Big Data Analyst Training

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Big Data Analyst offers a wide range of scope for the job seekers, data analytics is considered as a backbone of the company, thus, the employees handling the related department should have working knowledge on SQL or basic LINUX commands, the database, SQL, and query language for databases. The Big Data Analyst Training insights on installing, updating and maintaining MongoDB environment. Make them understand Data Volume, Data Evolution, Velocity of Data, MapReduce and the procedure of developing distributed processing of large data sets across clusters of computers and administering Hadoop.

After completing the Big Data Analyst Certification, the candidates would be able to:

  • Perform data management and text processing using Hive
  • Understand the comparative study of MapReduce, Pig, Hive, Impala, and Relational Databases
  • Develop expertise on the basic concepts of Apache Storm and its architecture.
  • Demonstrate what is Grouping & Data Insertion in Apache Storm?
  • How to develop the skill sets that help in processing a huge amount of data by using MongoDB tools?
  • Develop an understanding on Resilient Distributed Dataset and DataFrames
Target audience
  • Analytics professionals
  • Data Scientists
  • IT developers and testers
  • Project Managers
  • Research professionals
  • The software developers, who indulge in processing large amounts of data
Prerequisites

The candidates should have working knowledge of SQL or basic LINUX commands, database and SQL.

Big Data Analyst offers a wide range of scope for the job seekers, data analytics is considered as a backbone of the company, thus, the employees handling the related department should have working knowledge on SQL or basic LINUX commands, the database, SQL, and query language for databases. The Big Data Analyst Training insights on installing, updating and maintaining MongoDB environment. Make them understand Data Volume, Data Evolution, Velocity of Data, MapReduce and the procedure of developing distributed processing of large data sets across clusters of computers and administering Hadoop.

After completing the Big Data Analyst Certification, the candidates would be able to:

  • Perform data management and text processing using Hive
  • Understand the comparative study of MapReduce, Pig, Hive, Impala, and Relational Databases
  • Develop expertise on the basic concepts of Apache Storm and its architecture.
  • Demonstrate what is Grouping & Data Insertion in Apache Storm?
  • How to develop the skill sets that help in processing a huge amount of data by using MongoDB tools?
  • Develop an understanding on Resilient Distributed Dataset and DataFrames
Target audience
  • Analytics professionals
  • Data Scientists
  • IT developers and testers
  • Project Managers
  • Research professionals
  • The software developers, who indulge in processing large amounts of data
Prerequisites

The candidates should have working knowledge of SQL or basic LINUX commands, database and SQL.

Big Data Analyst Training Course Content

1. Hadoop Data Analytics

  • Introduction
  • Hadoop Fundamentals
  • Introduction to Pig
  • Basic Data Analysis with Pig
  • Processing Complex Data with Pig
  • Multi-Dataset Operations with Pig
  • Extending Pig
  • Pig Troubleshooting and Optimization
  • Introduction to Hive
  • Relational Data Analysis with Hive
  • Hive Data Management
  • Text Processing with Hive
  • Hive Optimization
  • Extending Hive
  • Introduction to Impala
  • Analyzing Data with Impala
  • Choosing the Best Tool for the Job

2. Apache Spark

  • An Introduction to Spark
  • About Resilient Distributed Dataset and DataFrames
  • The Spark application programming
  • An Introduction to Spark libraries
  • About Spark configuration, monitoring and tuning

3. Apache Storm

  • Big Data Overview
  • Introduction to Storm
  • Installation and Configuration
  • Storm Advanced Concepts
  • Storm Interfaces
  • Storm Trident

4. MongoDB

  • Design Goals, Architecture and Installation
  • CRUD Operations
  • Schema Design and Data Modelling
  • Administration
  • Scalability and Availability
  • Indexing and Aggregation Framework
  • Application Engineering and MongoDB Tools
  • Project, Additional Concepts and Case Studies

5. Cassandra

  • Introduction to Cassandra Enterprise
  • Cassandra Enterprise Operations and Performance Tuning
  • Cassandra Enterprise Search with Apache Solr
  • Cassandra Core Concepts
  • Data Modeling with Cassandra Enterprise
  • Cassandra Enterprise Analytics with Apache Spark

Note: to know about the detailed information about the course modules please feel free to write us or give us a buzz.

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