- AddressRavi Nagar Square, Nagpur.
- Emailinfo@grrasnagpur.in
- Contact No.+91 99759 98226
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What's App No.+91 99759 98226
What's App No.+91 99759 98226
As the world is growing Digital, which leads us to large datasets called Big data and for processing and storing this large datasets is a new challenge. For that, one should have skills to analysis the data so there is growing trend for Big data analytics and Hadoop professional who have a good understanding of structured, unstructured, complex data and have skills to use Hadoop Technology for storing and processing Big Data.
Hadoop is an open source, smooth and easy-to-use Apache tool designed to store data, runs application on clusters and is been written in JAVA. Big Data is collection of voluminous and complex data sets that cannot be processed using traditional computer technologies. In this course we learn the Hadoop ecosystem components such as HDFS, Pig, Map reduce, yarn, impala, Hbase, Apache spark etc. which helps in Big Data processing.
| Tracks | Regular Track | Full day (Fastrack) |
|---|---|---|
| Training Duration | 60 hours | 60 hours |
| Training Days | 30 days | 7 days |
- About MapReduce
- Why MapReduce?
- History of MapReduce
- MapReduce Use Cases
- Work Flow of MapReduce
- Traditional way Vs MapReduce way to analyze BigData
- Hadoop 2.x MapReduce Architecture
- Hadoop 2.x MapReduce Components
- MapReduce components
• Combiner
• Partitioner
• Reducer
- Work Flow of YARN framework
- Relation between Input Splits and HDFS Blocks
- MapReduce Practical and Troubleshooting
- About Hive
- History of Hive
- Use of Hive
- Hive Use Case
- Hive Vs Pig
- Hive Architecture and Components
- Metastore in Hive
- Limitations of Hive
- Traditional Database Vs Hive
- Hive Data Types and Data Models
- Hive Management
- Partitions and Buckets
- Hive Tables(Managed Tables and External Tables)
- Importing Data
- Querying Data
- Managing Outputs
- Hive Script
- HiveQL
- Joining Tables
- Dynamic Partitioning
- Custom Map/Reduce Scripts
- Hive Indexes and views Hive query optimizers
- Hive : User Defined Functions
- Hive Practical and Troubleshooting
- About Sqoop
- History of Sqoop
- Usage and Management of sqoop with RDBMS
- Sqoop Architecture
- Sqoop Commands
- Command to get data from RDBMS form HDFS
- Command to put data in RDBMS form HDFS
- Importance of sqoop with HDFS and RDBMS
- Sqoop Practical and Troubleshooting
- About Apache Spark
- History of Spark and Spark Versions/Releases
- Spark Architecture
- Spark Components
- Usage and Management of Spark with HDFS
- Spark Practical
- Spark Streaming
- Spark MLlib
- About Flume
- History of Flume
- Flume Architecture
- Flume Components
- Usage and Management of Flume
- Data Fetching from many resources in HDFS using Flume
- Flume Practical and Troubleshooting
- Command to start Hadoop cluster setup
- Command to stop Hadoop cluster setup
- Command to start individual component
- Command to stop individual component
- Command to put data in HDFS
- Command to get data from HDFS
- Command to create and delete file, directory in HDFS and etc.
- About Oozie
- History of Oozie
- Oozie Architecture
- Oozie Components
- Oozie Work Flow
- Scheduling with Oozie
- Oozie with Hive, HBase, Pig, Sqoop, Flume
- Oozie Practical and Troubleshooting
- About Zoopkeeper
- History of Zoopkeeper
- Zoopkeeper components
- Zoopkeeper Architecture
- Usage and Importance Zoopkeeper with Hadoop
- Management of Zoopkeeper
- Zoopkeeper Practical and Troubleshooting
- About Cloudera Manager
- History of Cloudera Manager
- Usage and Management of Cloudera Manager
- Usage and Management of each ecosystem tool with Cloudera manager.
- Introduction and Configuration
- Producer API
- Consumer API
- Stream API
- Connector API
- Topics and Logs
- Consumers and Producers
- Kafka as messaging system
- Kafka as a storage System
- Kafka for Stream Processing
- EC2
- EMR
- RDS & Redshift
- Lambda
- S3 storage
- Elastic Search
- Data Bricks (Azure
- Project 1: Deploying Hadoop multi node cluster and deploying application and integrated for managing big data challenge.
Technology used : Redhat Linux, Apache Hadoop, Cluster management with backend storage, python programming, Mysql BackEnd Database.
Project 2: Deployment of Apache hadoop cluster and Managing Distributed application and Jobs scheduling in Automation.
Technology used: Redhat Linux, Apache Hadoop, hive, pig, Sqoop, flume, Python programming, shell script
- This is the best job in Hadoop, big Data Engineer develop, maintain, test and evaluate big data solutions with in organisations. He builds large scale data processing systems
- Hadoop developers are basically software programmers but working in the Big data Hadoop domain. They are masters of computer procedural languages.
- Technical managers work with the departmental managers to ensure their team’s technological developments align with the company's goals.They are also known as information systems (CIS) managers
- A lead data engineer, will lead a team to architect a big data platform that is real time, stable and scalable to support data analytics, reporting data.
- Hadoop Administrator is responsible for ongoing administration of hadoop infrastructure, Aligning with the systems engineering team to propose and deploy new hardware and software environment required for Hadoop and to expand existing environments.
- Placement Assistance
- Live Project Assessment
- Lifetime Career Support
- Lifetime Training Membership (Candidate can join same course again for purpose of revision and update at free of cost at our any center in India or you can solve your query by online help)
- Hadoop Based Exam Scenario Preparation Included IN Training
