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Zach Wilson

@EcZachly • 48,608 subscribers

Founder @ https://t.co/CWvLDHU2Lx $150k/month | https://t.co/F5VqLpyMZn $5k/month | ADHD | 10 yrs big data experience | ex @meta, @netflix, and @airbnb

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Getting a big tech data engineer job in 2016: - do you know SQL? - yes - here’s $500k Getting a big tech data engineer job in 2024: - do you know Spark, Kafka, Iceberg? - yes - did you shake hands with Bill Inmon when he invented the data warehouse? - no - rejected and ghosted Here’s why! Follow to stay up to date on data engineering!

Getting a big tech data engineer job in 2016: - do you know SQL? - yes - here’s $500k Getting a big tech data engineer job in 2024: - do you know Spark, Kafka, Iceberg? - yes - did you shake hands with Bill Inmon when he invented the data warehouse? - no - rejected and ghosted Here’s why! Follow to stay up to date on data engineering!

43,547 görüntüleme • 2 yıl önce

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

16,688 görüntüleme • 1 yıl önce

Building contextual pipelines for AI is the future! The stack you’re looking for: - compute: Spark or DuckDB - storage: S3 - catalog: Glue catalog - metadata: Apache Iceberg - vector DB: Milvus - foundational model: Claude Sonnet 4.5 - end-to-end AI workflow: n8n or OpenAI AgentKit If you want to build workflows and pipelines like this, join my October 20th AI boot camp!

Building contextual pipelines for AI is the future! The stack you’re looking for: - compute: Spark or DuckDB - storage: S3 - catalog: Glue catalog - metadata: Apache Iceberg - vector DB: Milvus - foundational model: Claude Sonnet 4.5 - end-to-end AI workflow: n8n or OpenAI AgentKit If you want to build workflows and pipelines like this, join my October 20th AI boot camp!

11,130 görüntüleme • 11 ay önce

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