154 ) SQL Concept questions

 

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sql :  union union all difference

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The Short Answer: SQL UNION vs. UNION ALL. The key difference is that UNION removes duplicate records, whereas UNION ALL includes all duplicates. 

This distinction not only changes the number of rows in the query result, but it also impacts performance

so always use UNION ALL instead of UNION to speed up the execution time

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DIFFERENCE between joins and union

joins combine data side-by-side, whereas unions combine data one on top of the other

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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

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153 ) List of Chellenges of Data modeling ?

 Problems / Chellenges of Data modeling ?

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 Challenge 1 : SCD 1 to SCD 2 issue

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Step-by-Step Resolution for an Existing Production Model

Step 1: Identifying the Anomaly & Root Cause Analysis

• The Production Symptom: Finance and sales teams noticed that historical monthly revenue reports changed after a routine product catalog update because past orders automatically reflected new prices and names.

• Root Cause Analysis: The data model used a standard design where the Fact_Orders table pointed directly to a single Dim_Product table via a foreign key.

• The Flaw: We found that Dim_Product acted as a mutable lookup table (SCD Type 0/1). When a product was renamed or repriced, the dimension row was overwritten, destroying historical point-in-time accuracy.

Step 2: Modifying the Existing Schema in Erwin & Database (Alter & Backfill)

Instead of dropping or recreating tables, we altered the existing structure and handled historical backfill:

1. In Erwin Data Modeler & Database:

   • Alter the existing dim_product table to add temporal columns: effective_start_date, effective_end_date, and is_current.

   • Add the auto-incrementing product_sk as the new primary key while retaining product_id as the natural business key.

2. Handling Existing Stored Data:

   • Run a one-time migration script using historical audit/CDC logs to seed past product versions into dim_product from day one.

   • Update existing rows in fact_order_line to point to the correct historical product_sk based on the order date.

Step 3: Implementing the SCD Type 2 ETL Pipeline

For ongoing data changes, the ingestion pipeline executes an Upsert/Merge pattern rather than a simple overwrite.

1. Expire the old record when an attribute changes:

UPDATE dim_product SET effective_end_date = CURRENT_DATE - INTERVAL '1 day', is_current = FALSE FROM stg_product_catalog stg WHERE dim_product.product_id = stg.product_id AND dim_product.is_current = TRUE AND (dim_product.unit_price <> stg.unit_price OR dim_product.product_name <> stg.product_name);

2. Insert the new active version with a new surrogate key:

INSERT INTO dim_product ( product_id, product_name, unit_price, effective_start_date, effective_end_date, is_current ) SELECT stg.product_id, stg.product_name, stg.unit_price, CURRENT_DATE AS effective_start_date, NULL AS effective_end_date, TRUE AS is_current FROM stg_product_catalog stg WHERE NOT EXISTS ( SELECT 1 FROM dim_product dp WHERE dp.product_id = stg.product_id AND dp.is_current = TRUE AND dp.unit_price = stg.unit_price AND dp.product_name = stg.product_name );

Step 4: Power BI Report Design & DAX Validation

• Model Relationships: Connect fact_order_line[product_sk] to dim_product[product_sk] using a standard Many-to-One (*:1) relationship.

• DAX Measure Result: Because the fact table points to the historical product_sk corresponding to the exact date the order was placed, aggregation formulas calculate values cleanly without retroactive distortion.

Total Historical Revenue = SUMX( fact_order_line, fact_order_line[quantity] * RELATED(dim_product[unit_price]) )

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 Challenge 2 : Multi-Currency Global Sales Reporting Discrepancies

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Step 1: Identifying the Anomaly & Root Cause Analysis

• The Production Symptom: Finance and leadership noticed severe financial discrepancies in global revenue reports, where cross-border orders showed massive variance compared to local bank ledger settlements.

• Root Cause Analysis: The data model stored international transaction amounts in local currencies but converted them using only the current day's exchange rate rather than the historical rate active on the exact day of the sale.

• The Flaw: We found that the star schema lacked a proper historical time-to-currency mapping, relying instead on a static exchange rate attribute inside the fact table or converting dynamically at query time using today's rates.

Step 2: Modifying the Existing Schema in Erwin & Database (Alter & Backfill)

Instead of rebuilding the entire data warehouse, we altered the existing structure to incorporate a historical currency mapping:

1. In Erwin Data Modeler & Database:

   • Create a new dim_currency_exchange_rate table containing natural keys for currency_code, target_currency_code, exchange_rate, and effective_date.

   • Alter the existing fact_order table to include a foreign key linking transactions to the currency dimension based on the transaction date.

2. Handling Existing Stored Data:

   • Run a one-time migration script to populate historical daily exchange rates across all operating regions from inception.

   • Update existing fact records to bind them to the correct historical exchange rate matching the order date.

Step 3: Implementing the Currency Conversion ETL Pipeline

For ongoing processing, the ingestion pipeline maps incoming transactions to the precise daily financial conversion value.

1. Inserting or refreshing daily exchange rates from financial feeds:

INSERT INTO dim_currency_exchange_rate (from_currency, to_currency, exchange_rate, rate_date) SELECT stg.from_currency, stg.to_currency, stg.rate, stg.rate_date FROM stg_daily_fx_rates stg;

2. Fact ingestion joining to the exact daily exchange rate:

INSERT INTO fact_order_line (order_id, order_date, local_currency_amount, currency_code) SELECT stg.order_id, stg.order_date, stg.amount, stg.currency_code FROM stg_orders stg JOIN dim_currency_exchange_rate fx ON stg.currency_code = fx.from_currency AND stg.order_date = fx.rate_date;

Step 4: Power BI Report Design & DAX Validation

• Model Relationships: Connect fact_order_line to dim_currency_exchange_rate using a Many-to-One (*:1) relationship based on both currency code and transaction date.

• DAX Measure Result: Because the fact table resolves the exact daily rate active when the order occurred, global revenue aggregates cleanly into base currency without audit variance.

Total Converted Revenue = SUMX( fact_order_line, fact_order_line[local_currency_amount] * RELATED(dim_currency_exchange_rate[exchange_rate]) )

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 Challenge 3 :  Insurance - missing columns for claims in policy table

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Step 1: Identifying the Anomaly & Root Cause Analysis

• The Production Symptom: Actuarial and finance teams discovered severe inaccuracies in reserving triangles and loss-ratio reports because claim payments and outstanding case reserves were being linked to the wrong policy underwriting year or coverage split.

• Root Cause Analysis: The data model enforced a simplistic 1:1 relationship between a claim and a policy number, failing to handle complex multi-line commercial policies where a single incident triggered multiple coverage parts (e.g., General Liability, Property, and Workers' Compensation) with separate sub-limits and deductibles.

• The Flaw: We found that treating claims as flat records attached directly to a master policy ignored the intermediate Coverage Layer / Risk Unit grain, causing payouts to bleed across unrelated coverage buckets and distorting actuarial reserving calculations.

Step 2: Modifying the Existing Schema in Erwin & Database (Alter & Backfill)

Instead of keeping a direct policy-to-claim link, we restructured the schema to introduce an intermediate coverage split entity:

1. In Erwin Data Modeler & Database:

   • Create a new bridging or dimension table called dim_policy_coverage containing coverage_id, policy_id, line_of_business, sub_limit_amount, and deductible.

   • Alter the existing fact_claim table to foreign-key link to coverage_id instead of directly to policy_id, ensuring every claim transaction maps to its specific legal coverage line.

2. Handling Existing Stored Data:

   • Run a one-time migration script parsing historical claim notes and payment logs to re-allocate past claim payouts to their correct sub-coverage buckets based on the loss description and policy schedule.

Step 3: Implementing the Claim-to-Coverage Allocation Pipeline

For ongoing claim processing, the ingestion pipeline maps payment transactions and reserve adjustments to the exact coverage line.

1. Inserting or updating claim transaction records tied to specific coverages:

INSERT INTO fact_claim_transaction (claim_id, coverage_id, transaction_type, transaction_amount, transaction_date) SELECT stg.claim_id, cov.coverage_id, stg.tx_type, stg.amount, stg.tx_date FROM stg_claim_feed stg JOIN dim_policy_coverage cov ON stg.policy_id = cov.policy_id AND stg.line_of_business = cov.line_of_business;

2. Calculating net incurred losses against specific coverage sub-limits:

SELECT cov.line_of_business, SUM(tx.transaction_amount) AS total_incurred FROM fact_claim_transaction tx JOIN dim_policy_coverage cov ON tx.coverage_id = cov.coverage_id GROUP BY cov.line_of_business;

Step 4: Power BI Report Design & DAX Validation

• Model Relationships: Connect fact_claim_transaction to dim_policy_coverage using a Many-to-One (*:1) relationship, which then relates back to dim_policy.

• DAX Measure Result: Because claims are segmented by the precise coverage line rather than a blanket policy header, actuarial loss development triangles aggregate without cross-liability distortion.

Total Incurred Loss = CALCULATE( SUM(fact_claim_transaction[transaction_amount]), fact_claim_transaction[transaction_type] IN {"Payment", "Outstanding Reserve"} )

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 Challenge 4 :  Insurance - missing columns for claims in policy table

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152 ) Explain Gdpr & Hipaa in data governance?

Explain Gdpr & Hipaa in data governance? 

• HIPAA  (Health Insurance Portability and Accountability Act) is United States legislation) 

What are the primary HIPAA goals?

• To limit the use of protected health information to those with a “need to know”
• To penalize those who do not comply with confidentiality regulations

What health information is protected?

• Any healthcare information with an identifier that links a specific patient to healthcare information (name, social security number, telephone number, email address, street address, among others)

https://www.techtarget.com/searchhealthit/definition/HIPAA

https://www.ncbi.nlm.nih.gov/books/NBK500019/

 

• GDPR (The General Data Protection Regulation )

https://en.wikipedia.org/wiki/General_Data_Protection_Regulation

https://gdpr.eu/what-is-gdpr/

The General Data Protection Regulation (GDPR) is a European law that protects individuals' personal information and their fundamental rights and freedoms

151 ) find the employees with sal more than manager

151 ) find the employees with sal more than manager

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SELECT    e.name  

FROM     Employee   e

WHERE     e.salary   > (

        SELECT  m.salary  FROM  Employee m 

        WHERE m.id = e.managerId

    );    

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 Below is DDL and DML for same 

### DDL (Create Table)

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

 CREATE TABLE employee

  (
     id        INT PRIMARY KEY,
     NAME      VARCHAR(50),
     salary    INT,
     managerid INT
  ); 

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

### DML (Insert Data)

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

INSERT INTO Employee (id, name, salary, managerId) 

VALUES 

  (1, 'Joe', 70000, 3), 

  (2, 'Henry', 80000, 4), 

  (3, 'Sam', 60000, NULL), 

  (4, 'Max', 90000, NULL);

INSERT INTO Employee (id, name, salary, managerId) 

VALUES 

  (5, 'Alice', 85000, 3), 

  (6, 'Bob', 95000, 4), 

  (7, 'Charlie', 62000, 1), 

  (8, 'Diana', 78000, 2), 

  (9, 'Ethan', 91000, NULL), 

  (10, 'Fiona', 72000, 3), 

  (11, 'George', 68000, 1), 

  (12, 'Hannah', 88000, 4), 

  (13, 'Ian', 99000, NULL), 

  (14, 'Julia', 75000, 2);

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

select * from Employee order by 1  

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

output  : 

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

1	Joe	70000	3
2	Henry	80000	4
3	Sam	60000
4	Max	90000
5	Alice	85000	3
6	Bob	95000	4
7	Charlie	62000	1
8	Diana	78000	2
9	Ethan	91000
10	Fiona	72000	3
11	George	68000	1
12	Hannah	88000	4
13	Ian	99000
14	Julia	75000	2

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

--------------------- # Method 1  : using joins  ---------

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

 

 SELECT 

    e1.name AS Employee, 

    e1.salary AS salary, 

    e2.name AS manager, 

    e2.salary AS manager_salary

FROM 

    employee e1

JOIN 

    employee e2 ON e1.managerid = e2.id

WHERE 

    e1.salary > e2.salary;

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

output : 

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

id, name, salary, managerId

Joe	70000	Sam	60000
Alice	85000	Sam	60000
Bob	95000	Max	90000
Fiona	72000	Sam	60000

    

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

--------------------------------- # Method 2 : using  SUBQUERY ---------

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

SELECT     e.name AS Employee

FROM     Employee e

WHERE     m.salary > (

        SELECT  m.salary  FROM Employee m 

        WHERE m.id = e.managerId

    );    

150 ) Data model - Optimization

 

 Data model - Optimization 

 Diagnosing, Reporting, and Resolving a Data Model Problem

Here is the complete, end-to-end process showing how a performance bottleneck is spotted in a database tool, reported to the data model team, and successfully resolved.

149 ) EDM with on (core subject areaas ) insurance project

An Enterprise Data Model (EDM) is traditionally designed as an OLTP (Online Transaction Processing) database structured in Third Normal Form (3NF) (or higher), rather than a dimensional model.

Here is how it breaks down in enterprise architecture:

1. Enterprise Data Model (EDM) = 3NF / OLTP Structure

• Purpose: An EDM (like the life insurance model discussed above) serves as the single source of truth for an entire enterprise's operational processes. It is designed to capture, process, and store atomic, transaction-level data with minimal redundancy.

 Plaintext

Life Insurance Enterprise Data Model
│
├── 1. Party Management Domain
│   ├── T_PARTY
│   ├── T_PARTY_ADDRESS
│   └── T_PARTY_CONTACT
│
├── 2. Product & Policy Domain
│   ├── T_PRODUCT
│   ├── T_POLICY
│   ├── T_COVERAGE
│   └── T_BENEFICIARY
│
├── 3. Underwriting & Risk Domain
│   ├── T_UNDERWRITING_CASE
│   └── T_MEDICAL_REQUIREMENT
│
├── 4. Billing & Premium Domain
│   ├── T_BILLING_ACCOUNT
│   └── T_PREMIUM_TRANSACTION
│
├── 5. Claims Management Domain
│   ├── T_CLAIM
│   └── T_CLAIM_PAYMENT
│
├── 6. Agent & Commission Domain
│   ├── T_AGENT
│   └── T_COMMISSION_TRANSACTION
│
└── 7. Reference & Lookup Domain
    ├── R_POLICY_STATUS
    ├── R_CLAIM_STATUS
    └── R_PRODUCT_TYPE

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

PHASE 1 : Client meeting

 ( How client shares his OLTP requirement ) 

Client Meeting Transcript / Summary

Client: "Hi team. We are modernizing our core systems for our life insurance business. Right now, our data is heavily fragmented across legacy apps, spreadsheets, and third-party vendor tools. When a customer buys a policy, updates an address, or files a claim, things don't sync properly, and we run into data duplication and consistency issues. We need a centralized, rock-solid core database that can handle live, day-to-day transactions accurately without errors."

Architect / Technical Lead: "To make sure I understand correctly, you need a single source of truth that acts as the operational backbone for all live transactions—handling high-frequency data entries, rigorous data validation (like making sure a policy can't be issued without a valid insured party), and strict record-keeping?"

Client: "Exactly. It needs to capture every single operational detail cleanly. Specifically, we need to manage:"

1. Parties: Comprehensive profiles for our customers, agents, and beneficiaries, including their history of addresses and contact details.

2. Products & Policies: The actual life insurance products we sell, active policy contracts, riders/coverages attached to them, and designated beneficiaries.

3. Underwriting: Tracking the medical and financial risk assessment cases before a policy gets approved.

4. Billing & Premiums: Managing payment accounts, frequencies, and logging every single incoming payment transaction.

5. Claims: Handling the full lifecycle of a claim from the initial date of loss, through investigation, to final payout.

6. Agencies & Commissions: Tracking which agents sell what and calculating their commission payouts.

Architect / Technical Lead: "That gives us a complete picture. Based on what you've described—handling high-volume, concurrent operational transactions with zero redundancy and strict referential integrity—we should not build a reporting or analytical structure right now.

Instead, we need to design an Enterprise Data Model (EDM) built as an OLTP (Online Transaction Processing) database in Third Normal Form (3NF). This will ensure atomic data storage, eliminate anomalies for your daily operations, and cleanly organize everything into the core subject areas you outlined: Party Management, Product & Policy, Underwriting, Billing & Premium, Claims Management, and Agent & Commission. Once this operational foundation is live, we can feed it downstream into analytical star schemas for your executive dashboards."

Client: "That makes total sense. Let's proceed with that architectural approach

1.1 . Questions asked by Data Architect 

Phase 1: Understanding Business Processes & Operational Pain Points

These questions uncover how the business runs day-to-day and why the current system is failing.

1)"Can you walk me through the lifecycle of a life insurance policy—from the moment a customer applies, to when it gets approved, billed, and eventually paid out or claimed?"  

2) "Where are you experiencing the most friction or data inconsistency today across your current legacy systems or spreadsheets?" (Identifies operational bottlenecks and data silos).

3) "Do you need multiple systems to write data simultaneously, and how critical is it that a change in customer details updates across everything instantly?" (Justifies the need for an OLTP transactional backend rather than a batch-loaded data warehouse).