Explain the fundamentals of Apache Hadoop, Data ETL (extract, transform, load), data processing using Hadoop tools
Performing data analysis and processing complex data using Pig
Perform data management and text processing using Hive
Extending, troubleshooting, and optimizing Pig and Hive performance
Analyze data with Impala
Comparative study of MapReduce, Pig, Hive, Impala, and Relational Databases
Requirements
The candidates with working experience with SQL or basic LINUX commands are ideal for this training.
Description
|| About Hadoop Data Analytics Training Course
Hadoop Data Analytics online training course explains how to apply data analytics and business intelligence skills to Big Data. This Big Data Analytics training lays emphasis on the usage of Apache Pig, Hive, and Cloudera Impala. It will drive you through the process of developing distributed processing of large data sets across clusters of computers and administering Hadoop.The participants will learn how to handle heterogeneous data coming from different sources. This data may be structured, unstructured, communication records, log files, audio files, pictures, and videos. Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.
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. This Hadoop Analyst training will help you be fully proficient in becoming a master Data Analyst in order to collect, analyze and transform huge volumes of data on the Hadoop cluster setup by deploying powerful tools like SQL and other scripting languages.
Course Content
Live Lecture
·Introductions
Live Lecture
·The Motivation for Hadoop
·Hadoop Overview
·HDFS
·MapReduce
·The Hadoop Ecosystem
·Lab Scenario Explanation
Live Lecture
·What Is Pig?
·Pig’s Features
·Pig Use Cases
·Interacting with Pig
Live Lecture
·Pig Latin Syntax
·Loading Data
·Simple Data Types
·Field Definitions
·Data Output
·Viewing the Schema
·Filtering and Sorting Data
·Commonly-Used Functions
Live Lecture
·Storage Formats
·Complex/Nested Data Types
·Grouping
·Built-in Functions for Complex Data
·Iterating Grouped Data
Live Lecture
·Techniques for Combining Data Sets
·Joining Data Sets in Pig
·Set Operations
·Splitting Data Sets
Live Lecture
·Adding Flexibility with Parameters
·Macros and Imports
·UDFs
·Contributed Functions
·Using Other Languages to Process Data with Pig
Live Lecture
·Troubleshooting Pig
·Logging
·Using Hadoop’s Web UI
·Optional Demo: Troubleshooting a Failed Job with the Web UI
·Data Sampling and Debugging
·Performance Overview
·Understanding the Execution Plan
·Tips for Improving the Performance of Your Pig Jobs
Live Lecture
·What Is Hive?
·Hive Schema and Data Storage
·Comparing Hive to Traditional Databases
·Hive vs. Pig
·Hive Use Cases
·Interacting with Hive
Live Lecture
·Hive Databases and Tables
·Basic HiveQL Syntax
·Data Types
·Joining Data Sets
·Common Built-in Functions
Live Lecture
·Hive Data Formats
·Creating Databases and Hive-Managed Tables
·Loading Data into Hive
·Altering Databases and Tables
·Self-Managed Tables
·Simplifying Queries with Views
·Storing Query Results
·Controlling Access to Data
Live Lecture
·Overview of Text Processing
·Important String Functions
·Using Regular Expressions in Hive
·Sentiment Analysis and N-Grams
Live Lecture
·Understanding Query Performance
·Controlling Job Execution Plan
·Partitioning
·Bucketing
·Indexing Data
Live Lecture
·SerDes
·Data Transformation with Custom Scripts
·User-Defined Functions
·Parameterized Queries
Live Lecture
·What is Impala?
·How Impala Differs from Hive and Pig
·How Impala Differs from Relational Databases
·Limitations and Future Directions
·Using the Impala Shell
Live Lecture
·Basic Syntax
·Data Types
·Filtering, Sorting, and Limiting Results
·Joining and Grouping Data
·Improving Impala Performance
Live Lecture
·Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
·Which to Choose?
Fees
Offline Training @ Vadodara
Classroom Based Training
Practical Based Training
No Cost EMI Option
5500050000
Online Training preferred
Live Virtual Classroom Training
1:1 Doubt Resolution Sessions
Recorded Live Lectures*
Flexible Schedule
4500040000
Corporate Training
Customized Learning
Onsite Based Corporate Training
Online Corporate Training
Certified Corporate Training
Certification
Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.
