Get Best Big Data Hadoop Training with Certified Trainers.Technology is developing, and there is no doubt that there is immense data to deal with. Big data Hadoop training has made enormous data processing easier and faster. Hence, it is essential for IT world experts to get trained in Big Data Hadoop to make vital contributions.
Hadoop Course Overview
Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka’s Cloud Lab.
- 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 Field
Big Data is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take benefit of these opportunities, you need a structured training with the latest curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real world big data projects using different Big Data and Hadoop tools as a part of solution strategy.
Additionally, you need the guidance of a Hadoop expert who is currently working in the industry on real world Big Data projects and troubleshooting day to day challenges while implementing them.
- Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes
- McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
- Average Salary of Big Data Hadoop Developers is $97k
There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, Edureka offers a complimentary self-paced course on “Java essentials for Hadoop” when you enroll for the Big Data and Hadoop Course.
Hadoop Course Content
- 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
Goal: In this module, you will understand the 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.
- MapReduce Design flow
- MapReduce Program (Job) execution
- Types of Input formats & Output Formats
- MapReduce Datatypes
- Performance tuning of MapReduce jobs
- Counters techniques
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 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.
- Hive architecture flow
- Types of hive tables flow
- DML/DDL commands explanation
- Partitioning logic
- Bucketing logic
- Hive script execution in shell & HUE
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
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.
- 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
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 a certain part of data from traditional SQL DB to Hadoop
- Transferring data using the 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
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
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
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
Explain different configurations of the Hadoop cluster
- Identify different parameters for performance monitoring and performance tuning
- Explain the 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
Frequently asked questions
The trainer will give Server Access to the course seekers, and we make sure you acquire practical hands-on training by providing you with every utility that is needed for your understanding of the course.
In case you are not able to attend any lecture, you can view the recorded session of the class in Icronix . To make things better for you, we also provide the facility to attend the missed session in any other live batch.
The trainer is a certified consultant and has significant amount of experience in working with the technology.
Yes, we accept payments in two installments.
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within first 2 sessions of the training. Please make a note that refunds will be processed within 30 days of prior request.