Big-Data Hadoop Training Institutes in Pune | CloudEra Certification courses in Pune


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MindScripts is the best Big-Data Hadoop Training Institute/Center in Pune providing complete courses including Cloudera, Hortonworks, HDFS, MapReduce, Pig, Hive, Sqoop, ZooKeeper. The course is designed keeping CloudEra Certification syllabus in mind.

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Big-Data Hadoop Training Institutes in Pune | CloudEra Certification courses in Pune

  1. 1. Hadoop Training Institutes in Pune Introduction:Grab the Excellent & Huge Job opportunities in the Big-Data Hadoop Market today with the help of Mindscripts Technologies. Mindscripts Technologies is the Best Big-Data Hadoop Training Institutes in Pune. Providing you a Complete Hadoop Course with Cloud Era Certification. As Hadoop Course is developed & work under Apache License. Big-data Hadoop is the Latest Technology in IT Market. The Main objective of this course is to provide the complete knowledge to the candidates or to the Cocorporate employee who are interested to make their career in Big-Data Hadoop course. Target & final Career objective is to complete the Hadoop course from mindscripts technologies, is to complete the Course & get the Job in Multinational companies for starting your career. For More Details you may Contact us on:-
  2. 2. Hotline no - 9595957557 Mobile no – 8805674210 Website:- Office Branch Address:MindScripts Technologies, 2nd Floor, Siddharth Hall, Near Ranka Jewellers, Behind HP Petrol Pump, Karve Rd, Pune 411004 MindScripts Technologies, C8, 2nd Floor, Sant Tukaram Complex , Pradhikaran, Above Savali Hotel, Opp Nigdi Bus Stand, Nigdi, Pune – 411044 Objective:• Get Complete Knowledge Of Big-Data with Live Projects at the end of the Course. • Complete Big-Data Hadoop Course with CloudEra Certification from Technical Experts. Eligibility:• Candidate should a Graduate before applying for this course. • He should have the knowledge of Java. And Java skills are mandatory or he should posses a past Experience in Software Development field. Course Overview:The Course consists of following modules which include both theoretical & Practical knowledge. Hadoop Hadoop Ecosystem Introduction Hadoop: Basic Concepts What is Hadoop? The Hadoop Distributed File System Hadoop Map Reduce Works HBase HBase concepts HBase architecture Region server architecture File storage architecture HBase basics
  3. 3. Anatomy of a Hadoop Cluster Column access Scans Hadoop demons HBase use cases Master Daemons Install and configure HBase on a multi node Name node cluster Job Tracker Create database, Develop and run sample Secondary name node applications Slave Daemons Access data stored in HBase using clients like Job tracker Java, Python and Pearl Task tracker HBase and Hive Integration HBase admin tasks HDFS ( Hadoop Distributed File System ) Defining Schema and basic operation Blocks and Splits Hive Input Splits Hive concepts HDFS Splits Hive architecture Data Replication Install and configure hive on cluster Hadoop Rack Aware Create database, access it from java client Data high availability Buckets Data Integrity Partitions Cluster architecture and block placement Joins in hive Accessing HDFS Inner joins JAVA Approach Outer Joins CLI Approach Hive UDF Programming Practices Hive UDAF Developing MapReduce Programs in Hive UDTF Local Mode Develop and run sample applications in Running without HDFS and Mapreduce Java/Python to access hive Pseudo-distributed Mode Running all daemons in a single node PIG Fully distributed mode Pig basics Running daemons on dedicated nodes Install and configure PIG on a cluster PIG Vs MapReduce and SQL Setup Hadoop cluster Pig Vs Hive Make a fully distributed Hadoop cluster Write sample Pig Latin scripts Cluster Specification Modes of running PIG Network Topology Running in Grunt shell Cluster Specification and installation Programming in Eclipse Hadoop configuration Running as Java program PIG UDFs Writing a MapReduce Program Pig Macros Examining a Sample MapReduce Program With several examples Flume Basic API Concepts Flume concepts The Driver Code Install and configure flume on cluster The Mapper Create a sample application to capture logs The Reducer from Apache using flume
  4. 4. Hadoop's Streaming API Performing several hadoop jobs The configure and close Methods Sequence Files Record Reader Record Writer Role of Reporter Output Collector Processing XML files Counters Directly Accessing HDFS ToolRunner Using The Distributed Cache Common MapReduce Algorithms Sorting and Searching Indexing Classification/Machine Learning Term Frequency - Inverse Document Frequency Word Co-Occurrence Hands-On Exercise: Creating an Inverted Index Identity Mapper Identity Reducer Exploring well known problems using MapReduce applications Debugging MapReduce Programs Testing with MRUnit Logging Other Debugging Strategies. Advanced MapReduce Programming A Recap of the MapReduce Flow The Secondary Sort Customized Input Formats and Output Formats Sqoop Getting Sqoop A Sample Import Database Imports Controlling the import Imports and consistency Direct-mode imports Performing an Export CDH4 Enhancements Name Node High – Availability Name Node federation Fencing YARN
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