Description
Book Synopsis: Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you'll be able to:
- Learn Python, SQL, Scala, or Java high-level Structured APIs
- Understand Spark operations and SQL Engine
- Inspect, tune, and debug Spark operations with Spark configurations and Spark UI
- Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
- Perform analytics on batch and streaming data using Structured Streaming
- Build reliable data pipelines with open source Delta Lake and Spark
- Develop machine learning pipelines with MLlib and productionize models using MLflow
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Details
Are you struggling to process big data efficiently for your analytics or machine learning projects? Look no further than Learning Spark: Lightning-Fast Data Analytics Book. With the updated edition featuring Spark 3.0, this book is the ultimate guide for data engineers and data scientists seeking to understand the importance of structure and unification in Spark. Whether you prefer using Python, SQL, Scala, or Java high-level Structured APIs, this book has got you covered.
Do you want to master Spark operations and the SQL Engine? This book provides step-by-step walk-throughs, code snippets, and notebooks to help you become proficient in performing simple and complex data analytics, and employing machine learning algorithms. You'll also learn how to inspect, tune, and debug Spark operations with Spark configurations and Spark UI.
Connecting to various data sources can be a challenge, but not with Learning Spark by your side. You'll gain the knowledge and skills to connect seamlessly to JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka. Whether you're working with batch data or streaming data using Structured Streaming, this book will teach you how to perform powerful analytics on both.
Building reliable data pipelines is a crucial aspect of data processing, and this book introduces you to open-source Delta Lake and Spark, enabling you to create robust and scalable data pipelines effortlessly. Additionally, you'll discover how to develop machine learning pipelines with MLlib and effectively productionize models using MLflow.
Don't miss out on the opportunity to become a master of data analytics and machine learning with Apache Spark. Take your skills to the next level by grabbing a copy of Learning Spark: Lightning-Fast Data Analytics Book today.
Click here to purchase the book and unlock your potential in the world of data analytics and machine learning.
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