Curriculum

12 Chapters · 12 Lessons

  1. 1

    Book Preview

    1. Ultimate Big Data Analytics with Apache Hadoop Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Introduction to Hadoop and ASF

    1. (Included in full purchase)

      Introduction to Hadoop and ASF

  4. 4

    Chapter 2 : Overview of Big Data Analytics

    1. (Included in full purchase)

      Overview of Big Data Analytics

  5. 5

    Chapter 3 : Hadoop and YARN MapReduce and Tez

    1. (Included in full purchase)

      Hadoop and YARN MapReduce and Tez

  6. 6

    Chapter 4 : Distributed Query Engines: Apache Hive

    1. (Included in full purchase)

      Distributed Query Engines: Apache Hive

  7. 7

    Chapter 5 : Distributed Query Engines: Apache Spark

    1. (Included in full purchase)

      Distributed Query Engines: Apache Spark

  8. 8

    Chapter 6 : File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta)

    1. (Included in full purchase)

      File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta)

  9. 9

    Chapter 7 : Python and the Hadoop Ecosystem for Big Data Analytics - BI

    1. (Included in full purchase)

      Python and the Hadoop Ecosystem for Big Data Analytics - BI

  10. 10

    Chapter 8 : Data Science and Machine Learning with Hadoop Ecosystem

    1. (Included in full purchase)

      Data Science and Machine Learning with Hadoop Ecosystem

  11. 11

    Chapter 9 : Introduction to Cloud Computing and Other Apache Projects

    1. (Included in full purchase)

      Introduction to Cloud Computing and Other Apache Projects

  12. 12

    INDEX

    1. (Included in full purchase)

      INDEX

About the course

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. 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.

About the Author

Simhadri Govindappa holds a Bachelor of Engineering in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology, Bangalore, India. He is an accomplished professional with significant contributions to the field of big data. Simhadri began his career at GE Healthcare as part of the AI data platform team, where he developed AI models and deep learning annotation tools. His work led to a patent granted by the USPTO (patent no: US11069036B1). He then moved to Cloudera, a pioneer in big data, joining the Apache Hive R&D team. His work primarily focuses on Distributed systems, Apache Iceberg, Apache Hive, Hive- ACID-Spark Connectivity (HWC), and enhancing Hive Acid functionality.