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Data Engineering with Databricks Cookbook

You're reading from   Data Engineering with Databricks Cookbook Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781837633357
Length 438 pages
Edition 1st Edition
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Author (1):
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Pulkit Chadha Pulkit Chadha
Author Profile Icon Pulkit Chadha
Pulkit Chadha
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Table of Contents (16) Chapters Close

Preface 1. Part 1 – Working with Apache Spark and Delta Lake FREE CHAPTER
2. Chapter 1: Data Ingestion and Data Extraction with Apache Spark 3. Chapter 2: Data Transformation and Data Manipulation with Apache Spark 4. Chapter 3: Data Management with Delta Lake 5. Chapter 4: Ingesting Streaming Data 6. Chapter 5: Processing Streaming Data 7. Chapter 6: Performance Tuning with Apache Spark 8. Chapter 7: Performance Tuning in Delta Lake 9. Part 2 – Data Engineering Capabilities within Databricks
10. Chapter 8: Orchestration and Scheduling Data Pipeline with Databricks Workflows 11. Chapter 9: Building Data Pipelines with Delta Live Tables 12. Chapter 10: Data Governance with Unity Catalog 13. Chapter 11: Implementing DataOps and DevOps on Databricks 14. Index 15. Other Books You May Enjoy

Ingesting Streaming Data

Using the Spark SQL engine, Apache Spark Structured Streaming provides a stream processing engine that can handle large-scale and reliable data streams. You can write your streaming computation using the same syntax as a batch computation on static data. The Spark SQL engine will run your computation in an incremental and continuous manner and keep the final result updated as new streaming data arrives. The computation is performed on the same efficient Spark SQL engine. The system also ensures that the computation is fault-tolerant from end to end by using checkpointing and write-ahead logs.

Apache Spark Structured Streaming is favored for real-time data processing due to its high-level, unified API that seamlessly integrates both streaming and batch data processing. This unified approach simplifies development, making it accessible to those familiar with Spark SQL. It offers a wide range of benefits, including built-in fault tolerance mechanisms, support...

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