Best Hadoop Training Institute in Chennai
Big data is making its presence now, and Hadoop is a big data technology that lets distributed storage and computing of data. The businesses nowadays are keener on customers. Customer care relates to personalized service while dealing with various modes of consumer interaction. Hadoop fixes difficult challenges faced by companies. It also lessens the drawbacks of conventional data approaches and is gaining momentum in big data technology. Enroll in the Hadoop training in Chennai and get to know the importance of this solution for big data. Aimore Technologies concentrates on career-oriented training so that you have the confidence to attend the interviews.
Overview of Hadoop
Hadoop offers a powerful and affordable data storage system. Hadoop with its entire ecosystem is a solution for significant data concerns, Several components of Hadoop ecosystem including MapReduce, TEZ, etc. offer support for big data analytics, Companies utilize Hadoop for big data crunching, Learn about this powerful concept from the best Hadoop training institute in Chennai.
Advantages of Hadoop
- Hadoop is a storage platform and is thoroughly scalable. It can conveniently store and distribute massive databases at a specific instance on servers that could be functioned in parallel.
- Hadoop is very affordable compared to conventional database management systems.
- Hadoop controls data via clusters, thus offering a unique storage method according to distributed file systems. Hadoop’s striking feature of mapping data on the clusters facilitates quick data processing.
- Hadoop lets companies access and process data in a very simple way to produce the values needed by the company. It offers companies with the tools to gain valuable insights from several types of data sources that are functioning in parallel.
- Fault tolerance is one of the significant highlights of Hadoop. This attribute is offered by replicating the data to another node in the cluster. So when there is a failure, the data from the replicated node can be utilized, thereby adhering to data consistency.
Parts of Hadoop
- Hadoop Distributed File System: Generally called as HDFS, it is a distributed file system that is compatible with massive scale bandwidth.
- MapReduce: A software framework that is used for processing big data
- YARN: It is a technology applied for controlling and scheduling Hadoop’s resources in Hadoop infrastructure.
- Libraries: Assists other models to function with Hadoop
Career scope of Hadoop
When you have skills including Hadoop, etc. you can get into promising big data jobs. The best means to kickstart your career in big data is taking up Hadoop developer or administrator training. As per the job profile you require for yourself, you can opt for the right training for you.
Big data applications and demand for skilled, trained personnel demonstrates momentous growth. Besides, the job scope of Hadoop is ever-growing since data continues to increase tremendously and is utilized by most devices today. Hadoop also serves as the most popular suite for managing big data sets. Moreover, Hadoop is excellent at handling petabytes of big data.
It is suggested to join Big Data Hadoop training in Chennai and gain practical hands-on experience. Theoretical knowledge is not sufficient to dive into Big Data. The companies may pay a handsome pay for the right candidate. So you can quickly get into the Hadoop job with an excellent salary if you could prove your worth. Big Data Hadoop job needs more concentration, and you should take care of the enormous sum of data. So enroll in the best Hadoop training institute in Chennai and get trained by proficient trainers.
Job profiles for Hadoop professionals after Big data Hadoop training in Chennai
- Hadoop Architect
- Hadoop Developer
- Hadoop Administrator
- Hadoop Analysts
- Hadoop Scientist
- Hadoop Engineer
Prerequisites for Hadoop classes in Chennai
Fair knowledge of Java, Linux, and big data
Who can attend Big Data Hadoop training in Chennai?
If you are enthusiastic towards Big Data Hadoop, then this course will take your career to the next level. However, if you belong to a Science background and possess excellent mathematical skills, then big data could be a great career option.
- Software Developers,
- Project Managers
- Software Architects
- ETL and Data Warehousing Professionals
- Testing professionals
- Analytics & Business Intelligence Professionals
- Senior IT Professionals
- Mainframe professionals
- Graduates inclined to build a career in the big data field
- Cloudera Hadoop Certification
- Cloudera Certified Professional – Data Scientist (CCP DS)
- Cloudera Certified Administrator for Hadoop (CCAH)
- Cloudera Certified Hadoop Developer (CCDH)
MapR Hadoop Certification
- MapR Certified Hadoop Developer (MCHD)
- MapR Certified Hadoop Administrator (MCHA)
- MapR Certified HBase Developer (MCHBD)
Hortonworks Hadoop Certification
- Hortonworks Certified Apache Hadoop Developer (HCAHD)
- Hortonwoks Certified Apache Hadoop Administrator (HCAHA)
Hadoop training in Chennai Syllabus
- Introduction to Big Data & Hadoop Fundamentals
- Dimensions of Big data
- Type of Data generation
- Apache ecosystem & its projects
- Hadoop distributors
- HDFS core concepts
- Modes of Hadoop employment
- HDFS Flow architecture
- HDFS MrV1 vs. MrV2 architecture
- Types of Data compression techniques
- Rack topology
- HDFS utility commands
- Min h/w requirements for a cluster & property files changes
Module 2 (Duration :03:00:00)
Goal : In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner &Partitioner and Demos on MapReduce using different data sets.
Objectives – Upon completing this Module, you should be able to understand MapReduce involves processing jobs using the batch processing technique.
- MapReduce can be done using Java programming.
- Hadoop provides with Hadoop-examples jar file which is normally used by administrators and programmers to perform testing of the MapReduce applications.
- MapReduce contains steps like splitting, mapping, combining, reducing, and output.
Introduction to MapReduce
- MapReduce Design flow
- MapReduce Program (Job) execution
- Types of Input formats & Output Formats
- MapReduce Datatypes
- Performance tuning of MapReduce jobs
- Counters techniques
Module 3 (Duration :03:00:00)
Goal : This module will help you in understanding Hive concepts, Hive Data types, Loading and Querying Data in Hive, running hive scripts and Hive UDF.
Objectives – Upon completing this Module, you should be able to understand Hive is a system for managing and querying unstructured data into a structured format.
- The various components of Hive architecture are metastore, driver, execution engine, and so on.
- Metastore is a component that stores the system catalog and metadata about tables, columns, partitions, and so on.
- Hive installation starts with locating the latest version of tar file and downloading it in Ubuntu system using the wget command.
- While programming in Hive, use the show tables command to display the total number of tables.
Introduction to Hive & features
- Hive architecture flow
- Types of hive tables flow
- DML/DDL commands explanation
- Partitioning logic
- Bucketing logic
- Hive script execution in shell & HUE
Module 4 (Duration :03:00:00)
Goal : In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.
Objectives – Upon completing this Module, you should be able to understand Pig is a high-level data flow scripting language and has two major components: Runtime engine and Pig Latin language.
- Pig runs in two execution modes: Local mode and MapReduce mode. Pig script can be written in two modes: Interactive mode and Batch mode.
- Pig engine can be installed by downloading the mirror web link from the website: pig.apache.org.
- Introduction to Pig concepts
- Pig modes of execution/storage concepts
- Pig program logics explanation
- Pig basic commands
- Pig script execution in shell/HUE
Module 5 (Duration :03:00:00)
Goal : This module will cover Advanced HBase concepts. We will see demos on Bulk Loading, Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.
Objectives – Upon completing this Module, you should be able to understand HBasehas two types of Nodes—Master and RegionServer. Only one Master node runs at a time. But there can be multiple RegionServersat a time.
- The data model of Hbasecomprises tables that are sorted by rows. The column families should be defined at the time of table creation.
- There are eight steps that should be followed for installation of HBase.
- Some of the commands related to HBaseshell are create, drop, list, count, get, and scan.
- Introduction to Hbase concepts
- Introdcution to NoSQL/CAP theorem concepts
- Hbase design/architecture flow
- Hbase table commands
- Hive + Hbase integration module/jars deployment
- Hbase execution in shell/HUE
Module 6 (Duration :02:00:00)
Goal : Sqoop is an Apache Hadoop Eco-system project whose responsibility is to import or export operations across relational databases. Some reasons to use Sqoop are as follows:
- SQL servers are deployed worldwide
- Nightly processing is done on SQL servers
- Allows to move certain part of data from traditional SQL DB to Hadoop
- Transferring data using script is inefficient and time-consuming
- To handle large data through Ecosystem
- To bring processed data from Hadoop to the applications
Objectives – Upon completing this Module, you should be able to understand Sqoop is a tool designed to transfer data between Hadoop and RDBs including MySQL, MS SQL, Postgre SQL, MongoDB, etc.
- Sqoop allows the import data from an RDB, such as SQL, MySQL or Oracle into HDFS.
- Introduction to Sqoop concepts
- Sqoop internal design/architecture
- Sqoop Import statements concepts
- Sqoop Export Statements concepts
- Quest Data connectors flow
- Incremental updating concepts
- Creating a database in MySQL for importing to HDFS
- Sqoop commands execution in shell/HUE
Module 7 (Duration :02:00:00)
Goal : Apache Flume is a distributed data collection service that gets the flow of data from their source and aggregates them to where they need to be processed.
Objectives – Upon completing this Module, you should be able to understand Apache Flume is a distributed data collection service that gets the flow of data from their source and aggregates the data to sink.
- Flume provides a reliable and scalable agent mode to ingest data into HDFS.
- Introduction to Flume & features
- Flume topology & core concepts
- Property file parameters logic
Module 8 (Duration :02:00:00)
Goal : Hue is a web front end offered by the ClouderaVM to Apache Hadoop.
Objectives – Upon completing this Module, you should be able to understand how to use hue for hive,pig,oozie.
- Introduction to Hue design
- Hue architecture flow/UI interface
Module 9 (Duration :02:00:00)
Goal : Following are the goals of ZooKeeper:
- Serialization ensures avoidance of delay in reading or write operations.
- Reliability persists when an update is applied by a user in the cluster.
- Atomicity does not allow partial results. Any user update can either succeed or fail.
- Simple Application Programming Interface or API provides an interface for development and implementation.
Objectives – Upon completing this Module, you should be able to understand ZooKeeper provides a simple and high-performance kernel for building more complex clients.
- ZooKeeper has three basic entities—Leader, Follower, and Observer.
- Watch is used to get the notification of all followers and observers to the leaders.
- Introduction to zookeeper concepts
- Zookeeper principles & usage in Hadoop framework
- Basics of Zookeeper
Module 10 (Duration :05:00:00)
Explain different configurations of the Hadoop cluster
- Identify different parameters for performance monitoring and performance tuning
- Explain configuration of security parameters in Hadoop.
Objectives – Upon completing this Module, you should be able to understand Hadoop can be optimized based on the infrastructure and available resources.
- Hadoop is an open-source application and the support provided for complicated optimization is less.
- Optimization is performed through xml files.
- Logs are the best medium through which an administrator can understand a problem and troubleshoot it accordingly.
- Hadoop relies on the Kerberos based security mechanism.
- Principles of Hadoop administration & its importance
- Hadoop admin commands explanation
- Balancer concepts
- Rolling upgrade mechanism explanation