
- 332 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Starting with the fundamentals of the databricks lakehouse platform, the book teaches readers on administering various data operations, including Machine Learning, DevOps, Data Warehousing, and BI on the single platform.The subsequent chapters discuss working around data pipelines utilizing the databricks lakehouse platform with data processing and audit quality framework. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, and administer data sharing and orchestration. The book explores how to schedule and manage jobs through the Databricks notebook UI and the Jobs API. The book discusses how to implement DevOps methods on the Databricks Lakehouse platform for data and AI workloads. The book helps readers prepare and process data and standardizes the entire ML lifecycle, right from experimentation to production. The book doesn't just stop here; instead, it teaches how to directly query data lake with your favourite BI tools like Power BI, Tableau, or Qlik. Some of the best industry practices on building data engineering solutions are also demonstrated towards the end of the book.
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Information
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Authors
- About the Reviewer
- Acknowledgement
- Preface
- Errata
- Table of Contents
- 1. Getting Started with Databricks Platform
- 2. Management of Databricks Platform
- 3. Spark, Databricks, and Building a Data Quality Framework
- 4. Data Sharing and Orchestration with Databricks
- 5. Simplified ETL with Delta Live Tables
- 6. SCD Type 2 Implementation with Delta Lake
- 7. Machine Learning Model Management with Databricks
- 8. Continuous Integration and Delivery with Databricks
- 9. Visualization with Databricks
- 10. Best Security and Compliance Practices of Databricks
- Index