Mastering Apache Spark: Real-Time Big Data Analytics: Build Large-Scale Data Processing Pipelines with Apache Spark

Chesterfield, Greyson

ISBN 13: 9798303148406

Edité par Independently published, 2024

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N° de réf. du vendeur I-9798303148406

Détails bibliographiques

• Titre: Mastering Apache Spark: Real-Time Big Data Analytics: Build Large-Scale Data Processing Pipelines with Apache Spark
• Éditeur: Independently published
• Date de parution: 2024
• Langue: anglais
• ISBN à 13 chiffres: 9798303148406
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

Unlock the power of big data with Mastering Apache Spark: Real-Time Big Data Analytics! This comprehensive guide is your ultimate resource for building, processing, and analyzing large-scale data using Apache Spark, the fast, flexible, and powerful open-source framework for big data processing. Whether you're a data engineer, scientist, or analyst, this book will teach you how to harness Spark's real-time analytics capabilities to process and analyze massive datasets.

Apache Spark is widely used for its speed, ease of use, and scalability. It’s the go-to solution for building data pipelines, running machine learning algorithms, and processing streams of real-time data. In this book, you’ll learn everything from the fundamentals of Spark to advanced techniques for scaling your big data workflows.

What’s Inside:

• Getting Started with Apache Spark: Learn the core concepts behind Apache Spark, including Spark RDDs, DataFrames, and Spark SQL, and how to set up Spark on your system or in the cloud.
• Real-Time Data Processing: Dive into real-time data processing with Spark Streaming, handling live data streams, and building real-time analytics applications.
• Building Data Pipelines: Learn how to design and implement scalable data pipelines that can process large volumes of structured and unstructured data.
• Data Analytics with Spark: Explore how to analyze big data using Spark’s powerful libraries, including Spark MLlib for machine learning and Spark GraphX for graph processing.
• Optimizing Spark Performance: Discover strategies to optimize Spark performance, including partitioning, caching, and using the Catalyst optimizer for SQL queries.
• Advanced Spark Topics: Get hands-on with advanced topics like Spark on Kubernetes, Spark integration with Hadoop, and deploying Spark on cloud platforms such as AWS and Azure.
• Batch vs. Stream Processing: Learn when to use batch processing and when to go for stream processing for different use cases in data analytics.
• Use Cases and Real-World Applications: Explore real-world use cases for Spark in industries like finance, healthcare, e-commerce, and IoT.

By the end of this book, you’ll be equipped with the knowledge and hands-on experience to build efficient, scalable data pipelines and perform advanced real-time big data analytics using Apache Spark.

Ready to master big data with Spark? Grab your copy now and start building powerful, high-performance data solutions that scale with your business needs!