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

 

Working with NoSQL Databases," provides a comprehensive guide to NoSQL storage models, querying techniques, database migration, and a deep dive into Apache Cassandra.

The key sections are summarized below:

1. Types of NoSQL Databases

• Document Databases (e.g., MongoDB, Couchbase): Stores data as semi-structured JSON/BSON documents instead of rigid rows and columns. Ideal for content management systems and user profiles.

• Key-Value Stores (e.g., Redis, DynamoDB): The simplest model, pairing unique keys with values. Ideal for caching layers, sessions, and real-time leaderboards.

• Wide-Column Stores (e.g., Cassandra, Bigtable): Groups data into column families rather than rows, allowing massive horizontal scaling. Ideal for IoT logging and time-series data.

• Graph Databases (e.g., Neo4j, Neptune): Uses nodes, edges, and properties to represent interconnected data. Ideal for fraud detection, recommendation engines, and social networks.

2. Working with NoSQL Databases (Queries)

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

• Document (MongoDB): Uses native drivers or shells (e.g., db.users.find({ username: "john_doe" })).

• Key-Value (Redis): Uses simple CLI commands like SET and GET.

• Wide-Column (Cassandra): Uses Cassandra Query Language (CQL), which resembles SQL but requires strict partitioning rules.

3. Migrating NoSQL to a Relational Database

Moving flexible or nested NoSQL structures into rigid relational tables requires flattening the data through a multi-step process:

1. Schema Mapping & Normalization: Break down nested arrays/JSON objects into parent and child tables linked by foreign keys.

2. Extraction: Export data into flat files (e.g., CSV, JSON) using native tools like mongoexport.

3. Transformation & Flattening: Parse attributes, handle missing fields, cast data types, and map primary/foreign keys using Python/Pandas or staging scripts.

4. Loading: Bulk insert the cleaned tabular data into the target RDBMS (e.g., PostgreSQL, MySQL).

5. Validation: Run row-count reconciliation and integrity checks to ensure zero data loss.

4. Deep Dive: Apache Cassandra

• Storage Architecture: Uses a distributed, masterless structure consisting of Keyspaces, Tables, Partitions (determined by hashing a partition key), and Clustering Columns (for sorting rows within a partition). Physically writes data sequentially to a CommitLog and Memtable, flushing them into immutable SSTables.

• Project Example: Frequently used in high-velocity banking/fraud-detection environments where relational databases fail to scale horizontally for real-time write streams.

• Migrating Cassandra to Snowflake: Involves redesigning wide Cassandra structures into star-schema dimension and fact tables, exporting data into flat files, staging them in cloud storage (AWS S3/Azure Blob), bulk-loading them into Snowflake using COPY INTO, and performing data reconciliation.

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.