data-engineer-handbook

Implement data warehouse schemas with star and snowflake models. Learn when to use denormalized dimensions for speed or normalized structures for efficiency and data integrity.

Learn to build an LLM-powered SQL query engine with LangChain. This guide covers prompt engineering, SQL validation, and secure database execution for natural language to SQL conversion.

Design modern OLAP architectures with ClickHouse and Apache Druid. Learn to combine batch and streaming analytics for petabyte-scale data processing using Kafka and schema registries.

Master pandas data cleaning with a five-step workflow. Learn best practices for production-ready datasets, preventing errors and data leakage. Your data engineer handbook guide.

Build robust data engineering projects on Databricks and Azure. This guide details an end-to-end architecture from ingestion to secure storage and transformation, optimizing your data pipelines.

Master incremental Slowly Changing Dimensions Type 2 SQL queries. Learn to efficiently update dimension tables using CTEs and UNNEST for accurate historical and current data.

Design flexible graph-based data models for player-game relationships with SQL. Implement property graphs for efficient player analysis, career tracking, and co-play statistics.

Learn how to build data pipelines with Apache Airflow orchestration. This guide details Python-based DAGs, task dependencies, and monitoring for reliable data workflows.

Learn how to implement data deduplication strategies for microbatch processing using stateful filtering, window functions, and idempotent writes for exactly-once semantics.

Set up local Spark development environments with Docker. Clone the DataExpert-io repository and use `make up` for a complete setup including Iceberg, MinIO, and Jupyter Notebook.

Learn to implement data visualization and impact analysis using a three layered architecture. Ingest data, transform with dbt, and expose KPIs via REST APIs for stakeholder BI tools.

Design effective KPIs and run A/B testing experiments in data engineering. Learn to define metrics, implement user assignment, and log conversions for statistical significance.