It is a comprehensive Hadoop Big Data training course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This is an industry recognized Big Data certification training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics. This Cloudera Hadoop training will prepare you to clear big data certification.
There is no pre-requisite to take this Big data training and to master Hadoop. But basics of UNIX, SQL and java would be good.At Intellipaat, we provide complimentary unix and Java course with our Big Data certification training to brush-up the required skills so that you are good on you Hadoop learning path.
|Proficiency||MapReduce,Spark, HBase||Cluster schedule, monitor, provision||Includes all components|
|Audience||Analytics,BI,ETL personnel, Coders,||Mainframe,QA personnel||Includes audience of both|
|Average Salaries||$100,000||$123,000||$ 172,000|
Project 1 – Working with MapReduce, Hive, Sqoop
Topics : As part of this Big data Hadoop certification training, you will undergo the project which involves working on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.
Project 2 – Work on MovieLens data for finding top records
Data – MovieLens dataset
Topics : In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves the following important components:
Project 3 – Hadoop YARN Project – End to End PoC
Topics : In this Big Data project you will work on a live Hadoop YARN project. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager. The salient features of this project include:
Project 4 – Partitioning Tables in Hive
Topics : This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways like:
This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.
Project 5 – Connecting Pentaho with Hadoop Ecosystem
Topics : This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. Some of the components of this project include the following:
Project 6 – Multi-node cluster setup
Topics : This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. The major components of this project involve:
You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.
Project 7 – Hadoop Testing using MR
Topics : In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. Some of the important aspects of this project include:
After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.
Project 8 – Hadoop Weblog Analytics
Data – Weblogs
Topics : This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The various modules of this project include:
The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.
Project 9 – Hadoop Maintenance
Topics : This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks like:
|Course Name||Location||Start Date|
|Big Data Hadoop Training||Bangalore||04 Mar||Know More|
|Big Data Hadoop Training||Melbourne||04 Mar||Know More|
|Big Data Hadoop Training||Chicago||04 Mar||Know More|
|Big Data Hadoop Training||Hyderabad||04 Mar||Know More|
|Big Data Hadoop Training||San Francisco||04 Mar||Know More|
|Big Data Hadoop Training||London||04 Mar||Know More|
|Big Data Hadoop Training||New York City||04 Mar||Know More|
What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x
Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster
What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,
Understanding Apache Pig, the features, various uses and learning to interact with Pig
The syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.
Various data types including nested and complex, processing data with Pig, grouped data iteration, practical exercise
Data set joining, data set splitting, various methods for data set combining, set operations, hands-on exercise
Understanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercises
Working with real data sets involving Walmart and Electronic Arts as case study
Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive
Understanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.
The various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.
Learning performance of query, data indexing, partitioning and bucketing
Deploying user defined functions for extending Hive
Deploying Hive for huge volumes of data sets and large amounts of querying
Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.
What is Impala?, How Impala Differs from Hive and Pig, How Impala Differs from Relational Databases, Limitations and Future Directions, Using the Impala Shell
Data Storage Overview, Creating Databases and Tables, Loading Data into Tables, HCatalog, Impala Metadata Caching
Partitioning Overview, Partitioning in Impala and Hive
Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression
What is Hbase, Where does it fits, What is NOSQL
What is Spark, Comparison between Spark and Hadoop, Components of Spark
Apache Spark- Introduction, Consistency, Availability, Partition, Unified Stack Spark, Spark Components, Scalding example, mahout, storm, graph
Explain python example, Show installing a spark, Explain driver program, Explaining spark context with example, Define weakly typed variable, Combine scala and java seamlessly, Explain concurrency and distribution., Explain what is trait, Explain higher order function with example, Define OFI scheduler, Advantages of Spark, Example of Lamda using spark, Explain Mapreduce with example
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
Putting it all together and Connecting Dots, Working with Large data sets, Steps involved in analyzing large data
How ETL tools work in Big data Industry, Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.
Configuration overview and important configuration file, Configuration parameters and values, HDFS parameters MapReduce parameters, Hadoop environment setup, ‘Include’ and ‘Exclude’ configuration files, Lab: MapReduce Performance Tuning
Namenode/Datanode directory structures and files, File system image and Edit log, The Checkpoint Procedure, Namenode failure and recovery procedure, Safe Mode, Metadata and Data backup, Potential problems and solutions / what to look for, Adding and removing nodes, Lab: MapReduce File system Recovery
Best practices of monitoring a cluster, Using logs and stack traces for monitoring and troubleshooting, Using open-source tools to monitor the cluster
How to schedule Jobs on the same cluster, FIFO Schedule, Fair Scheduler and its configuration
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
ZOOKEEPER Introduction, ZOOKEEPER use cases, ZOOKEEPER Services, ZOOKEEPER data Model, Znodes and its types, Znodes operations, Znodes watches, Znodes reads and writes, Consistency Guarantees, Cluster management, Leader Election, Distributed Exclusive Lock, Important points
Why Oozie?, Installing Oozie, Running an example, Oozie- workflow engine, Example M/R action, Word count example, Workflow application, Workflow submission, Workflow state transitions, Oozie job processing, Oozie security, Why Oozie security?, Job submission, Multi tenancy and scalability, Time line of Oozie job, Coordinator, Bundle, Layers of abstraction, Architecture, Use Case 1: time triggers, Use Case 2: data and time triggers, Use Case 3: rolling window
Overview of Apache Flume, Physically distributed Data sources, Changing structure of Data, Closer look, Anatomy of Flume, Core concepts, Event, Clients, Agents, Source, Channels, Sinks, Interceptors, Channel selector, Sink processor, Data ingest, Agent pipeline, Transactional data exchange, Routing and replicating, Why channels?, Use case- Log aggregation, Adding flume agent, Handling a server farm, Data volume per agent, Example describing a single node flume deployment
HUE introduction, HUE ecosystem, What is HUE?, HUE real world view, Advantages of HUE, How to upload data in File Browser?, View the content, Integrating users, Integrating HDFS, Fundamentals of HUE FRONTEND
IMPALA Overview: Goals, User view of Impala: Overview, User view of Impala: SQL, User view of Impala: Apache HBase, Impala architecture, Impala state store, Impala catalogue service, Query execution phases, Comparing Impala to Hive
Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing
Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.
Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.
Automation testing using the OOZIE, Data validation using the query surge tool.
Test plan for HDFS upgrade, Test automation and result
How to test install and configure
Cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques
Intellipaat is the pioneer of Hadoop training in India. As you know today the demand for Hadoop professionals far exceeds the supply. So it pays to be with the market leader like Intellipaat when it comes to learning Hadoop in order to command top salaries. As part of the training you will learn about the various components of Hadoop like MapReduce, HDFS, HBase, Hive, Pig, Sqoop, Flume, Oozie among others. You will get an in-depth understanding of the entire Hadoop framework for processing huge volumes of data in real world scenarios.
The Intellipaat training is the most comprehensive course, designed by industry experts keeping in mind the job scenario and corporate requirements. We also provide lifetime access to videos, course materials, 24/7 Support, and free course material upgrade. Hence it is a one-time investment.
Intellipaat basically offers the self-paced training and online instructor-led training. Apart from that we also provide corporate training for enterprises. All our trainers come with over 12 years of industry experience in relevant technologies and also they are subject matter experts working as consultants. You can check about the quality of our trainers in the sample videos provided.
If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers. The best part is that you can contact Intellipaat even after completion of training to get support and assistance. There is also no limit on the number of queries you can raise when it comes to doubt clearance and query resolution.
Yes, you can learn Hadoop without being from a software background. We provide complimentary courses in Java and Linux so that you can brush up on your programming skills. This will help you in learning Hadoop technologies better and faster.
The Intellipaat self-paced training is for people who want to learn at their own leisurely pace. As part of this program we provide you with one-on-one sessions, doubt clearance over email, 24/7 Live Support, 1yr of cloud access and lifetime LMS and upgrade to the latest version at no extra cost. The prices of self-paced training can be 75% lesser than online training. While studying should you face any unexpected challenges then we shall arrange a Virtual LIVE session with the trainer.
We provide you with the opportunity to work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various Hadoop components making you perfectly industry-ready. These projects could be in exciting and challenging fields like banking, insurance, retail, social networking, high technology and so on. The Intellipaat projects are equivalent to six months of relevant experience in the corporate world.
Yes, Intellipaat does provide you with placement assistance. We have tie-ups with 80+ organizations including Ericsson, Cisco, Cognizant, TCS, among others that are looking for Hadoop professionals and we would be happy to assist you with the process of preparing yourself for the interview and the job.
Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.
Upon successful completion of training you have to take a set of quizzes, complete the projects and upon review and on scoring over 60% marks in the qualifying quiz the official Intellipaat verified certificate is awarded.The Intellipaat Certification is a seal of approval and is highly recognized in 80+ corporations around the world including many in the Fortune 500 list of companies.
This training course is designed to help you clear both Cloudera Spark and Hadoop Developer Certification (CCA175) exam and Cloudera Certified Administrator for Apache Hadoop (CCAH) exam. The entire training course content is in line with these two certification programs and helps you clear these certification exams with ease and get the best jobs in the top MNCs.
As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Course Completion Certificate will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
Intellipaat courses come with lifetime free upgrade to latest version. It’s a lifetime investment in the skills you want to enhance
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