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Data Platform And Pipeline Design
Part 1 - Platform & Pipeline Basics
Introduction & Contents (3:13)
The Platform Blueprint (10:11)
Data Engineering Tools Guide (2:44)
End to End Pipeline Example (6:18)
Push Ingestion Pipelines (3:42)
Pull Ingestion Pipelines (3:34)
Batch Pipelines (3:07)
Streaming Pipelines (3:34)
Stream Analytics (2:26)
Lambda Architecture (4:02)
Visualization Pipelines (3:47)
Visualization with Hive & Spark on Hadoop (6:21)
Visualization Data via Spark Thrift Server (3:27)
Platform Examples (Currently slides only)
AWS
Azure
GCP
Hadoop
Part 2 - Advanced Concepts
Part 2 introduction (1:16)
Core Use Cases in Platform Design: Transactions, Analytics, and Reverse ETL (2:57)
Blueprint Recap: Mapping Tools Across the Modern Data Platform (3:31)
Demystifying Event-Driven, Batch, and Streaming Workflows in Data Platforms (8:10)
Micro-Batching vs. Streaming: What’s the Real Difference? (4:55)
Connecting Sources to Goals: Batch and Stream Processing in a Data Platform (6:28)
Building Blocks of a Modern Data Platform: Components, Storage, and Processing (3:10)
Before the Tech: How Data and Goals Shape Your Data Platform (10:09)
Lakehouse Architecture Explained: From Raw Files to Transactional Tables (3:35)
How Machine Learning Fits into Data Platforms: Training, Inference, and Deployment (6:23)
From Embeddings to Answers: Understanding Semantic Search and Retrieval-Augmented Generation (6:06)
Testing in the Modern Data Platform: From Ingestion to Transformation (3:11)
Understanding the Medallion Architecture: Bronze, Silver, and Gold Layers in Data Warehousing (2:25)
Core Use Cases in Platform Design: Transactions, Analytics, and Reverse ETL
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