Study Guides
Detailed, step-by-step guides on core data engineering topics. Each guide includes code snippets, architecture diagrams, and links to external resources.
Data Pipelines
Architecture, design patterns, error handling, monitoring, and orchestration of end-to-end data pipelines.
ETL & ELT
Extract, transform, load strategies. When to use ETL vs ELT, tools, and best practices for data integration.
Streaming Data
Real-time data processing with Kafka, Flink, and Spark Streaming. Event-driven architectures and exactly-once semantics.
Batch Processing
Batch job design, scheduling, partitioning, and optimization. MapReduce, Spark, and orchestration patterns.
Data Modeling
Dimensional modeling, star schemas, data vault, normalization, and modeling for analytical and operational use cases.
Cloud Data Platforms
Snowflake, BigQuery, Redshift, Databricks. Architecture, pricing, use cases, and multi-cloud strategies.