The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data industry has gained significant growth in recent years and recent surveys have estimated that the Big Data market is more than a $50 billion industry. Gartner survey has confirmed that 64% companies have invested in Big Data in 2013 and the number keeps increasing every year. With the challenges in handling and arriving at meaningful insights from Bigdata, opportunities are boundless for everyone who wants to get into Big data Hadoop ecosystem. Software Professionals working in outdated technologies, JAVA Professionals, Analytics Professionals, ETL Professionals, Data warehousing Professionals, Testing Professionals, Project Managers can undergo our Hadoop training in Chennai and make a career shift. Our Big Data Training in Chennai will give hands-on experience to you to meet the demands of industry needs.
- 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 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:
- 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)
- 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. Topics:
- 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)
- 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. Topics:
- 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)
- 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. Topics:
- 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)
- 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
- Sqoop allows the import data from an RDB, such as SQL, MySQL or Oracle into HDFS. Topics:
- 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)
- Flume provides a reliable and scalable agent mode to ingest data into HDFS. Topics:
- Introduction to Flume & features
- Flume topology & core concepts
- Property file parameters logic Module 8 (Duration :02:00:00)
- Introduction to Hue design
- Hue architecture flow/UI interface Module 9 (Duration :02:00:00)
- 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.
- 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:
- Introduction to zookeeper concepts
- Zookeeper principles & usage in Hadoop framework
- Basics of Zookeeper Module 10 (Duration :05:00:00)
- Identify different parameters for performance monitoring and performance tuning
- Explain configuration of security parameters in Hadoop.
- 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:
- Principles of Hadoop administration & its importance
- Hadoop admin commands explanation
- Balancer concepts
- Rolling upgrade mechanism explanation
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.
Introduction to MapReduce
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.
Introduction to Hive & features
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.
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.
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:
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.
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.
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:
Goal : Following are the goals of ZooKeeper:
Objectives – Upon completing this Module, you should be able to understand ZooKeeper provides a simple and high-performance kernel for building more complex clients.
Goal:Explain different configurations of the Hadoop cluster
Objectives – Upon completing this Module, you should be able to understand Hadoop can be optimized based on the infrastructure and available resources.
- Best institution for Hadoop Training in Chennai with working professionals
- Learn by working on real-time Hadoop projects
- Flexible Timings – Weekday, Weekend & Fast-track
- Tips and discussion to build your technical interview skills
- Excel your knowledge with Affordable price in ALCE Complimentary Training on Core JAVA Hadoop Experts from industry with ample teaching Experience take Hadoop training in Chennai at ALCE Practical Training with Many Real time projects and Case studies Big Data Hadoop Training enables you to expertise the Hadoop framework concepts. Free Cloudera Certification Guidance as part of the Course Rated as Best Hadoop Training Center in Chennai by Professionals and Industry Experts! Master the tricks of data and analytics trade by pursuing a Big Data Certification.
Storage and Processing speed. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that's a key consideration.
Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.
- Fully hands-on training
- Learn by working on real-time projects
- Flexible Timings – Weekday, Weekend & Fast-track
- Certification guidance
- Interview preparation
- Tips and discussion to build your technical interview skills
- Excel your knowledge with Affordable price in ALCE
- More than 7+ Years of experience in Hadoop 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 Hadoop projects in their Industries.
- Are working professionals working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, WIPRO.
- 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.
- Classroom training
- One to One training
- Fast track training
- Customized training
No 147 1st Floor,NJK Avenue,