What we do at Besant Technologies, Tambaram for Hadoop?
We always encourage the students to explore more on all the components; as a result, the candidates from every batch are able to deliver a PROOF OF CONCEPT. Below are some of the POCs done by our previous batch students
- A Web page added to HDFS web UI to upload the files to the Hadoop distributed file system.
- A complete English – English Dictionary application using MAP REDUCE program.
Whom Hadoop is suitable for?
Hadoop and Big Data related Technology is suitable for all the IT professionals who look forward to becoming Data Analyst / Data Scientist in the future and those who have a good passion for data handling techniques and to become industry experts on the same. Moreover, Hadoop can be pursued by Java as well as non- Java background professionals (including Mainframe, DWH, etc.)
Job Opportunity for Hadoop?
The demand for Hadoop skills never dies as this technology is like the reactant of a chain reaction where one leads to the growth or creation of another. As we are living in Internet era, we know how much of data is getting generated every day in every aspect of our life starting from the social media, e-commerce, banking, etc… So, it is an indispensable need to handle the data. A growing number of companies have begun to tap the technology to store and analyze petabytes of data such as weblogs, click stream data and social media content to gain better insights about their customers and their business. Hence there is a very huge demand for Hadoop professionals.
What is Hadoop?
Hadoop is a frame work written in Java language which allows us to deals with large set of data using a simple programming model. The two core components of Hadoop are, HDFS – Hadoop Distributed file system for storage. Map Reduce – A programming model to process the data. Using the Hadoop framework it is very easy to store and process the different variety and the huge volume of data… Initially, this open-source framework started with just two core components – HDFS and map-reduce. But later on, there are more than 15 components were integrated into the ecosystem.
Hadoop Training Key Features
Besant Technologies offers Hadoop Training in Chennai in more than 9+ branches with expert trainers. Here are the key features,
- 30+ Hours Course Duration
- 100% Job Oriented Training
- Industry Expert Faculties
- Free Demo Class Available
- Completed 500+ Batches
- Certification Guidance
Hadoop Training Batch Schedule
Here are the Hadoop Training Classes in Chennai Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your interest.
Hadoop Training Syllabus
Module 1 (Duration :06:00:00)
Introduction to Big Data & Hadoop Fundamentals Goal : In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works. Objectives – Upon completing this Module, you should be able to understand Big Data is a term applied to data sets that cannot be captured, managed, and processed within a tolerable elapsed and specified time frame by commonly used software tools.
- Big Data relies on volume, velocity, and variety with respect to processing.
- Data can be divided into three types—unstructured data, semi-structured data, and structured data.
- Big Data technology understands and navigates big data sources, analyzes unstructured data, and ingests data at a high speed.
- Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
Topics: Apache Hadoop
- 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)
MapReduce Framework 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.
Topics: 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)
Apache Hive 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 megastore, 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 the tar file and downloading it in the Ubuntu system using the wget command.
- While programming in Hive, use the show tables command to display the total number of tables.
Topics: 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)
Apache Pig 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 HBaseha’s 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 the installation of HBase.
- Some of the commands related to HBaseshell create, drop, list, count, get, and scan.
Topics: Apache Hbase GangBoard.com
- Introduction to Hbase concepts
- Introduction 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.
Topics: Apache Sqoop
- 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.
Topics: Apache Flume
- 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. Topics: Apache HUE
- 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.
Topics: Apache Zookeeper
- Introduction to zookeeper concepts
- Zookeeper principles & usage in Hadoop framework
- Basics of Zookeeper
Module 10 (Duration :05:00:00)
Goal: 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.
Topics: Administration concepts
- Principles of Hadoop administration & its importance
- Hadoop admin commands explanation
- Balancer concepts
- Rolling upgrade mechanism explanation
Besant Technologies offers 250+ IT training course in more than 20+ branches with 10+ years Experienced Expert level Trainers.
- Fully hands-on training
- 30+ hours course duration
- Industry expert faculties
- Completed 1500+ batches
- 100% job oriented training
- Certification guidance
- Own course materials
- Resume editing
- Interview preparation
- Affordable fees structure
Besant Technologies is the Legend in offering placement to the students. Please visit our Placed Students List on our website.
More than 2000+ students placed in last year. It has a dedicated placement portal which caters to the needs of the students during placements. Besant Technologies conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
- 92% percent placement record.
- 1000+ interviews organized.
Our trainers are more than 10+ years of experience in course relevant technologies. Trainers are expert level and fully up-to-date in the subjects they teach because they continue to spend time working on real-world industry applications. Trainers have experienced on multiple real-time projects in their industries.
- Are working professionals working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies, etc…
- Trained more than 2000+ students in a year.
- Strong theoretical & practical knowledge.
- Are certified professionals with high grade. Are well connected with hiring HRs in multinational companies.
No worries. Besant technologies assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
Besant Technologies provides many suitable modes of training to the students like
- Classroom training
- One to One training
- Fast track training
- Customized training
You will receive Besant Technologies globally recognized course completion certification.
Yes, Besant Technologies provides group discounts for its training programs. To get more details, visit our website and contact our support team via Call, Email, Live Chat option or drop a Quick Enquiry. Depending on the group size, we offer discounts as per the terms and conditions.
We accept all major kinds of payment options. Cash, Card (Master, Visa, and Maestro, etc), Net Banking and etc.
Hadoop Training Reviews
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Hadoop Trainer Profile & Placement
Our Hadoop Trainers
- More than 10 Years of experience in Hadoop® Technologies
- Has worked on multiple realtime Hadoop projects
- Working in a top MNC company in Chennai
- Trained 2000+ Students so far
- Strong Theoretical & Practical Knowledge
- Hadoop certified Professionals
Hadoop Placement Training in Chennai
- More than 2000+ students Trained
- 93% percent Placement Record
- 1100+ Interviews Organized
Besant Technologies Official Branches
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Tamil Nadu, India.
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