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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

Processing Streaming Data

Streaming data is data that is continuously generated and updated in real time, such as sensor readings, weblogs, social media posts, online transactions, and more. Streaming data can provide valuable insights into the current state and trends of various domains, such as e-commerce, finance, health care, gaming, and the Internet of Things (IoT). However, streaming data also poses many challenges for data ingestion and processing, such as scalability, reliability, fault tolerance, latency, and consistency.

Apache Spark is a popular open source framework for large-scale distributed data processing. Apache Spark Structured Streaming is an extension of Spark SQL that enables scalable and fault-tolerant processing of streaming data using a declarative API based on DataFrames and datasets. Apache Spark Structured Streaming supports various sources and sinks for streaming data, such as Kafka, Flume, Hadoop Distributed File System (HDFS), Amazon Simple Storage...

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