Big Data Hadoop Classes in Bangalore,Job Oriented Modules Added

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Big Data Hadoop Training in Bangalore Marathahalli BTM

Hadoop is an Apache project to store & process Big Data. Hadoop stores large chunk of data called Big Data in a distributed & fault tolerant manner over commodity hardware. After storing, Hadoop tools are used to perform data processing over HDFS (Hadoop Distributed File System).

As companies over time have realised the benefits of Big Data Analytics, there is a huge demand for Big Data & Hadoop professionals. Companies are in search of Big data & Hadoop professionals with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop& Flume.

Course objectives of Big Data Hadoop training

Big Data Hadoop Training in Bangalore is designed by our industry experts to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers the following:

  • Complete knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) and MapReduce
  • Comprehensive knowledge of various tools that is a part of Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
  • Capability to ingest data in HDFS using Sqoop and Flume, and analyze those large datasets stored in the HDFS.
  • The exposure to many real world industry-based projects which will be executed in CloudLab
  • Projects which are very diverse i.e. different from each other covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce.

How we standout among other training institutes ?

@ Apponix @ Other institutes
Course fees Very competitive and affordable. Most of the institutes provide at less fees but compromise with the quality of the training
Placement assistance We have a dedicated HR team to help students in placement and tied with leading job portals. Most of the institutes may make false promises
Dedicated HR team Yes None
Working Professionals as trainers Yes Very Few
Trainers Experience Min 7+ Years experience Most of the institutes hire full time trainers with very less real time experience
Student Web Portal We have a dedicated students portal where you will find course materials and technical questions hr questions prepared by it professionals None
Class Room Infrastructure All classrooms are Air conditioned to make sure our students feel comfortable Very few institutes
Reference Pay We pay Rs 1000 for every student you refer. None
Pay After Job Yes, for most of the courses students can pay part of the fees after they get a job None, You need to pay full fess before joining
Instalments Yes its very flexible, you can pay the fees in installments, we understand the financial situation of the students Very few institutes
Lab Infrastructure For most of the courses each student is given with laptop or desktop throughout the course None
Who are our trainers?
IT consultants,IT project managers, Solutions Architects, Technical Leads Most of the institutes hire full time trainers with very little experience
Student’s Ratings 5 ***** ratings from more than 4000 students Mixed
Trust & Credibility  Very High Moderate.
Fees Negotiable? Definitely yes we understand the financial situation of each student Very few
Refer and Win We run refer and win a holiday every 6 months, All referrers will have chance to win holiday to goa, please refer this link None

Big Data engineer Job Responsibilities

  1. Develop solutions/design ideas.
  2. Identify design ideas to enable the software to meet the acceptance and success criteria.
  3. Working with architects/BA to build data components on the Big Data environment.
  4. Developing databases using SSIS packages, T-SQL, MSSQL, and MySQL scripts
  5. Designing and implementing data models and data integration
  6. Designing, building, and maintaining the business’s ETL pipeline and data warehouse.
  7. Data modeling and query performance tuning on SQL Server, MySQL, Redshift, Postgres or similar platforms.
  8. Participate in and build tools to diagnose and fix complex distributed systems handling petabytes of data & drive opportunities to automate infrastructure, deployments, and observability of data services.
  9. Testing, monitoring, administering, optimizing and operating multiple Hadoop / Spark clusters across cloud providers – AWS, GCP on premise data centers, primarily in Python, Java and Scala.
  10. Investigate emerging technologies in Hadoop ecosystem that relate to needs and implement those technologies.
  11. Partner with Hadoop developers in building best practices for Data Warehouse and analytics environment.
  12. Share an on-call rotation and handle service incidents.
  13. Working with Big Data tools and building high performance, high throughput, and distributed data pipeline and big data platform with Hadoop, Spark, Kakfa, Hive, and Presto
  14. Designing, developing, user interface, technology integration, and site architecture management
  15. Address data-related problems in regards to systems integration, compatibility, and multiple-platform integration
  16. Develop and promote data management methodologies and standards
  17. Identifying inefficiencies and gaps in current architecture and identify on road map
  18. Test prototypes and oversee handover to operational teams
  19. Troubleshoot and correct problems discovered in production and non-production platforms
  20. Mentor and/or provide leadership to junior members of the team.

Who should attend Big Data Hadoop training Course?

It is best suited for:

  • Engineering Fresher
  • Software Developers, Project Managers
  • Software Architects
  • ETL and Data Warehousing Professionals
  • Data Engineers
  • Data Analysts & Business Intelligence Professionals
  • DBAs and DB professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Graduates looking to build a career in Big Data domain.

How will Big Data Hadoop Training from Apponix help your career?

These predictions will help you in understanding the growth of Big Data:

  • Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 43%
  • McKinsey predicts that by 2018 there will be a shortage of 1.6M data experts
  • Average Salary of Big Data Hadoop Developers is $96k
  • All our Big data trainers have min 7 years industry experience
  • Our Big Data training course is designed to meet present & future industry requirements
  • We provide an excellent lab facility with live projects
  • All our Big data trainers have min 7 years industry experience
  • Our Big Data training course is designed to meet present & future industry requirements
  • We provide an excellent lab facility for Hadoop Training in Bangalore with live projects

What are the skills that you will be learning with our Big Data Hadoop Training?

Big Data Hadoop Training will help you to become a Big Data professional. It will offer you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. During Big Data Hadoop course you will be trained by our trainers to:

  • Understand the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand to work with Hadoop storage & resource management.
  • Understand MapReduce Framework
  • Implement complex business solution using MapReduce
  • Learn data ingestion techniques using Sqoop and Flume
  • Perform ETL operations and data analytics using Pig and Hive
  • Implementing, partitioning, bucketing and Indexing in Hive
  • Understand HBase, i.e a NoSQL Database in Hadoop, HBase Architecture & Mechanisms
  • Integrate HBase with Hive
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Understand Apache Spark and its Ecosystem
  • Learn to work with RDD in Apache Spark
  • Work on real world Big Data Analytics Project
  • Work on a real-time Hadoop cluster

What are the pre-requisites for Hadoop Training Course?

There are no such prerequisites required for Big Data Hadoop Course. Prior knowledge of Core Java and SQL will be helpful but not mandatory. Further, to brush up your skills, we will offer you a complimentary self-paced course on “Java essentials for Hadoop” when you enroll for the Big Data Hadoop Course at Apponix.We offer the best and quality Hadoop training in Bangalore

Hadoop (1.x / 2.x) Course Content

  1. Introduction to Data and System
  2. Types of Data
  3. Traditional way of dealing large data and its problems
  4. Types of Systems & Scaling
  5. What is Big Data
  6. Challenges in Big Data
  7. Challenges in Traditional Application
  8. New Requirements
  9. What is Hadoop? Why Hadoop?
  10. Brief history of Hadoop
  11. Features of Hadoop
  12. Hadoop and RDBMS
  13. Hadoop Ecosystem’s overview
  1. Installation in detail
  2. Creating Ubuntu image in VMware
  3. Downloading Hadoop
  4. Installing SSH
  5. Configuring Hadoop, HDFS & MapReduce
  6. Download, Installation & Configuration Hive
  7. Download, Installation & Configuration Pig
  8. Download, Installation & Configuration Sqoop
  9. Download, Installation & Configuration Hive
  10. Configuring Hadoop in Different Modes
  1. File System - Concepts
  2. Blocks
  3. Replication Factor
  4. Version File
  5. Safe mode
  6. Namespace IDs
  7. Purpose of Name Node
  8. Purpose of Data Node
  9. Purpose of Secondary Name Node
  10. Purpose of Job Tracker
  11. Purpose of Task Tracker
  12. HDFS Shell Commands – copy, delete, create directories etc.
  13. Reading and Writing in HDFS
  14. Difference of Unix Commands and HDFS commands
  15. Hadoop Admin Commands
  16. Hands on exercise with Unix and HDFS commands
  17. Read / Write in HDFS – Internal Process between Client, NameNode & DataNodes
  18. Accessing HDFS using Java API
  19. Various Ways of Accessing HDFS
  20. Understanding HDFS Java classes and methods
  21. Commissioning / DeCommissioning DataNode
  22. Balancer
  23. Replication Policy
  24. Network Distance / Topology Script
  1. About MapReduce
  2. Understanding block and input splits
  3. MapReduce Data types
  4. Understanding Writable
  5. Data Flow in MapReduce Application
  6. Understanding MapReduce problem on datasets
  7. MapReduce and Functional Programming
  8. Writing MapReduce Application
  9. Understanding Mapper function
  10. Understanding Reducer Function
  11. Understanding Driver
  12. Usage of Combiner
  13. Usage of Distributed Cache
  14. Passing the parameters to mapper and reducer
  15. Analysing the Results
  16. Log files
  17. Input Formats and Output Formats
  18. Counters, Skipping Bad and unwanted Records
  19. Writing Join’s in MapReduce with 2 Input files. Join Types
  20. Execute MapReduce Job - Insights
  21. Exercise’s on MapReduce
  1. Hive concepts
  2. Hive architecture
  3. Install and configure hive on cluster
  4. Different type of tables in hive
  5. Hive library functions
  6. Buckets
  7. Partitions
  8. Joins in hive
  9. Inner joins
  10. Outer Joins
  11. Hive UDF
  12. Hive Query Language
  1. Pig basics
  2. Install and configure PIG on a cluster
  3. PIG Library functions
  4. Pig Vs Hive
  5. Write sample Pig Latin scripts
  6. Modes of running PIG
  7. Running in Grunt shell
  8. Running as Java program
  9. PIG UDFs
  1. Install and configure Sqoop on cluster
  2. Connecting to RDBMS
  3. Installing Mysql
  4. Import data from Mysql to hive
  5. Export data to Mysql
  6. Internal mechanism of import/export
  1. HBase concepts
  2. HBase architecture
  3. Region server architecture
  4. File storage architecture
  5. HBase basics
  6. Column access
  7. Scans
  8. HBase use cases
  9. Install and configure HBase on a multi node cluster
  10. Create database, Develop and run sample applications
  11. Access data stored in HBase using Java API
  12. Map Reduce client to access the HBase data
  1. Resource Manager (RM)
  2. Node Manager (NM)
  3. Application Master (AM)

Typical Big Data Hadoop Administrator job responsibilities

 

  • The responsibilities of a Big data Hadoop administrator includes – deploying and manage a hadoop cluster.
  • Maintaining and Managing a hadoop cluster
  • Adding, updating and removing nodes using cluster monitoring tools
  • Usage of Ganglia Nagios
  • Configuring the NameNode high availability cluster and keeping a track.
  • Monitor and Fix the Hadoop cluster connectivity and its performance.
  • Management and analyze stored Hadoop log files.
  • File system management and monitoring and troubleshooting if any problems.
  • Preparing design or maintain documentation
  • Manage Hadoop big data users.
  • Administration of complete Hadoop infrastructure in prod & dev environment
  • DBA Responsibilities like data modeling, data design
  • Software installation and configuration and database backup and db recovery,
  • Manage Database connectivity and security.
  • Hadoop Cluster Monitoring and Troubleshooting
  • Monitoring the big data Hadoop Clusters
  • Securing a Hadoop Cluster Security Concepts
  • Configuring HDFS for Rack
  • Configuring HDFS for High Availability
  • Configuring the HDFS Service
  • Configuring  and check Hadoop Daemon Logss on regular basis
  • Configuring and manage the YARN Service
  • Running Computational Frameworks on YARN
  • Exploring YARN Applications through the Web UIs
  • Hadoop admin is also responsible for deciding the size of the hadoop cluster based on no of factors
  • Ensure that the hadoop cluster is up and running all the time.

Big Data Hadoop Training in Bangalore

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