Building a Big Data Analytics Stack

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 31m | 277 MB

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.

What You Will Learn

• Use Pig and Hive in a non-Java way to understand the power of Hadoop
• Explore Spark and use it to stream and batch process
• Use HBase database from Java application
• Find out more about the machine learning toolkit and its use with Spark
• Know how to leverage the pros of Big Data tools