Data engineers are stuck in a reactive loop: maintaining fragile pipelines using complex tools while managing constantly shifting priorities.
The Data Engineer Toolkit is a collection of best-practice resources to help data engineers be less reactive and more productive.
From ensuring data reliability to streamlining ETL and optimizing data pipelines — this toolkit features technical guidance that data engineers can put to work, as well as insights from technical experts.
You’ll find:
- Frameworks for simplifying ingestion, transformation and orchestration
- Visual design patterns for ETL vs. ELT and batch vs. streaming pipelines
- Best practices for governance, lineage and observability
- Real-world examples and practitioner insights from Databricks customers and experts
- Hands-on training for building and optimizing pipelines
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A practical reference guide for reliable, scalable pipelines
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