Best Hadoop Training institute In Noida,Delhi

            Best Hadoop Training Institute in noida , Delhi

Inovi Technologies offers the industry recognized Big Data Hadoop Training in Noida that combines corporate training, classroom training effectively to fulfill the educational demands of the students worldwide. Big Data Hadoop training course in Noida provides in-depth knowledge on Hadoop Ecosystem tools and Big Data. Here you learn HDFS , MapReduce , Hbase , Hive , Pig, Oozie, Flume and Sqoop by working on real-world Big Data Hadoop Projects. Big data Hadoop Courses in Noida  With this Hadoop training conducted by experienced trainers in our institute, you can easily learn the components of the Hadoop ecosystem , such as Hadoop 2.7 , HDFS, Yarn, MapReduce , Pig , Impala , Flume , HBase , Apache Spark , and more. Designed by reputed industry experts , this best Hadoop certification training in Noida provides in-depth knowledge on Hadoop Ecosystem tools and Big Data. We also offer real-time training with case study based projects that provide hands-on experience of the subject.




Course Contents




Hadoop Installation & setup



The architecture of Hadoop 2.0 cluster , what is High Availability and Federation , how to setup a production cluster , the various shell commands in Hadoop , understanding configuration files in Hadoop 2.0, installing single node cluster with Cloudera Manager , understanding Spark , Scala , Sqoop, Pig and Flume.

Introduction to Big Data Hadoop. Understanding HDFS & Mapreduce




Introducing Big Data & Hadoop , what is Big Data and where does Hadoop fits in , two important Hadoop ecosystem components namely Mapreduce and HDFS, in-depth Hadoop Distributed File System – Replications , Block Size, Secondary Name node, High Availability , in-depth YARN – Resource Manager , Node Manager.Hands-on Exercise – HDFS working mechanism , data replication process , how to determine the size of the block , understanding a DataNode and NameNode.
Deep Dive in Mapreduce
Learning the working mechanism of MapReduce , understanding the mapping and reducing stages in MR , the various terminologies in MR like Input Format , Output Format , Partitioners , Combiners , Shuffle and Sort

Hands-on Exercise – How to write a Word Count program in MapReduce , how to write a custom Partitioner , what is a MapReduce Combiner , how to run a job in a local job runner , deploying unit test, what is a map side join and reduce side join , what is a tool runner , how to use counters , dataset joining with map side and reduce side joins.

Introduction to Hive

Introducing Hadoop Hive, detailed architecture of Hive, comparing Hive with Pig and RDBMS, working with Hive Query Language, creation of database, table, Group by and other clauses, the various types of Hive tables, Hcatalog, storing the Hive Results, Hive partitioning and Buckets.Hands-on Exercise – Database creation in Hive, dropping a database, Hive table creation, how to change the database, data loading, Hive table creation, dropping and altering table, pulling data by writing Hive queries with filter conditions, table partitioning in Hive, what is a group by clause

Advance Hive & Impala

The indexing in Hive , the Map side Join in Hive , working with complex data types , the Hive User-defined Functions , Introduction to Impala , comparing Hive with Impala , the detailed architecture of Impala Hands-on Exercise – How to work with Hive queries , the process of joining table and writing indexes, external table and sequence table deployment , data storage in a different table.

Introduction to Pig

Apache Pig introduction , its various features , the various data types and schema in Hive , the available functions in Pig , Hive Bags , Tuples and Fields.Hands-on Exercise – Working with Pig in MapReduce and local mode, loading of data , limiting data to 4 rows , storing the data into file , working with Group By ,Filter By ,Distinct ,Cross ,Split in Hive.

Flume , Sqoop & Hbase

Apache Sqoop introduction , overview , importing and exporting data , performance improvement with Sqoop , Sqoop limitations , introduction to Flume and understanding the architecture of Flume , what is HBase and the CAP theorem.Hands-on Exercise – Working with Flume to generating of Sequence Number and consuming it , using the Flume Agent to consume the Twitter data , using AVRO to create Hive Table , AVRO with Pig , creating Table in HBase , deploying Disable , Scan and Enable Table.

Writing Spark Applications using Scala

Using Scala for writing Apache Spark applications , detailed study of Scala , the need for Scala , the concept of object oriented programing , executing the Scala code , the various classes in Scala like Getters , Setters , Constructors , Abstract , Extending Objects, Overriding Methods, the Java and Scala interoperability , the concept of functional programming and anonymous functions , Bob Rockets package , comparing the mutable and immutable collections.Hands-on Exercise – Writing Spark application using Scala , understanding the robustness of Scala for Spark real-time analytics operation.

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