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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

By : Pulkit Chadha
4.4 (7)
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Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

4.4 (7)
By: Pulkit Chadha

Overview of this book

Written by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.
Table of Contents (16 chapters)
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1
Part 1 – Working with Apache Spark and Delta Lake
9
Part 2 – Data Engineering Capabilities within Databricks

Reading data from real-time sources, such as Apache Kafka, with Apache Spark Structured Streaming

In this recipe, you will learn how to read data from real-time sources, such as Apache Kafka, with Apache Spark Structured Streaming. You will use the same APIs as when working with batch data. Integrating Apache Spark and Apache Kafka offers a powerful combination of real-time data processing capabilities. It enables real-time data processing as Kafka serves as a highly scalable and fault-tolerant message broker that receives and delivers data streams, which Spark can ingest and analyze as they are generated. Kafka acts as a data buffer, ensuring that data is not lost in cases of processing delays or failures in Spark.

Getting ready

Before we start, we need to make sure that we have a Kafka cluster running and a topic that produces some streaming data. For simplicity, we will use a single-node Kafka cluster and a topic named users. Open the 4.0 user-gen-kafka.ipynb notebook and...

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