Day 28 - Amazon Redshift

Date: 2025-10-15 (Wednesday)
Status: “Done”

Lecture Notes

Amazon Redshift

Fully managed cloud data warehouse optimized for large-scale analytics (OLAP).

  • Columnar storage, compression, MPP execution; scales from hundreds of GB to PB.
  • Integrations: S3, Kinesis, DynamoDB, BI tools; strong security features.
  • Concurrency Scaling adds capacity automatically during spikes.
  • Architecture: cluster (leader node + compute nodes), each compute node has slices.

Deployment options:

  • Redshift Provisioned
  • Redshift Serverless
  • Redshift Spectrum (query S3 directly)

Use cases: enterprise BI, data lake analytics, dashboards, trend analysis, forecasting.

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Redshift Features:

  • Columnar Storage: Optimized for analytics queries
  • Massively Parallel Processing (MPP): Distributes queries across nodes
  • Result Caching: Speeds up repeated queries
  • Automatic Compression: Reduces storage costs
  • Workload Management (WLM): Query prioritization
  • Concurrency Scaling: Handle burst workloads

Redshift vs Traditional Data Warehouse:

Feature Redshift Traditional DW
Setup Minutes Weeks/Months
Scaling Elastic Fixed capacity
Cost Pay-as-you-go Large upfront
Maintenance Managed Self-managed

Redshift Spectrum:

  • Query data directly in S3 without loading
  • Separate compute and storage
  • Support for various file formats (Parquet, ORC, JSON)
  • Cost-effective for infrequently accessed data

Hands-On Labs

Lab 43 – AWS Database Migration Service (DMS) (Part 2)

  1. MSSQL → Aurora MySQL Target Config → 43-07
  2. MSSQL → Aurora MySQL Create Project → 43-08
  3. MSSQL → Aurora MySQL Schema Conversion → 43-09
  4. Oracle → MySQL Schema Conversion (1) → 43-10
  5. Create Migration Task & Endpoints → 43-11