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Data Pipelines & Analytics

Build robust data pipelines for analytics and machine learning

Overview​

Data pipelines move, transform, and aggregate data from operational systems to analytical systems. Batch for accuracy, streaming for latency. Data lakes for raw storage, data warehouses for structured analytics.

Core Patterns​

  • Batch vs Streaming - High-latency accuracy vs low-latency approximation
  • ETL/ELT - Extract-Transform-Load vs Extract-Load-Transform
  • Data Lakes & Warehouses - Raw data storage vs curated analytics repository
  • Event Streams - Log-based integration with Kafka, Pulsar
  • Data Quality & Governance - Lineage, cataloging, quality checks
  • ML Features & Model Serving - Feature stores, online/offline features

Next Steps​

  1. Batch vs Streaming - understand trade-offs
  2. ETL/ELT - data transformation strategies
  3. Data Lakes & Warehouses - storage architectures
  4. Event Streams - log-based integration
  5. Data Quality - governance and lineage
  6. Feature Stores - ML feature management