analyze Execution Plans: Use tools provided by your database (like EXPLAIN in PostgreSQL) to

● Refactor Queries: Simplify complex queries,

●

● Monitor Server Performance:

Query optimization best practices

When optimizing queries, best practices include:

+ using indexes effectively,

+ avoiding SELECT * from ... instead use limit 10 ,

+ filtering data early in the WHERE clause,

+ choosing appropriate JOIN types,

+ minimizing subqueries ,

+ utilizing stored procedures,

+ monitoring query performance time,

+ and selecting the correct data typeis instead of varchar and conversion

+ always review and analyze execution plans to identify areas for improvement.

Here are the

best practices for SQL query optimization summarized in one-liners:

● Indexes: Apply indexes strategically to columns used in filtering, joining, and sorting to accelerate data retrieval.

● WHERE vs. HAVING: Prioritize the WHERE clause to filter rows before aggregation, as it is significantly more efficient than HAVING.

● Avoid Loops: Replace iterative row-by-row processing with set-based bulk operations to minimize overhead.

● Selective SELECT: Retrieve only necessary columns by avoiding SELECT * to reduce network traffic and database I/O.

● EXPLAIN Plan: Use the EXPLAIN keyword to analyze and debug execution paths to identify performance bottlenecks.

● Wildcard Placement: Place wildcards at the end of search strings (e.g., LIKE 'Value%') to allow the database to utilize indexes effectively.

● EXISTS over COUNT: Utilize EXISTS() to confirm record presence, as it terminates immediately upon finding the first match.

● Avoid Cartesian Products: Always include specific JOIN conditions to prevent unintentional cross-joins that generate massive, unnecessary datasets.

● Denormalization: Selectively introduce redundancy to reduce the number of costly JOIN operations for read-heavy workloads.

● Optimize JOINs: Ensure all join columns are properly indexed and select the most efficient JOIN type for your data relationship.

best practices for data modeling summarized in one-liners:

● Grain: Define the lowest level of granularity first to ensure the model can support both atomic details and summarized insights.

●

● Naming: Establish and strictly adhere to a consistent naming convention, utilizing schemas to logically organize namespaces by business unit or data source.

●

● Materialization: Carefully balance the trade-off between tables (precomputed, faster performance) and views (real-time, potentially slower performance) based on your specific use case.

●

● Permissions and Governance: Collaborate with security teams to ensure the model strictly complies with industry-specific regulations and data privacy requirements.

Project based qsns

Project based questions

---

1-- . Hospital project data model

1 . About Hospital project

The Requirement:

"The main goal was to build a system that helps a hospital handle things like patient check-ins, doctor schedules, and medical records.

The biggest challenges were making sure the data stays accurate across different departments, managing tricky connections—like linking many patients to many different medications—and keeping sensitive patient information safe and private.”

The Design Approach: "To handle this, I broke the design process down into a few simple steps:

● Mapping it out: I started by identifying the main parts of the system, like

■ Patients

■ Doctors,

■ Appointments,

■ and Departments.

■

○ Diagram

■ I drew a diagram to see how they all connect, which helped me track a patient’s journey from the moment they arrive to when they leave.

● Keeping it organized:

■ I organized the data with normalization to avoid duplication. For example, I kept doctor details in one place and appointment records in another. This way, if a doctor’s info changes, we only have to update it in one spot, which prevents mistakes.

● Handling connections:

■ For complex parts, like connecting patients to their medications, I used 'bridge' tables. This allowed me to keep track of the relationship while also saving extra details like the dosage and how often the medicine needs to be taken.

● Setting clear rules:

■ Finally, I added built-in rules to the database. Things like making sure two appointments don't overlap for the same doctor or ensuring a patient ID is always there. This keeps the data clean and prevents the system from saving wrong or incomplete information.”

2 . The Patient Visit Lifecycle

To design a system that truly works, I mapped out the end-to-end journey of a patient. By understanding these specific stages, I could ensure the database captured the right data at every touchpoint. Here is how I explained that lifecycle during the project:

Registration (Front Desk): The lifecycle begins when a patient arrives. We capture demographics, insurance details, and the reason for the visit. This creates a record in the Patients table and initializes an Encounters record to track the visit's start.
Triage & Clinical Examination: The patient meets with nursing staff for vitals (blood pressure, temperature, etc.) and then the doctor. The doctor records observations, diagnoses, and orders. These are stored in Encounters, Medical_Records, and Diagnosis tables.
Advanced Procedures (Surgeries/Labs): If the doctor determines a surgery or lab work is needed, a new record is created in Surgery_Schedules or Lab_Orders.

○ Surgery: We track the operating room, the surgical team, and anesthesia details.

○ Lab: Results are logged in Lab_Results once the test is completed, linking back to the original Encounters ID.

Pharmacy & Medication: If medication is prescribed, the doctor creates a record in the Prescriptions table. This links the specific drug, dosage, and duration to the patient. The pharmacy team updates the status of these prescriptions to "Fulfilled" or "Dispatched."
Discharge & Final Billing: Once the treatment is complete, the Encounters status is updated to "Closed." The system automatically aggregates all charges—consultation fees, surgery costs, lab tests, and medications—into the Billing_Invoices table.
Payment Processing: The final stage is financial. The Payments table tracks the amount paid by the patient and the amount covered by their insurance provider, effectively closing out the financial lifecycle of that specific visit.

Data Traceability

By following this lifecycle, I ensured that every Patient_ID and Encounter_ID acted as the "thread" that connects all these tables. This allowed us to generate a complete history for any patient, showing exactly what happened from the moment they walked in the front door to the moment they paid their bill.

3 . How we identified the entities

The Requirement: "The main goal was to build a system that helps a hospital handle things like patient check-ins, doctor schedules, and medical records. The biggest challenges were making sure the data stays accurate across different departments, managing tricky connections—like linking many patients to many different medications—and keeping sensitive patient information safe and private."

How the Client Explained Business Processes: "I started by sitting down with the hospital staff to map out their daily workflow. They walked me through the lifecycle of a patient visit—from the front desk registration, through the examination with a doctor, to the final billing and pharmacy stage. By listening to their 'pain points'—like scheduling conflicts and missing patient history—I was able to identify exactly which data points needed to be captured at each step."

How We Identified Entities: "To turn those workflows into a model, I looked for the 'nouns' in their descriptions. If they talked about a 'doctor,' 'patient,' 'room,' or 'medication,' those became my primary entities. I then identified the actions connecting them (like 'schedules,' 'visits,' or 'prescribes') to determine the relationships between those entities."

Designing the Conceptual Model (Facts and Dims): "For the analytical side of the project, I designed a star schema. I treated 'Appointments' or 'Medical Procedures' as the Fact table, which contains the quantitative data (like visit counts or total costs). Around this, I created Dimension tables for 'Patients,' 'Doctors,' 'Time,' and 'Department.' This setup allows the hospital to easily pull reports, like 'How many patients did Dr. Smith see in the Pediatrics department last month?'"

Tables in OLTP vs. OLAP: "I separated the databases to ensure high performance:

● OLTP (Operational Database): I used about 15 to 20 tables. This was designed for day-to-day operations—fast inserts and updates for things like checking in a patient or updating a record.

● OLAP (Analytics/Data Warehouse): I simplified this down to about 6 to 8 core tables. By using a star schema, we reduced the number of joins, making it much faster for leadership to run complex queries and generate business intelligence reports."

The Design Approach: "To handle this, I broke the design process down into a few simple steps:

Mapping it out: I used a diagram to see how all parts connect, tracking a patient’s journey from the moment they arrive to when they leave.

Keeping it organized: I organized the data to avoid duplication, ensuring that if a doctor’s info changes, we only update it in one spot.

Handling connections: For complex parts, like connecting patients to their medications, I used 'bridge' tables to store extra details like dosage and frequency.

Setting clear rules: I added built-in rules, like preventing overlapping appointments, to keep the data clean and error-free."

4 . Tables list of OLTP and OLAP

4.1. ) OLTP tables list : Operational Database (approx. 15–20 tables)

These tables are normalized to ensure there is no data redundancy, making it easy for staff to update records without errors.

Patients (Personal info)
Doctors (Staff credentials)
Departments (Hospital wards/units)
Appointments (Date, time, status)
Medical_History (Chronic conditions)
Encounters (Each specific visit)
Staff_Roles (Job titles/permissions)
Rooms (Bed/room availability)
Medications (Drug catalog)
Prescriptions (The link between patients and drugs)
Lab_Tests (Available test types)
Lab_Results (Individual patient test outcomes)
Billing_Invoices (Financial records)
Payments (Transaction logs)
Insurance_Providers (Insurance company details)
Patient_Insurance (Linking patients to policies)
Schedules (Doctor shift patterns)
Referrals (Records of outside consults)

4.2 ) OLAP tables list: Data Warehouse ( facts, dims )

(6–8 core tables)

For the analytical model (Star Schema), we centralize the data. We use Fact tables to hold the "numbers" (metrics) and Dimension tables to hold the "descriptions" (filters).

4.2.1) Fact Tables (The focus of your analytics):

Fact_Appointments: (KPIS : Count of visits, wait times)
Fact_Lab_Results: ( : Normal/abnormal count, test duration)
Fact_Prescriptions: ( : Medication frequency, prescription volume)
Fact_Billing: ( : Total revenue, insurance coverage amounts)
Fact_Bed_Occupancy: ( : Duration of stay, room turnover)

5 . KPIS and FACTS

● Fact_Appointments

○ KPIs: Count of Appointments, No-show Rate, Average Wait Time, Total Scheduled Duration.

○ Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Department, Dim_Location.

● Fact_Lab_Results

○ KPIs: Count of Tests Performed, Average Turnaround Time, Abnormal Result Rate.

○

○ Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Test_Type
.

● Fact_Prescriptions

○ KPIs: Count of Prescriptions, Total Medication Cost, Average Dosage Frequency.

○ Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Medication.

● Fact_Billing

○ KPIs: Total Billed Amount, Total Collected Amount, Outstanding Balance, Insurance Reimbursement Rate.

○ Dimensions: Dim_Date, Dim_Patient, Dim_Department, Dim_Insurance.

● Fact_Bed_Occupancy

○ KPIs: Bed Occupancy Rate, Average Length of Stay (ALOS), Patient Turnover Rate.

○ Dimensions: Dim_Date, Dim_Patient, Dim_Department, Dim_Location.

6 . KPI Definitions

Average Wait Time: Measures the average time a patient spends waiting.

No-show Rate: Tracks the percentage of missed appointments.

Average Turnaround Time: Measures the time from ordering a lab test to getting results.

Abnormal Result Rate: Tracks the percentage of lab results outside of normal ranges.

Total Medication Cost: Calculates the total cost of all prescribed medications.

Average Dosage Frequency: Tracks how often a medication is administered to patients.

Total Billed Amount: Measures total revenue from all hospital services.

Insurance Reimbursement Rate: Calculates the percentage of bills covered by insurance.

Bed Occupancy Rate: Measures the percentage of available beds currently in use.

Average Length of Stay (ALOS): Tracks the average number of days a patient stays in the hospital.

---

\\ 2 . Insurance project and how you build data model

1 . Insurance Project Data Model Specification

1.1 About the Insurance Project

The core objective was to build a system capable of managing the entire insurance lifecycle—from policy enrollment to complex claims adjudication and provider reimbursement. The primary challenges included managing longitudinal patient histories, enforcing complex business rules for coverage verification, and ensuring strict compliance with healthcare privacy regulations.

1.2Design Approach

● Mapping the Lifecycle: We identified key touchpoints: Enrollment, Policy Issuance, Provider Network Verification, Claims Submission, Adjudication, and Payment Reconciliation.

● Normalization (OLTP): Data is organized into 3rd Normal Form (3NF) to eliminate redundancy. Each entity (Patient, Provider, Policy) exists in a single source of truth, ensuring that updates to records (e.g., policy renewal) propagate correctly.

● Handling Complex Relationships: We utilized "bridge" tables (e.g., Claim_Line_Items, Patient_Insurance) to handle many-to-many relationships, storing specific attributes like coverage ratios or procedure-level costs.

● Business Rules & Integrity: Constraints such as effective_date < expiry_date and mandatory Provider_ID association ensure clean, audit-ready data.

2. The Patient-Insurance Lifecycle

To ensure the database captures every relevant touchpoint, we mapped the patient’s financial journey:

Enrollment: Patient/Member demographic data is captured and linked to an Insurance_Provider record.
Policy Activation: A Policy is generated, defining coverage limits and plan types (HMO/PPO).
Claim Submission: During an Encounter, the provider submits a claim header with service details.
Adjudication: The system checks the claim against Plan rules (deductibles, out-of-pocket maximums).
Billing & Reimbursement: Billing_Invoices are generated, tracking patient responsibility vs. insurance payments.
Payment Processing: The Payments table logs the final closure of the financial lifecycle for that visit.

3. Insurance Data Model Entities

3.1 OLTP Tables List (Operational Database)

Data model image

Designed for high-concurrency inserts and updates (approx. 15–20 tables).

● Members/Patients: Personal info, eligibility status.

● Insurance_Providers: Company credentials, contact details.

● Plans: Master product definitions, deductible logic.

● Policies: Contract records linking members to plans.

● Providers: Practitioner NPI details, network status.

● Claims: Header information for service requests.

● Claim_Line_Items: Procedure-level details (CPT/HCPCS codes).

● Adjudication_Logs: Status history (Submitted, Review, Paid, Denied).

● Billing_Invoices: Aggregated financial charges.

● Payments: Transaction logs (Patient vs. Insurance).

● Authorizations: Records for pre-approved procedures.

● Networks: Mapping of providers to specific plan networks.

3.2 OLAP Tables List (Data Warehouse)

Designed for fast complex analytical queries (Star Schema).

Fact Tables (Metrics)

● Fact_Claims: Claims processed, denial rates, total billed vs. allowed amounts.

● Fact_Payments: Revenue collected, reimbursement turnaround times.

● Fact_Enrollment: Membership growth, churn rates, policy renewal frequency.

Dimension Tables (Filters)

● Dim_Date: Temporal analysis (Fiscal year, quarter, month).

● Dim_Member: Demographic segmentation (Age, region, plan type).

● Dim_Provider: Network status, specialty, geographical location.

● Dim_Plan: Product tier, coverage types.

4. KPIs and Analytics

Fact Table	Key Performance Indicators (KPIs)
Fact_Claims	Denial Rate, Average Adjudication Time, Total Billed Amount
Fact_Payments	Reimbursement Rate, Outstanding Balances, Provider Payouts
Fact_Enrollment	Member Retention Rate, Policy Renewal Frequency, Plan Utilization Rate

5. Data Traceability & Integrity

Every Member_ID and Policy_ID acts as a unique thread connecting operational data across systems. This allows for full auditability, enabling the hospital/insurance entity to generate a complete history for any claim—from the initial service order to the final financial settlement.

6. KPIS and FACTS

Fact_Appointments

● KPIs: Count of Appointments, No-show Rate, Average Wait Time, Total Scheduled Duration.

● Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Department, Dim_Location.

Fact_Lab_Results

● KPIs: Count of Tests Performed, Average Turnaround Time, Abnormal Result Rate.

● Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Test_Type.

Fact_Prescriptions

● KPIs: Count of Prescriptions, Total Medication Cost, Average Dosage Frequency.

● Dimensions: Dim_Date, Dim_Patient, Dim_Provider, Dim_Medication.

Fact_Billing

● KPIs: Total Billed Amount, Total Collected Amount, Outstanding Balance, Insurance Reimbursement Rate.

● Dimensions: Dim_Date, Dim_Patient, Dim_Department, Dim_Insurance.

Fact_Bed_Occupancy

● KPIs: Bed Occupancy Rate, Average Length of Stay (ALOS), Patient Turnover Rate.

● Dimensions: Dim_Date, Dim_Patient, Dim_Department, Dim_Location.

6. KPI Definitions

● Average Wait Time: Measures the average time a patient spends waiting.

● No-show Rate: Tracks the percentage of missed appointments.

● Average Turnaround Time: Measures the time from ordering a lab test to getting results.

● Abnormal Result Rate: Tracks the percentage of lab results outside of normal ranges.

● Total Medication Cost: Calculates the total cost of all prescribed medications.

● Average Dosage Frequency: Tracks how often a medication is administered to patients.

● Total Billed Amount: Measures total revenue from all hospital services.

● Insurance Reimbursement Rate: Calculates the percentage of bills covered by insurance.

● Bed Occupancy Rate: Measures the percentage of available beds currently in use.

● Average Length of Stay (ALOS): Tracks the average number of days a patient stays in the hospital.

---

\\ 3 . Bank project data model

1. About the Banking Project

The core objective was to build a system capable of managing core banking operations—from customer account onboarding to complex transaction processing and loan lifecycle management. The primary challenges included ensuring high-frequency transaction consistency, maintaining strict security for financial records, and enforcing regulatory compliance (e.g., AML, KYC).

Design Approach

● Mapping the Lifecycle: We identified key touchpoints: Account Opening, Deposits/Withdrawals, Fund Transfers, Loan Origination, and Interest Accrual.

● Normalization (OLTP): Data is organized into 3rd Normal Form (3NF) to maintain a single source of truth for customers, accounts, and ledger entries, ensuring atomic transaction integrity.

● Handling Complex Relationships: We utilized "bridge" tables (e.g., Account_Signatories, Transaction_Categories) to handle many-to-many relationships, storing detailed audit logs for every movement of funds.

● Business Rules & Integrity: Constraints such as minimum_balance_check and daily_limit_validation ensure financial safety and prevent unauthorized overdrafts.

2. The Customer-Account Lifecycle

Onboarding (KYC): Capture customer demographic data and verify identification records.
Account Creation: A Savings or Checking account is initialized with unique routing and account numbers.
Transaction Execution: Deposits, withdrawals, or transfers are processed, triggering ledger updates in real-time.
Interest & Fee Calculation: End-of-period processes calculate interest and service charges based on account balance rules.
Statement Generation: Periodic summary of account activity is generated for the customer.
Closure/Retention: Account status is updated based on dormancy or customer request, finalizing the lifecycle.

3. Bank Data Model Entities

3.1 OLTP Tables List (Operational Database)

● Customers: KYC info, contact details, identity verification status.

● Accounts: Account type (Savings/Checking), balance, status, open date.

● Transactions: Ledger of all deposits, withdrawals, and transfers.

● Loans: Loan terms, interest rates, repayment schedules.

● Repayments: Payment logs against outstanding loan balances.

● Branch: Geographic and operational banking locations.

● Employees: Staff credentials, role-based access permissions.

● Audit_Logs: Immutable history of all record changes for security compliance.

3.2 OLAP Tables List (Data Warehouse)

Fact Tables (Metrics)

● Fact_Transactions: Count of transfers, total transaction volume, fee revenue.

● Fact_Deposits: Daily balance snapshots, deposit inflow/outflow.

● Fact_Loans: Outstanding principal, interest earned, delinquency rates.

Dimension Tables (Filters)

● Dim_Date: Time-based analysis (Date, Month, Quarter, Fiscal Year).

● Dim_Customer: Demographic segmentation (Age group, income tier, risk profile).

● Dim_Account_Type: Product classification (Retail, Corporate, Student).

● Dim_Branch: Branch performance and regional clusters.

4. KPIs and Analytics

Fact Table	Key Performance Indicators (KPIs)
Fact_Transactions	Transaction Volume, Average Transaction Value, Processing Error Rate
Fact_Deposits	Net Deposit Growth, Average Daily Balance, Liquidity Ratio
Fact_Loans	Delinquency Rate, Loan Approval Rate, Interest Income Ratio

5. KPIS and FACTS

Fact_Transactions

● KPIs: Count of Transactions, Total Transaction Volume, Transaction Error Rate, Average Transaction Value.

● Dimensions: Dim_Date, Dim_Customer, Dim_Account_Type, Dim_Branch.

Fact_Savings_Accounts

● KPIs: Average Daily Balance, Interest Earned, Total Deposits, Total Withdrawals.

● Dimensions: Dim_Date, Dim_Customer, Dim_Account_Type, Dim_Branch.

Fact_Loan_Performance

● KPIs: Total Principal Outstanding, Interest Income, Default Rate, Loan Approval Volume.

● Dimensions: Dim_Date, Dim_Customer, Dim_Loan_Type, Dim_Branch.

Fact_Customer_Activity

● KPIs: New Account Openings, Account Closure Rate, Total Customer Lifetime Value (CLV).

● Dimensions: Dim_Date, Dim_Customer, Dim_Customer_Segment, Dim_Branch.

6. KPI Definitions

● Transaction Volume: Total number of successful transactions within a period.

● Transaction Error Rate: Percentage of failed or disputed transactions.

● Average Daily Balance: The mean balance across a specified period.

● Delinquency Rate: Percentage of loans with payments overdue by more than 30/60/90 days.

● Net Deposit Growth: Difference between total deposits and withdrawals over time.

● Loan Approval Rate: Percentage of loan applications approved against total applications.

● Interest Income: Revenue generated from interest-bearing loan portfolios.

● Customer Lifetime Value (CLV): Total predicted revenue attributed to a single customer relationship.

● Account Closure Rate: Percentage of accounts closed relative to total active accounts.

● Liquidity Ratio: Ratio of liquid assets available to cover immediate deposit withdrawals.

---

\\ 4 . Food delivery project data model

Explain about your project

1. About the Food Delivery Project

The core objective was to build a system capable of managing a multi-sided marketplace—connecting customers, restaurants, and delivery drivers in real-time. The primary challenges included managing high-concurrency order placement, real-time location tracking for drivers, and ensuring fair commission structures and timely payouts.

Design Approach

● Mapping the Lifecycle: We identified key touchpoints: User Registration, Restaurant Menu Management, Order Placement, Dispatch/Routing, Order Fulfillment, and Driver Payouts.

● Normalization (OLTP): Data is organized into 3rd Normal Form (3NF) to ensure consistency between customer profiles, restaurant menus, and order status tracking, preventing inventory and delivery errors.

● Handling Complex Relationships: We utilized "bridge" tables (e.g., Order_Items, Driver_Earnings) to manage many-to-many relationships, storing detailed attributes like item modifiers, delivery distance, and variable service fees.

● Business Rules & Integrity: Constraints such as order_status_progression and delivery_window_validation ensure that orders move logically through the lifecycle without status conflicts.

2. The Order Fulfillment Lifecycle

User Onboarding: Customer registration with saved addresses and payment methods.
Menu Management: Restaurants publish menus with prices, descriptions, and availability status.
Order Placement: Customers browse and select items, initiating an Order record with a specific status ("Pending").
Dispatch: The system assigns a nearby driver to the order based on geographic proximity and availability.
Fulfillment: Real-time tracking of the order from restaurant pickup to customer doorstep.
Financial Settlement: The Billing stage calculates the order total, delivery fee, restaurant commission, and driver earnings.

3. Food Data Model Entities

Data model image

3.1 OLTP Tables List (Operational Database)

Based on your requirements, here is the list of database tables:

● Customers

● Customer_Addresses

● Restaurants

● Restaurant_Hours

● Menu_Categories

● Menu_Items

● Orders

● Order_Items

● Item_Modifiers

● Drivers

● Deliveries

● Payments

● Commission_Splits

● Driver_Payouts

●

3.2 OLAP Tables List (Data Warehouse)

Fact Tables (Metrics)

● Fact_Orders: Total volume, average order value, cancellation rates.

● Fact_Delivery_Performance: Delivery times, driver performance metrics, fulfillment efficiency.

● Fact_Restaurant_Revenue: Commission earnings, daily sales volume, top-selling items.

Dimension Tables (Filters)

● Dim_Date: Temporal analysis (Date, Hour, Day of Week, Month).

● Dim_Customer: Demographic segmentation (Frequency of use, location).

● Dim_Restaurant: Cuisine type, geographical region.

● Dim_Driver: Driver performance tier, vehicle type.

4. KPIs and Analytics

Fact Table	Key Performance Indicators (KPIs)
Fact_Orders	Order Volume, Average Order Value, Order Cancellation Rate
Fact_Delivery_Performance	Average Delivery Time, On-time Arrival Rate, Driver Utilization Rate
Fact_Restaurant_Revenue	Total Gross Merchandise Value (GMV), Commission Revenue

5. KPIS and FACTS

Fact_Orders

● KPIs: Count of Orders, Average Order Value, Order Cancellation Rate, Gross Merchandise Value (GMV).

● Dimensions: Dim_Date, Dim_Customer, Dim_Restaurant, Dim_City.

Fact_Delivery_Performance

● KPIs: Average Delivery Time, On-time Arrival Rate, Driver Utilization Rate, Total Distance Traveled.

● Dimensions: Dim_Date, Dim_Driver, Dim_Restaurant, Dim_City.

Fact_Restaurant_Revenue

● KPIs: Total Commission Earned, Total Sales Volume, Top Performing Categories, Average Item Price.

● Dimensions: Dim_Date, Dim_Restaurant, Dim_Category.

Fact_Customer_Activity

● KPIs: Customer Retention Rate, New User Signups, Average Orders per Customer, Lifetime Value (CLV).

● Dimensions: Dim_Date, Dim_Customer, Dim_City.

6. KPI Definitions

● Order Volume: Total number of successful orders placed.

● Average Order Value (AOV): Total sales revenue divided by the number of orders.

● Order Cancellation Rate: Percentage of orders cancelled by users or restaurants.

● Average Delivery Time: Mean duration from order confirmation to delivery completion.

● On-time Arrival Rate: Percentage of orders delivered within the estimated window.

● Driver Utilization Rate: Percentage of time drivers spend on active delivery tasks.

● Gross Merchandise Value (GMV): The total value of food sold through the platform.

● Commission Revenue: Total income generated through restaurant commissions and service fees.

● Customer Retention Rate: Percentage of customers who repeat orders within a period.

● Customer Lifetime Value (CLV): Total predicted revenue attributed to a customer relationship.

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\\ 5. Casella waste management project Data model

Waste Management Project Data Model Specification

1. About the Waste Management Project

The core objective was to build a system capable of managing waste collection logistics—connecting residential/commercial generators, fleet operations, and disposal facilities. The primary challenges included optimizing collection routes, tracking container fill levels via IoT sensors, and ensuring regulatory compliance for hazardous and municipal waste disposal.

Design Approach

● Mapping the Lifecycle: We identified key touchpoints: Service Contract Initiation, Route Planning, Collection/Pickup, Disposal/Recycling Processing, and Invoicing.

● Normalization (OLTP): Data is organized into 3rd Normal Form (3NF) to ensure consistency between customer accounts, service locations, and collection history, preventing billing errors and missed pickups.

● Handling Complex Relationships: We utilized "bridge" tables (e.g., Service_Contracts, Fleet_Assignments) to manage many-to-many relationships, storing detailed attributes like waste types, container capacities, and pickup frequencies.

● Business Rules & Integrity: Constraints such as collection_schedule_validation and disposal_compliance_check ensure that waste is processed according to environmental regulations and contract terms.

2. The Waste Collection Lifecycle

Contract Management: Customers (residential/commercial) sign up for service, defining waste types and pickup frequency.
Route Planning: The system uses geographic data to assign specific bins/locations to fleet routes for optimal fuel and time usage.
Collection: Drivers follow assigned routes, logging pickup confirmations and recording container status (e.g., damaged/full).
Disposal/Processing: Waste is transported to designated transfer stations, landfills, or recycling centers where weight and type are verified.
Fleet Maintenance: IoT and maintenance logs track truck health, mileage, and driver behavior.
Financial Settlement: The Billing stage aggregates pickups, tonnage fees, and service charges into customer invoices.

3. Data Model Entities

3.1 OLTP Tables List (Operational Database)

● Customers: Service profiles, billing info, location coordinates.

● Service_Contracts: Frequency, waste type (Recycling/Compost/Trash), container info.

● Routes: Route segments, scheduled pickup days, assigned zones.

● Fleet_Vehicles: Asset info, mileage, maintenance status, fuel logs.

● Drivers: Profiles, shift schedules, certification levels.

● Pickups: Real-time collection status, timestamps, geolocation.

● Disposal_Logs: Destination site, tonnage/weight records, waste classification.

● Billing_Invoices: Aggregated charges based on tonnage and contract terms.

3.2 OLAP Tables List (Data Warehouse)

Fact Tables (Metrics)

● Fact_Pickups: Volume collected, service completion rates, missed pickup logs.

● Fact_Fleet_Operations: Fuel consumption, route efficiency, truck utilization.

● Fact_Disposal_Revenue: Revenue by material type, facility costs, service margins.

Dimension Tables (Filters)

● Dim_Date: Temporal analysis (Date, Shift, Route Day).

● Dim_Customer: Demographic/Business type, service tier.

● Dim_Route: Geographic zone, route ID.

● Dim_Vehicle: Vehicle type, age, maintenance history.

4. KPIs and Analytics

Fact Table	Key Performance Indicators (KPIs)
Fact_Pickups	Pickup Completion Rate, Missed Pickup Rate, Total Weight Collected
Fact_Fleet_Operations	Cost per Route, Fuel Efficiency (MPG), Vehicle Downtime
Fact_Disposal_Revenue	Revenue per Ton, Recycling Diversion Rate, Outstanding Receivables

5. KPIS and FACTS

Fact_Pickups

● KPIs: Count of Pickups, Missed Pickup Rate, Total Tonnage Collected, Service Time.

● Dimensions: Dim_Date, Dim_Customer, Dim_Route, Dim_Location.

Fact_Fleet_Operations

● KPIs: Fuel Efficiency, Route Deviation Rate, Total Distance Traveled, Vehicle Utilization.

● Dimensions: Dim_Date, Dim_Vehicle, Dim_Driver, Dim_Route.

Fact_Disposal_Revenue

● KPIs: Total Revenue, Cost per Ton, Recycling Diversion Rate, Service Fee Margin.

● Dimensions: Dim_Date, Dim_Customer, Dim_Material_Type.

Fact_Customer_Activity

● KPIs: Contract Renewal Rate, Service Volume Growth, Customer Churn, Outstanding Balance.

● Dimensions: Dim_Date, Dim_Customer, Dim_Service_Type.

6. KPI Definitions

● Pickup Completion Rate: Percentage of scheduled stops successfully serviced.

● Missed Pickup Rate: Percentage of stops skipped or reported as missed.

● Total Tonnage Collected: Aggregate weight of waste collected across all services.

● Fuel Efficiency: Average miles per gallon (MPG) for the collection fleet.

● Route Deviation Rate: Frequency at which drivers deviate from optimized paths.

● Recycling Diversion Rate: Percentage of waste diverted from landfills to recycling/compost.

● Cost per Route: Operational cost (fuel, labor, maintenance) per scheduled route.

● Contract Renewal Rate: Percentage of customers renewing their service contracts.

● Service Volume Growth: Percentage increase in tonnage or new customers month-over-month.

● Revenue per Ton: Total revenue divided by the total weight of material processe

Challenges in project

Problems /challenges you faced in Data modeling

Hospital project

1. Lack of Relational Mapping

A data model is not just a list of nouns; it is a map of how data interacts. Your list does not define relationships (e.g., One-to-Many or Many-to-Many).

● Missing Links: You haven't defined how a Patient relates to an Appointment, or how a Doctor is assigned to a Department. Without these, you cannot query "Which doctor saw this patient?"

● Ambiguity: Does an Invoice belong to a Patient, an Appointment, or a Prescription?

2. Failure to Normalize (Redundancy)

Without a relational structure, you will face data redundancy issues.

● Hospital Name: Storing Hospital_Name as a standalone entity is usually unnecessary unless you are managing a chain of hospitals. If it's a single hospital, this is redundant data that should be a global configuration setting.

● Duplicate Data: If you don't use foreign keys, you might end up typing a Patient_Name or Doctor_ID multiple times across Appointment, Prescription, and Invoice tables, leading to data inconsistencies.

3. Missing Critical Entities

Your model is missing "bridge" entities required to handle complex hospital workflows:

● Medical Record / Visit: A patient may visit multiple times. You need a way to track specific episodes of care rather than just linking a patient directly to a prescription.

● Specialization: Doctors belong to departments, but they also have specific qualifications.

● Treatment / Procedure: Prescription covers medication, but Invoice often needs to account for medical procedures, tests, or bed charges.

4. Naming and Integrity Issues

● Typos: Involce (should be Invoice).

● Granularity: Staff is too broad. You need to distinguish between administrative staff, nurses, and doctors, as they have different roles, access levels, and salary structures.

● Lack of Primary Keys: Each entity needs a unique identifier (e.g., Patient_ID, Doctor_ID) to ensure data integrity.

Recommended Entity-Relationship Structure

To improve this model, you should transition to a relational design.

Entity	Primary Key	Key Attributes
Department	Dept_ID	Name, Location, Head_Doctor
Doctor	Doctor_ID	Name, Specialization, Dept_ID (FK)
Patient	Patient_ID	Name, DOB, Contact, Insurance_Info
Appointment	Appt_ID	Patient_ID (FK), Doctor_ID (FK), Date, Time
Prescription	Presc_ID	Appt_ID (FK), Medication_Details, Dosage

Pharma challenges

1. Challenge: Inconsistent definition of "Patient"

(e.g., whether a patient is identified by phone number, name, or insurance ID). We identified this in the initial data modeling workshop when different business units gave conflicting counts for total customer base.

Resolution: We established a "Golden Record" standard where Patient_ID is strictly mapped to a unique government-issued health identification number or insurance primary key, with all other fields (name, phone) treated as descriptive attributes.

3. Challenge: Regulatory compliance gaps for prescription history

a. (e.g., no clear requirement on how long to store the Doctor authorization). We identified this in the compliance review stage when auditing the Prescriptions table against local pharmacy laws. Resolution: We added a Retention_Expiry_Date attribute and a soft-delete/archive flag to the Prescriptions table, ensuring records are retained per mandatory audit cycles without cluttering the active transactional view.

4. Challenge: Supplier lead-time volatility

a. (the Lead_Time_Days field in Suppliers is a static estimate, but reality varies). We identified this in the Inventory forecasting session when comparing manual ledger records to the Inventory table performance.

b. Resolution: We modified the model to include a Supplier_Performance_Log table that captures historical delivery times for every replenishment, replacing static estimates with a rolling average calculation for more accurate inventory planning.

5. Challenge: Product classification ambiguity

a. (many drugs have brand names and chemical names that appear as different entities). We identified this in the catalog validation phase when populating the Products table

b. . Resolution: We implemented a mapping layer in the Products table where Chemical_Formula acts as the master-key (or generic identifier), while Name handles the brand aliases, ensuring we can aggregate sales by drug efficacy regardless of the brand name used.

6. Challenge: Partial payment and refund handling

a. (the Payments table was designed for a single lump sum per order). We identified this in the finance workflow walkthrough when discussing returns and insurance co-pay splits.

b. Resolution: We refactored the Payments table from a 1:1 relationship with Orders to a 1:N structure, allowing multiple entries per Order_ID to track initial payments, partial insurance settlements, and subsequent customer refunds independently.

Food delivery project

1. The "Infinite Customization" Modifier Problem

Challenge: Supporting unlimited combinations of menu modifiers (e.g., a pizza with "extra cheese" on the left half, "no onions," and "gluten-free crust") without creating thousands of redundant menu rows.

Difficulty: In a standard relational model, creating columns for every possible topping leads to "Sparse Data" (mostly empty columns) and is impossible to maintain as restaurants change their menus weekly. Hard-coding these options makes the database rigid and slow.

Solution: The Recursive Associative Bridge. We implemented a hierarchical modifier_group table. Instead of linking a pizza to "Pepperoni," we link the pizza to a "Toppings Group." That group contains many modifier_items. This allows for "nesting"—where choosing a "Burger" triggers a "Protein Group," and choosing "Steak" within that group triggers a "Doneness Group." This metadata-driven approach allows restaurants to update options in real-time without a single schema change.

2. Bi-Temporal Menu Versioning (The "Price Lock" Paradox)

Challenge: Ensuring that historical financial reports remain accurate even when a restaurant changes its prices or deletes a menu item.

Difficulty: If you simply update the price column in the menu_item table, every historical order linked to that ID will suddenly show the new price in your reports, breaking your accounting. However, creating a brand-new ID for every price change causes "Data Fragmentation," making it impossible to track a dish's popularity over a year.

Solution: Bi-Temporal Effective Dating. We added valid_from_timestamp and valid_to_timestamp columns to the pricing and menu tables. When a price change occurs, the system doesn't "Update" the row; it "Closes" the current row by setting the valid_to date and inserts a new row with the same business_key. Queries then use a WHERE order_timestamp BETWEEN valid_from AND valid_to clause to fetch the exact price that existed at the second the order was placed.

3. High-Concurrency Geofence Matching

Challenge: Assigning the "best" delivery partner to an order in real-time based on moving GPS coordinates and complex delivery zones.

Difficulty: Standard SQL "Distance Calculations" (Pythagorean theorem) are mathematically expensive. Running these calculations for 500 orders against 2,000 moving drivers every few seconds will crash a standard database CPU.

Solution: Spatial Partitioning with GiST Indexing. We moved the logistics data into a dedicated schema using PostGIS. Instead of calculating raw distance, we implemented Geofencing. Each driver is mapped to a current_zone_id (a polygon). When an order is placed, the system first filters for drivers within that specific polygon using a Generalized Search Tree (GiST) index. This reduces the search pool from thousands to dozens instantly, allowing the final distance math to run only on a tiny subset of candidates.

4. Multi-Party Split Settlement (The "Double-Entry" Ledger)

Challenge: Automatically splitting a single customer payment into three distinct payouts: the Restaurant’s earnings, the Driver’s fee/tip, and the Platform’s commission.

Difficulty: A single transaction can have multiple "Sub-Statuses." For example, the customer is charged immediately, but the driver is only paid after delivery, and the restaurant might be paid weekly. Tracking this in a single "Orders" table creates a "Financial Reconciliation Nightmare" where cents go missing during refunds or cancellations.

Solution: The Atomic Settlement Ledger. We decoupled "Orders" from "Money" by building a Double-Entry Bookkeeping Ledger. Every order triggers a set of balanced entries: a "Debit" to the Customer and "Credits" to three separate internal "Holding Accounts" (Restaurant_Wallet, Driver_Wallet, Platform_Revenue). This ensures the sum of all payouts always equals the total collected. If a refund occurs, the system moves

1. Real-Time Security & Compliance

● Requirement: Automated tracking of all administrative overrides and suspicious activities.

● Problem: Manual logging is prone to gaps, leaving the Audit and Frauddetection tables incomplete.

● Solution: Implemented database-level triggers to capture immutable logs for every state change automatically.

2. Customer 360-Degree Integration

● Requirement: Consolidating a single Customer identity across retail, loans, and wealth management.

● Problem: Data silos made it difficult to link an Account to an Investment or Insurance policy.

● Solution: Established a master CustomerID as a global Foreign Key across all 15 tables for unified reporting.

3. Financial Precision & Multi-Currency

● Requirement: Handling global transactions via Forex while maintaining sub-cent accuracy.

● Problem: Standard float data types caused rounding errors in Transaction and Creditscore calculations.

● Solution: Enforced strict DECIMAL(15,2) for balances and DECIMAL(10,6) for exchange rates to ensure 100% accuracy.

4. Amortization & Payment Integrity

● Requirement: Precise tracking of debt repayment schedules in the Loanpayment table.

● Problem: Difficulty accurately splitting payments between principal, interest, and penalties over time.

● Solution: Architected a relational link between Loan and Loanpayment with calculated fields to automate balance updates.

Issue: Data inconsistency in customer profiles (duplicates).

Stage Identified: During the initial data profiling and join operations between Customer_Master and Policy_Master. Resolution: We implemented an MDM (Master Data Management) layer with fuzzy matching logic to link multiple records representing the same individual into a single "Golden Customer ID."

Issue: Conflicts between Policy status and Payment history.

Stage Identified: During the validation phase of the "Active Policies" financial reporting dashboard. Resolution: We created a "Derived Status" field in the transformation layer that cross-references both tables to flag policies as "Suspended" if premiums are overdue, regardless of the system-level status.

Issue: Late-arriving fact records (claims ingested after the reporting window).

Stage Identified: During daily reconciliation when current-week metrics didn't match the transactional logs. Resolution: We updated the ETL pipeline to include a "Look-back Window," where the system automatically re-processes and updates the last 7 days of data with every daily load.

Issue: Referential integrity failure (orphaned payments due to out-of-order ingestion).

Stage Identified: During the automated staging-to-warehouse load process. Resolution: We built a "Holding Table" for orphaned records, which temporarily stores transactions until the parent policy record arrives, at which point the ETL triggers a retry to link them.

Issue: Historical schema drift (change in data structure for Underwriting_Rules).

Stage Identified: When the automated daily ETL pipeline began failing due to unexpected JSON format errors. Resolution: We moved to an ELT (Extract, Load, Transform) approach, where raw data is dumped into a flexible "Staging/Landing Zone" first, allowing us to parse the complex JSON logic after the raw data is safely ingested.

1. Lack of Relational Mapping

A data model is not just a list of nouns; it is a map of how data interacts. Your list does not define relationships (e.g., One-to-Many or Many-to-Many).

● Missing Links: You haven't defined how a Patient relates to an Appointment, or how a Doctor is assigned to a Department. Without these, you cannot query "Which doctor saw this patient?"

● Ambiguity: Does an Invoice belong to a Patient, an Appointment, or a Prescription?

2. Failure to Normalize (Redundancy)

Without a relational structure, you will face data redundancy issues.

3. Missing Critical Entities

Your model is missing "bridge" entities required to handle complex hospital workflows:

● Medical Record / Visit: A patient may visit multiple times. You need a way to track specific episodes of care rather than just linking a patient directly to a prescription.

● Specialization: Doctors belong to departments, but they also have specific qualifications.

● Treatment / Procedure: Prescription covers medication, but Invoice often needs to account for medical procedures, tests, or bed charges.

4. Naming and Integrity Issues

● Typos: Involce (should be Invoice).

● Granularity: Staff is too broad. You need to distinguish between administrative staff, nurses, and doctors, as they have different roles, access levels, and salary structures.

● Lack of Primary Keys: Each entity needs a unique identifier (e.g., Patient_ID, Doctor_ID) to ensure data integrity.

Recommended Entity-Relationship Structure

To improve this model, you should transition to a relational design.

Entity	Primary Key	Key Attributes
Department	Dept_ID	Name, Location, Head_Doctor
Doctor	Doctor_ID	Name, Specialization, Dept_ID (FK)
Patient	Patient_ID	Name, DOB, Contact, Insurance_Info
Appointment	Appt_ID	Patient_ID (FK), Doctor_ID (FK), Date, Time
Prescription	Presc_ID	Appt_ID (FK), Medication_Details, Dosage

Banking project

1. Real-Time Security & Compliance

● Requirement: Automated tracking of all administrative overrides and suspicious activities.

● Problem: Manual logging is prone to gaps, leaving the Audit and Frauddetection tables incomplete.

● Solution: Implemented database-level triggers to capture immutable logs for every state change automatically.

2. Customer 360-Degree Integration

● Requirement: Consolidating a single Customer identity across retail, loans, and wealth management.

● Problem: Data silos made it difficult to link an Account to an Investment or Insurance policy.

● Solution: Established a master CustomerID as a global Foreign Key across all 15 tables for unified reporting.

3. Financial Precision & Multi-Currency

● Requirement: Handling global transactions via Forex while maintaining sub-cent accuracy.

● Problem: Standard float data types caused rounding errors in Transaction and Creditscore calculations.

● Solution: Enforced strict DECIMAL(15,2) for balances and DECIMAL(10,6) for exchange rates to ensure 100% accuracy.

4. Amortization & Payment Integrity

● Requirement: Precise tracking of debt repayment schedules in the Loanpayment table.

● Problem: Difficulty accurately splitting payments between principal, interest, and penalties over time.

● Solution: Architected a relational link between Loan and Loanpayment with calculated fields to automate balance updates.

more qsns

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Data governance my notes .pdf

Ralph,Kimball : says build the data marts first , then build the data warehouse by joining them into single data warehouse , This is called bottom up approach

Inmon : says build one single data warehouse for the entire enterprise , then divide the warehouse into Datamarts , This is called Top down approach .


https://docs.google.com/spreadsheets/d/1Dou4e0g5r61INb24nbdDKQFXFjcaTEuk0R-A1h9AHCk/edit#gid=1145721315
sno	Question	Answer	Links
1	Types of Fact tables	1. Transactional Fact Tables Transactional fact tables are designed to capture individual business events or transactions. 2. Periodic Snapshot Fact Tables Periodic snapshot fact tables provide a summarized view of metrics over regular time intervals. 3. Accumulating Snapshot Fact Tables Accumulating snapshot fact tables are designed to track the stages of a business process or workflow. 4. Factless facts	https://easy-data-modeling.blogspot.com/2024/03/types-of-fact-tables.html
45	types of fact tables 51	1. Additive ( can be added like total qty , sales ) 2. Semi additive ( cannot be added but updated like account balance ) 3. Non Additive ( numerical but cannot be added like today price of scotk )
2	Granularity and Granule	Granularity in data refers to the level of detail > high level of granularity would have a large number of individual pieces (columns) of information, such as individual records or measurements. >Data that has a low level of granularity would have a small number of individual pieces of information, such as summary data or aggregated data. Data granularity can affect how it is used and analyzed, and can impact the accuracy and usefulness of the results.
3	what are 3 levels of Data modeling	1. Conceptual model list of entities and relationships , no attributes diagram only 2. logical 3. physical model
4	Logical and physical model - Differences ?
5	Star schema & Snowflake schema - Differences ?
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9			https://www.linkedin.com/pulse/data-warehouse-project-life-cycle-design-karumanchi-sujatha/?trk=article-ssr-frontend-pulse_more-articles_related-content-card

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15			https://bookshelf.erwin.com/bookshelf/public_html/2019R2/Content/User%20Guides/erwin%20Help/foward_engineering_schema_generation.html
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22	Fact and dimension table - differences	. Fact tables contain numerical data, while dimension tables provide context and background information.	https://www.simplilearn.com/fact-table-vs-dimension-table-article#what_is_a_fact_table
23	What is Datawarehouse	A data warehouse is a centralized system that stores data from various sources for reporting and analysis Data warehouses can: Consolidate data: Store data from multiple sources into a single source of truth Store historical data: Store and analyze long-term historical data Analyze data about a particular subject or functional area:
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PAGES

Tuesday, June 2, 2026

1. Types of Facts

2. Types of Fact tables

3. Types of Dimension tables

4. > WHY 3 models needed

5. > Why use surrogate key

6. > star and snowflake when to use

7. > how scd works on downsteams

8. How to handle late arriving dimensions ?

How snowflake is different from other databases or data warehouses 1. Separate "Storage" and "Brain" Power

Tell me your knowledge on the 3 layers of ETL

## Data Modeling Best Practices – Headings

Data model testing how

Data modeler roles

design a common date table

define the data level granularity

Layer of Data lake

Views and materialized views

Data governance

ELT (Extract, Load, Transform) advantages

What is a Data Lake?

A data lakehouse

features of snowflake db

A Compact List of Snowflake Features

Data vault qsns

Two types of model versions exist:

Create a Delta Version

Create a Named Version

Compare Model Versions

1. > unique key and primary key diff

2. query to find number of subjects of each student

3. Peoplefy - How to design facts and dimensions

4. Peoplefy - sql query to find sum

5. query to find length of each column and subtract total commas from it

5.

Query to find third highest ?

6. Data model constraints ?

Teksystems - in data ware house how do you update the history table with scd2 if the last updated data was corrupt and to update the data with correct values

Teksystems - query : find and delete duplicate rows Two methods to find and delete duplicate rows

what is inner join and outer join count of below

54 . How to find cumulative check - if employee got hike or not

55. How to find if a single user purchased same product on different dates

SQL - show highest salary in each department

SQL - count(*) , count(column) , count(1)

-- BOTH COUNT(*) AND COUNT(1) ARE SAME BUT COUNT(1) IS FASTER

Query TASKS lists

SQL FUNCTIONS

SQL - CONSTRAINTS

sql query : TO get highest ordered products in each state

VARCHAR AND NVARCHAR difference

- sql query : find and delete duplicate rows

SQL : query to show Total salary of each department

How do you do Data model optimization ?

Query optimization best practices

best practices for SQL query optimization summarized in one-liners:

best practices for data modeling summarized in one-liners:

How snowflake is different from other databases or data warehouses

1. Separate "Storage" and "Brain" Power