Phase 6: Data Engineering & Analytics
Modern systems are data-intensive. Learn how to process, transform, and analyze data at scale with stream processing, data warehousing, and event-driven architectures.
Stream Processing
Kafka Streams, Apache Flink, Spark Streaming. Real-time data processing at scale.
Data Warehousing
Snowflake, BigQuery, Redshift. OLAP workloads and columnar storage for analytics.
Data Lakes
S3 + Parquet/ORC, Delta Lake, Apache Iceberg. Raw data storage with schema-on-read.
ETL/ELT Pipelines
Apache Airflow, dbt, Fivetran. Orchestrating data transformation workflows.
Change Data Capture
Debezium, Maxwell. Real-time database replication and event streaming.
Event Sourcing & CQRS
Event stores, command-query separation. Audit trails and temporal queries.
Batch vs Stream
MapReduce, Spark batch mode. When to use batch processing vs real-time streaming.
Data Validation
Great Expectations, schema enforcement. Ensuring data quality in pipelines.