Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3
Key Features
- Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud
- Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink
- Exploit big data using Hadoop 3 with real-world examples
Book Description
Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples.
Once you have taken a tour of Hadoop 3's latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases.
By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
What you will learn
- Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce
- Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples
- Integrate Hadoop with R and Python for more efficient big data processing
- Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics
- Set up a Hadoop cluster on AWS cloud
- Perform big data analytics on AWS using Elastic Map Reduce
Who This Book Is For
Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3's powerful features, or you're new to big data analytics. A basic understanding of the Java programming language is required.
Table of Contents
- Introduction to Hadoop
- Overview of Big Data Analytics
- Big Data Processing with MapReduce
- Scientific Computing and Big Data Analysis with Python and Hadoop
- Statistical Big Data Computing with R and Hadoop
- Batch analytics with Apache Spark
- Real time analytics with Apache Spark
- Batch analytics with Apache Flink
- Stream Processing with Apache Flink
- Visualizing Big Data
- Introduction to Cloud Computing
- Using Amazon Web Services
Sridhar Alla is a big data expert helping small and big companies solve complex problems, such as data warehousing, governance, security, real-time processing, high-frequency trading, and establishing large-scale data science practices. He is an agile practitioner as well as a certified agile DevOps practitioner and implementer. He started his career as a storage software engineer at Network Appliance, Sunnyvale, and then worked as the chief technology officer at a cyber security firm, eIQNetworks, Boston. His job profile includes the role of the director of data science and engineering at Comcast, Philadelphia. He is an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also provides onsite/online training on several technologies. He has several patents filed in the US PTO on large-scale computing and distributed systems. He holds a bachelors degree in computer science from JNTU, Hyderabad, India, and lives with his wife in New Jersey.
Sridhar has over 18 years of experience writing code in Scala, Java, C, C++, Python, R and Go. He also has extensive hands-on knowledge of Spark, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing and high performance computing.
