Big Data Online Course

Learn Big Data Analytics courses using Hadoop and Apache Spark from India's top-ranked Big Data training and placement institute, which offers award-winning faculty, real-world projects, and extensive job placement assistance, all of which are designed to help you become a Big Data Engineer.

The most in-depth online Big Data Analytics courses using SQL, NoSQL, Hadoop, Spark, and Cloud computing. Attend this Big Data Hadoop Certification Training Course in our Classroom or Online Training with an Instructor.

Big Data Course Description

Needintech's Big Data Course in Chennai is taught by Big Data Hadoop industry experts and covers everything you need to know about Big Data Hadoop course content tools like MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume, Sqoop HDFS, and YARN.

Mock Interviews

Needintech's mock interviews provide a platform for you to prepare for, practise for, and experience a real-life job interview. You will have an advantage over your colleagues if you familiarise yourself with the interview environment beforehand in a comfortable and stress-free environment.

Have Questions? Ask our Experts to Assist with Course Selection.

7010687183

Course Objetives
  • Hadoop and YARN fundamentals and application development
  • Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX, and MLlib writing Spark applications HDFS, MapReduce, Hive, Pig, Sqoop, Flume, and ZooKeeper.
  • Utilizing Avro data formats.
  • Real-world project practise with Hadoop and Apache Spark.
  • Be prepared to pass Big Data Hadoop Certification.
  • System Administrators and Programming Developers.
  • Working professionals with relevant experience and Project Managers.
  • Big Data Hadoop Developers interested in learning about other industries such as testing, analytics, and administration.
  • Professionals in the mainframe, architecture, and testing.
  • Professionals in Business Intelligence, Data Warehousing, and Analytics.
  • Graduates and undergraduates who want to learn about Big Data.
  • There are no prerequisites for enrolling in this Big Data course and mastering the technology. However, knowledge of UNIX, SQL, and Java is required to learn Big Data Hadoop. At Needintech in Chennai, we include free Linux and Java training with our Big Data certification course to help you brush up on the necessary skills and get started on the technology learning path.

We offer Big data Live Online Training or Big data Classroom Training sessions. Any of these training options are available to you.

  • Needintech actively seeks to place all learners who have successfully completed the training. We have exclusive partnerships with over 80 top MNCs from around the world for this. This allows you to work for companies like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among others. We can also assist you with job interview and résumé preparation.

Get Training Quote
Syllabus of Big Data Hadoop Certification Course
Module 1: Introduction to Big Data Hadoop Certification
  • High Availability
  • Scaling
  • Advantages and Challenges

More

Module 2: Introduction to Big Data

  • What is Big data
  • Big Data opportunities,Challenges
  • Characteristics of Big data

Module 3: Introduction to Big Data Hadoop Certification

  • Big Data Hadoop Certification Distributed File System
  • Comparing Big Data Hadoop Certification & SQL
  • Industries using Big Data Hadoop Certification
  • Data Locality
  • Big Data Hadoop Certification Architecture
  • Map Reduce & HDFS
  • Using the Big Data Hadoop Certification single node image (Clone)

Module 4: Big Data Hadoop Certification Distributed File System (HDFS)

  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Big Data Hadoop Certification DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read,File Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
  • How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
  • FSCK Utility. (Block report)
  • How to override default configuration at system level and Programming level
  • HDFS Federation
  • ZOOKEEPER Leader Election Algorithm
  • Exercise and small use case on HDFS

Module 5: Map Reduce

  • Map Reduce Functional Programming Basics
  • Map and Reduce Basics
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
  • Types of Schedulers and Counters
  • Comparisons between Old and New API at code and Architecture Level
  • Getting the data from RDBMS into HDFS using Custom data types
  • Distributed Cache and Big Data Hadoop Certification Streaming (Python, Ruby and R)
  • YARN
  • Sequential Files and Map Files
  • Enabling Compression Codec’s
  • Map side Join with distributed Cache
  • Types of I/O Formats: Multiple outputs, NLINEinputformat
  • Handling small files using CombineFileInputFormat

Module 6: Map Reduce Programming – Java Programming

  • Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
  • Sorting files using Big Data Hadoop Certification Configuration API discussion
  • Emulating “grep” for searching inside a file in Big Data Hadoop Certification
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion,Split API discussion
  • Custom Data type creation in Big Data Hadoop Certification

Module 7: NOSQL

  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail
  • Columnar Databases in Detail (HBASE and CASSANDRA)
  • TTL, Bloom Filters and Compensation

Module 8: HBase

  • HBase Installation, Concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • Master & Region Servers
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
  • Catalog Tables
  • Block Cache and sharding
  • SPLITS
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys)
  • Java API’s and Rest Interface
  • Client Side Buffering and Process 1 million records using Client side Buffering
  • HBase Counters
  • Enabling Replication and HBase RAW Scans
  • HBase Filters
  • Bulk Loading and Co processors (Endpoints and Observers with programs)
  • Real world use case consisting of HDFS,MR and HBASE

Module 9: Hive

  • Hive Installation, Introduction and Architecture
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store, Hive QL
  • OLTP vs. OLAP
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • User Defined Functions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
  • Dynamic Partition
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Bucketing and Sorted Bucketing with Dynamic partition
  • RC File
  • INDEXES and VIEWS
  • MAPSIDE JOINS
  • Compression on hive tables and Migrating Hive tables
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE
  • Log Analysis on Hive
  • Access HBASE tables using Hive
  • Hands on Exercises

Module 10: Pig

  • Pig Installation
  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on read
  • Primitive data types and complex data types
  • Tuple schema, BAG Schema and MAP Schema
  • Loading and Storing
  • Filtering, Grouping and Joining
  • Debugging commands (Illustrate and Explain)
  • Validations,Type casting in PIG
  • Working with Functions
  • User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail
  • SPLITS and Multiquery execution
  • Error Handling, FLATTEN and ORDER BY
  • Parameter Substitution
  • Nested For Each
  • User Defined Functions, Dynamic Invokers and Macros
  • How to access HBASE using PIG, Load and Write JSON DATA using PIG
  • Piggy Bank
  • Hands on Exercises

Module 11: SQOOP

  • Sqoop Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises

Module 12: HCatalog

  • HCatalog Installation
  • Introduction to HCatalog
  • About Hcatalog with PIG,HIVE and MR
  • Hands on Exercises

Module 13: Flume

  • Flume Installation
  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG

Module 14: More Ecosystems

  • HUE.(Hortonworks and Cloudera)

Module 15: Oozie

  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
  • Zoo Keeper
  • HBASE Integration with HIVE and PIG
  • Phoenix
  • Proof of concept (POC)

Module 16: SPARK

  • Spark Overview
  • Linking with Spark, Initializing Spark
  • Using the Shell
  • Resilient Distributed Datasets (RDDs)
  • Parallelized Collections
  • External Datasets
  • RDD Operations
  • Basics, Passing Functions to Spark
  • Working with Key-Value Pairs
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Removing Data
  • Shared Variables
  • Broadcast Variables
  • Accumulators
  • Deploying to a Cluster
  • Unit Testing
  • Migrating from pre-1.0 Versions of Spark
  • Where to Go from Here
 
0

Students Enrolled

0

Unlimited Access

0

24/7 Learning Assistants

0

Last Year Placed Students

×