Название: Ultimate Big Data Analytics with Apache Hadoop: Master Big Data Analytics with Apache Hadoop Using Apache Spark, Hive, and Python
Автор: Simhadri Govindappa
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 787
Язык: английский
Формат: pdf, epub
Размер: 25.3 MB

Master the Hadoop Ecosystem and Build Scalable Analytics Systems.

Key Features:
- Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management.
- Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics.
- Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics.

Book Description
In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape.

This book serves as your comprehensive guide to navigating the complexities of Big Data analytics using Hadoop, offering both foundational knowledge and practical expertise to empower your journey. The Hadoop ecosystem, nurtured under the auspices of the Apache Software Foundation (ASF), represents a cornerstone of modern data processing infrastructure. Starting with an exploration of Hadoop's historical roots and its evolution, we delve into its pivotal role in managing vast datasets efficiently. We also illuminate the broader impact of the Apache Software Foundation, highlighting its collaborative ethos and the innovation it catalyzes across the technology sector.

The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises.

You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with Big Data visualization and statistical analysis using Python.

Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively.

Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop.

What you will learn:
- Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce.
- Master real-time analytics and data processing with Apache Spark’s powerful features.
- Develop skills in using Apache Hive for efficient data warehousing and complex queries.
- Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem.
- Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta.
- Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes.
- Build and deploy machine learning models with tools integrated into the Hadoop ecosystem.

Who is this book for?
This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material.

Contents:









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