Extract-Transform-Load: The Foundation of Data Pipelines
ETL is the core data integration pattern. Learn how extraction, transformation, and loading work, and how modern ETL differs from classical approaches.
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GCP Data Services: Dataflow, BigQuery, and Pub/Sub
Guide to Google Cloud data services for building pipelines. Compare Dataflow vs Kafka, leverage BigQuery for analytics, use Pub/Sub, and design data lakes.
Incremental Loads: Processing Only What Changed
Incremental loads reduce pipeline cost and latency. Learn watermark strategies, upsert patterns, and how to handle late-arriving data.
Joins and Aggregations: SQL Patterns for Data Analysis
Master SQL joins and aggregation techniques for building efficient analytical queries in data warehouses and analytical databases.
Kafka Streams: Real-Time Stream Processing Applications
Kafka Streams is a client library for real-time stream processing. Learn stream primitives, state stores, exactly-once processing, and scaling.
Kimball Dimensional Modeling: Star Schema Techniques
Learn Kimball dimensional modeling techniques for building efficient star schema data warehouses with fact and dimension tables.
Lakehouse: Best of Both Worlds in Data Architecture
Understand how lakehouse architecture combines the scalability of data lakes with the reliability and performance of data warehouses.
One Big Table: The Denormalized Approach to Data Modeling
Learn how One Big Table architecture simplifies data pipelines by combining all attributes into single wide denormalized tables.
PII Handling: Protecting Personal Data in Data Systems
Learn techniques for identifying, protecting, and managing personally identifiable information across your data platform.
Pipeline Orchestration: Coordinating Data Workflows
Airflow, Dagster, and Prefect coordinate complex data workflows. Learn orchestration patterns, DAG design, and failure handling.