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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

Configuring Spark Structured Streaming for real-time data processing

In this recipe, you will learn how to configure Apache Spark Structured Streaming using Python for real-time data processing. Spark Structured Streaming is used in a variety of scenarios in which you need to ingest and analyze data as they arrive in real time from sources such as IoT devices, social media streams, sensors, or financial transactions. Structured Streaming provides the means to handle these continuous data streams. This configuration is particularly relevant when low-latency processing is crucial for making timely decisions or taking immediate actions based on incoming data. Structured Streaming also becomes essential when dealing with event time-based processing, enabling you to perform time-based aggregations and windowing operations on data with timestamps.

Getting ready

To run this recipe, we first need to set up incoming streaming data. We will feed data by opening a terminal window in the...

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