Big Data Hadoop Analyst Training
This course will enable an Analyst to work on Big Data and Hadoop which takes into consideration the burgeoning demands of the industry to process and analyze data at high speeds. This Training Course will give you the right skills to deploy various tools and techniques to be a Hadoop Analyst working with Big Data.
What you will learn in this Hadoop Analyst Training Course?
- Hadoop Architecture and Ecosystem
- Learn about Apache Hive, Pig, YARN
- Complex data processing techniques
- Come up with Hadoop real-time queries using Impala
- Integrate HBase with MapReduce
- Deploy MapReduce advanced Indexing
- ETL connectivity with Hadoop ecosystem
- Real-time analysis on large data sets
What are the prerequisites for taking this Big Data Analyst Training Course?
A basic knowledge in any programming language is beneficial but not necessary.
Why should you take this Hadoop Analyst Online Training Course?
Hadoop is gaining a steady groundswell with some of the biggest companies exclusively relying on Hadoop for making sense of Big Data. This Combo Course will help you work on the Hadoop framework and process humungous amounts of data at top speeds so as to make sense of it in real-time. There is a huge demand for professionals with the exact skills that this Training Course is providing. This shall ensure you can get top salaries and grow in your career.
Hadoop Analyst Course Content
Introduction to Big Data & Hadoop and its Ecosystem, Map Reduce and HDFS
What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x
Hadoop Installation & setup
Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster
Deep Dive in Mapreduce
How Mapreduce Works, How Reducer works, How Driver works, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sort, Mapside Joins, Reduce Side Joins, MRUnit, Distributed Cache
Lab exercises :
Working with HDFS, Writing WordCount Program, Writing custom partitioner, Mapreduce with Combiner , Map Side Join, Reduce Side Joins, Unit Testing Mapreduce, Running Mapreduce in Local Job Runner Mode
Graph Problem Solving
What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,
Exercise 1: Exercise 2:Exercise 3:
Who should attend
- Business Professionals,Data and System Analysts
- ETL and Data warehousing Professionals, Project Managers and Business Intelligence experts
- Anyone wants to learn Big data and Hadoop and doesn’t have programming experience