Learn about Big Data tools needed to create Big Data Stack
About This Video
- Understand ETL and deploy a Hadoop project to the cloud
- Learn to maintain a Hadoop cluster running HDFS and Map Reduce in this hands-on course
- A practical course that includes real-world examples of developing YARN and MapReduce
Building a Big Data ecosystem is hard. There are a variety of technologies available and every one of them has its pros and cons. When building a big data pipeline for software engineers, we need to use more low-level tools and APIs such as HBase and Apache Spark.
In this course, we’ll check out HBase, a database built by optimizing on the HDFS. Moving on, we’ll have a bit of fun with Spark MLlib. Finally, you’ll get an understanding of ETL and deploy a Hadoop project to the cloud. Building Big Data Ecosystem is hard. There are a variety of technologies available and every one of them has own pros and cons. Software Engineers we need to use more low-level tools and APIs like HBase and Apache Spark while building big data pipeline.
By the end of the course, you’ll be able to use more high-level tools that have more user-friendly, declarative APIs such as Pig and Hive.
Table of Contents
- Chapter 1 : Pig and Hive
- Chapter 2 : Spark Your Engines
- Chapter 3 : HBase the Hadoop Database
- Chapter 4 : Machine Learning Toolkit
- Chapter 5 : AWS EMR
- Title: Building a Big Data Analytics Stack
- Release date: November 2017
- Publisher(s): Packt Publishing
- ISBN: 9781787125018