Advanced Hadoop Techniques A Comprehensive Guide To Mastery
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Advanced Hadoop Techniques: A Comprehensive Guide to Mastery
Unlock the full potential of Hadoop with "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery"—your essential resource for navigating the intricate complexities and harnessing the tremendous power of the Hadoop ecosystem. Designed for data engineers, developers, administrators, and data scientists, this book elevates your skills from foundational concepts to the most advanced optimizations necessary for mastery. Delve deep into the core of Hadoop, unraveling its integral components such as HDFS, MapReduce, and YARN, while expanding your knowledge to encompass critical ecosystem projects like Hive, HBase, Sqoop, and Spark. Through meticulous explanations and real-world examples, "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery" equips you with the tools to efficiently deploy, manage, and optimize Hadoop clusters. Learn to fortify your Hadoop deployments by implementing robust security measures to ensure data protection and compliance. Discover the intricacies of performance tuning to significantly enhance your data processing and analytics capabilities. This book empowers you to not only learn Hadoop but to master sophisticated techniques that convert vast data sets into actionable insights. Perfect for aspiring professionals eager to make an impact in the realm of big data and seasoned experts aiming to refine their craft, "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery" serves as an invaluable resource. Embark on your journey into the future of big data with confidence and expertise—your path to Hadoop mastery starts here.
Hadoop: The Definitive Guide
Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN). Store large datasets with the Hadoop Distributed File System (HDFS) Run distributed computations with MapReduce Use Hadoop’s data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud Load data from relational databases into HDFS, using Sqoop Perform large-scale data processing with the Pig query language Analyze datasets with Hive, Hadoop’s data warehousing system Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems
Mastering Apache Spark
Gain expertise in processing and storing data by using advanced techniques with Apache SparkAbout This Book- Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan- Evaluate how Cassandra and Hbase can be used for storage- An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalitiesWho This Book Is ForIf you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.What You Will Learn- Extend the tools available for processing and storage- Examine clustering and classification using MLlib- Discover Spark stream processing via Flume, HDFS- Create a schema in Spark SQL, and learn how a Spark schema can be populated with data- Study Spark based graph processing using Spark GraphX- Combine Spark with H20 and deep learning and learn why it is useful- Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra- Use Apache Spark in the cloud with Databricks and AWSIn DetailApache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations.This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment.Style and approachThis book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.