148 ) ETL & ELT = when to suggest

 

• • Suggest ETL if the client is looking for:

    • Protecting weak databases: Keeping heavy data cleanup away from older or slow source systems so they don't crash or slow down.

    • Strict security upfront: Hiding or locking down sensitive information (like personal IDs or credit card numbers) before sending it anywhere else.

    • Saving storage space: Filtering out garbage data at the starting line so you don't pay to store junk.

  • Suggest ELT if the client is looking for:

    • Maximum speed with cloud power: Dumping raw data quickly into modern cloud tools (like Snowflake) and letting their massive computers handle the cleanup later.

    • Keeping a raw history book: Storing an exact, untouched copy of the original data just in case someone needs to look at it later for audits or machine learning.

    • Simpler, faster pipelines: Moving data over immediately without getting bogged down by complicated cleanup steps along the way.

147 ) Diff between Data lake and Data Lakehouse

 

Differences Between a Data Lake and a Data Lakehouse

Feature / Dimension	Data Lake	Data Lakehouse
Primary Architecture	Raw, low-cost object storage (e.g., AWS S3, Azure Blob) designed to store unformatted files, logs, and unstructured data in a flat file hierarchy.	A unified architectural layer built on top of cloud object storage that combines data lake storage with a transactional storage framework (e.g., Delta Lake, Apache Iceberg).
ACID Transactions	Not supported: Concurrent reads and writes often lead to corrupted data, half-written tables, or read inconsistencies.	Fully supported: Guarantees atomicity, consistency, isolation, and durability (ACID) for safe concurrent reads and writes.
Schema & Data Quality	Schema-on-read: Data is dumped in its raw form without strict structural rules, often turning into an unmanaged "data swamp."	Schema enforcement & evolution: Enforces strict data types, validation rules, and quality checks at ingestion while supporting safe schema changes.
Performance & Indexing	Slow for complex SQL queries and BI reporting because it lacks advanced indexing and statistics collection.	Fast query performance comparable to data warehouses, utilizing file-level statistics, caching, and partitioning (Z-ordering / layout optimization).
Workload Support	Ideal primarily for data science, machine learning, and raw storage archiving.	Supports all workloads simultaneously: BI reporting, SQL analytics, streaming, data science, and machine learning on a single copy of data.
Open Formats & Lock-in	Proprietary or raw file formats (CSV, JSON, plain Parquet) lacking transaction history tracking.	Uses open storage formats wrapped with transaction logs (Parquet + Delta/Iceberg metadata), preventing vendor lock-in.

147 ) Working with NO SQL Databases

 1. Types of NoSQL Databases Available in the Market

NoSQL databases are broadly categorized into four primary types based on their data storage model:

• Document Databases
• How they store data: Stores data as semi-structured documents (JSON, BSON, XML) rather than rigid rows and columns.
• Use Cases: Content management systems, catalogs, and user profiles.
• Market Examples: MongoDB, Couchbase, Amazon DocumentDB.
• Key-Value Stores
• How they store data: The simplest NoSQL structure, storing data as a collection of unique keys paired with values (strings, blobs, or JSON).
• Use Cases: Caching layers, session management, and real-time leaderboards.
• Market Examples: Redis, Amazon DynamoDB, Memcached.
• Wide-Column Stores (Column-Family)
• How they store data: Stores data in column families grouped together rather than traditional rows, allowing massive horizontal scaling across distributed nodes.
• Use Cases: Time-series data, Internet of Things (IoT) logging, and high-velocity transaction tracking.
• Market Examples: Apache Cassandra, ScyllaDB, Google Cloud Bigtable.
• Graph Databases
• How they store data: Uses nodes, edges, and properties to represent and store interconnected data relationships directly.
• Use Cases: Fraud detection, social networks, recommendation engines, and network topology mapping.
• Market Examples: Neo4j, Amazon Neptune, ArangoDB.

2. How to Work with NoSQL Databases (Running Queries)

Unlike relational databases that rely heavily on ANSI SQL, NoSQL systems use APIs, proprietary query languages, or domain-specific query drivers matching their storage model.

Example A: Working with a Document Database (MongoDB)

• Query Concept: Fetching a user profile where the username matches a specific value using the native MongoDB driver or shell.
• Query Command:

JavaScript

// MongoDB shell query

db.users.find({ username: "john_doe" });

Example B: Working with a Key-Value Store (Redis)

• Query Concept: Setting a cache value and retrieving it instantly via its unique key.
• Query Command:

Bash

# Redis CLI commands

SET session:1001 "active_user_token"

GET session:1001

Example C: Working with a Wide-Column Store (Cassandra)

• Query Concept: Using Cassandra Query Language (CQL), which resembles SQL but requires strict partitioning rules.
• Query Command:

SQL

-- Cassandra CQL query

SELECT * FROM sensor_logs

WHERE sensor_id = 'A-402' AND date = '2026-08-01';

3. How to Migrate NoSQL Data to a Relational Database

Migrating semi-structured or unstructured NoSQL data into a rigid relational database (RDBMS) requires transforming nested documents or flexible attributes into a normalized or flattened tabular structure.

Step-by-Step Migration Process:

1. Schema Mapping & Normalization Design:
• Break down nested JSON arrays or embedded objects from the NoSQL store into separate relational child tables linked by foreign keys and surrogate keys.
• Example: A MongoDB document containing an array of multiple addresses must be split into a parent users table and a child user_addresses table.
2. Extraction (Exporting from NoSQL):
• Export data from the NoSQL source into an intermediate structured format (such as CSV, newline-delimited JSON, or flat files) using native export utilities.
• Example (MongoDB):

Bash

mongoexport --collection=users --out=users_export.json

3. Transformation & Flattening:
• Write data transformation scripts (using Python with Pandas, or SQL-based staging tables) to parse JSON attributes, handle missing fields, cast data types, and generate proper primary/foreign key relationships.
4. Loading into the Relational Database:
• Load the cleaned and flattened tabular data into the target RDBMS (e.g., PostgreSQL, SQL Server, or MySQL) using bulk insert tools, ETL pipelines, or staging schemas.
• Example (PostgreSQL Bulk Copy):

SQL

COPY users(user_id, username, email)

FROM '/path/to/users_export.csv'

WITH FORMAT csv, HEADER true;

5. Validation and Integrity Checks:
• Run row-count reconciliation, data type validation, and relational integrity queries (checking foreign key matches and completeness) to ensure no data loss occurred during the transition from the flexible NoSQL store to the rigid relational structure.

  ----------------------

146 ) AWS : which features did you work

 

Relevant AWS Features for Banking Migration

• Amazon Aurora / Amazon RDS: Fully managed relational database services used as the cloud target for migrating on-premises banking databases, offering automated backups, multi-AZ high availability, and enterprise-grade security.

• AWS Database Migration Service (DMS) & AWS Schema Conversion Tool (SCT): Core migration utilities used to seamlessly convert database schemas and handle homogeneous or heterogeneous database migrations with minimal downtime.

• Amazon S3 (Simple Storage Service): Scalable cloud object storage used as the secure data lake landing zone for raw transaction files, customer records, and backup archives.

• AWS Glue: A fully managed, serverless ETL and data integration service used to discover, clean, transform, and load banking data across pipelines.

• Amazon EMR / AWS Glue Spark: Managed big data processing platforms used for heavy data transformation, risk modeling, and scrubbing large volumes of historical transaction data.

• AWS Secrets Manager / AWS Key Management Service (KMS): Secures and manages database credentials, API keys, and encryption keys to meet strict banking compliance standards (e.g., PCI-DSS).

End-to-End Migration Steps (On-Premises / Legacy to AWS)

1. Assessment and Discovery:

   • Evaluate the existing database footprint and schema dependencies using AWS SCT and assessment playbooks to flag compatibility issues or unsupported database objects.

2. Infrastructure and Security Foundation:

   • Provision target AWS resources, configure Virtual Private Clouds (VPCs), subnets, security groups, and implement encryption policies using AWS KMS.

3. Data Model Optimization:

   • Review and refine database structures, partitioning strategies, and indexing to ensure optimal query performance in the target cloud database engine.

4. Schema Conversion and Initial Load:

   • Convert database schemas using AWS SCT and execute full historical bulk data loads from the legacy environment into Amazon RDS or Aurora via AWS DMS.

5. Change Data Capture (CDC) and Synchronization:

   • Set up continuous CDC replication tasks in AWS DMS to mirror ongoing transactional updates from the legacy system to the AWS environment without interrupting live operations.

6. Parallel Testing and Cutover:

   • Run validation checks, reconcile ledger balances, perform user acceptance testing (UAT), and execute the final cutover by switching application connection strings to AWS.

Applications Involved in the Banking Ecosystem

• Core Banking System: Manages customer accounts, deposits, withdrawals, ledger balances, and daily transaction processing.

• Loan Origination and Management System: Handles credit checks, underwriting workflows, loan structuring, and repayment tracking.

• Fraud Detection and AML (Anti-Money Laundering) System: Monitors real-time transaction streams to flag suspicious activity and ensure regulatory compliance.

• Legacy On-Premises Databases: The existing relational database infrastructure being migrated away from.

• Downstream BI & Analytics Platforms: Connects to the new AWS data warehouse or data lake to generate executive dashboards, regulatory reporting, and risk analytics.

Role of a Data Modeler in the Banking Migration Project

• Legacy Schema Reverse Engineering: Analyze existing banking database schemas to map complex relationships, primary/foreign keys, audit triggers, and transactional business rules.

• Cloud-Optimized Structural Design: Redesign physical tables, constraints, and relationships to maximize performance on cloud database engines (like Amazon Aurora).

• Handling Complex Banking Entities: Structure intricate financial hierarchies, such as multi-currency accounts, joint ownership models, transaction categorization, and historical balance trails.

• Data Integrity and Governance Enforcement: Define standardized data types, naming conventions, and constraints to ensure clean, auditable data flows into the new AWS architecture.

• Collaboration with Data and Migration Engineers: Partner with engineering teams to ensure physical data models support efficient ETL transformations, partitioning strategies, and DMS migration tasks in AWS.