Big Data Hadoop is the next gen highly demanding technology today. It has very high capacity and potential to sustain in the market need. Simply put, it is the technology behind all the big social sites, search engines and advertisements. With this framework we can process huge data in terms of Velocity, Volume and Variery, it is set to change the IT industries in a big way that data is stored and processed. The employees and candidates who are not upgrading in the Big Data skills will be laid off in huge number in coming years. We are a focused enterprise that strongly believes in empowering minds with multi skills and enlightening them for current and future need. It making us best big data hadoop training institute in Pimpri Chinchwad.
Yes, You can enroll for any single course. Prior you have to send us an email or enquiry for the same.
Yes. You can enroll but you will have to work hard for learning from the scratch.
Yes. 70% amount can be refunded in case of enrollment cancellation is done in between 1-15 days from starting of course.
You must have basic knowledge of any programing language and dbms/sql.
Yes, we always recommend that course has to be taken from topics/subjects order to undestand better.
Hadoop is an open source distributed processing framework and technology that manages huge data processing and storage for applications running in clustere.
Big data Concepts Distributed network and computation Challenges in data management and control Introduction to Big Data Types of data in detail Sources of Big Data Concept of Streaming data Batch and Streaming data processing Big data Hadoop future opportunities Hadoop Overview Need of Hadoop technology Overview of Data centers and Cluster Hadoop Cluster and Racks in detail Learning Ubuntu for Hadoop Overview of Hadoop tools Overview of Map Reduce Big data Concepts Distributed network and computation Challenges in data management and control Introduction to Big Data Types of data in detail Sources of Big Data Concept of Streaming data Batch and Streaming data processing Big data Hadoop future opportunities Hadoop Overview Need of Hadoop technology Overview of Data centers and Cluster Hadoop Cluster and Racks in detail Learning Ubuntu for Hadoop Overview of Hadoop tools Overview of Map Reduce Understanding the Hadoop Installation and Configuration 5 daemons of Hadoop Name Node and its functionality Data Node and its functionality Secondary Name Node and its functionality Job Tracker and its functionality Task Tracker and its functionality HDFS HDFS Daemons Introduction about Blocks Data replication Hadoop DFS and Processing Fault Tolerance in Hadoop Files operations in Hadoop FS shell commands in use YARN (Yet Another Resource Negotiator) Introduction to YARN YARN Daemons Job assignment and Execution flow Map Reduce Programming Model Word count program demonstration Apache Pig Introduction to Apache Pig Apache pig Architecture Advantage of Pig over MapReduce programming Pig Latin and Grunt Shell Pig Latin basics Operators in Pig Group, Join Split and Combine Built in functions Load and Store function Date, Math and String functions Schema and Schema-less data in Pig Data processing in Pig Pig UDFs writing HCatalog Pig and Hive comparison Apache Hive Data warehouse basics OLTP and OLAP Concepts Introduction to Hive Hive Architecture in detail Metastore DB and Metastore Service Hive Query Language (HQL) Types of table in hive Partitioning and Bucketing Built in Functions Case writing Views and Indexes Joins and Group by Sorting, Distribute by Query Optimization methods JDBC , ODBC connection to Hive Hive Transactions Hive UDFs and UDAFs Working with file formats Sqoop Sqoop basic commands Sqoop practical implementation Sqoop Architecture Database operations Importing RDBMS data to HDFS Importing RDBMS data to Hive Exporting data to RDBMS Sqoop connectors Importing into Hive Flume Flume Architecture Flume Environment Data transfer and data flow Flume Configuration Configuration of Source, Channel and Sink Loading from web server or other storage data Loading from raw/flow data in HDFS using flume Oozie Introduction to Oozie Designing workflow jobs Job scheduling using Oozie Time based job scheduling Oozie Conf file Crontab use and implementation Hands on HUE (Hadoop User Interface) HUE usage User management Using Pig, Hive, Impala Impala Impala overview Impala Architecture Impala Vs Hive Impala Built in functions Connecting to impala Java for impala JDBC Output creation HBASE Basics Architecture and schema design HBase vs. RDBMS HMaster and Region Servers Column Families and Regions Write pipeline Read pipeline HBase commands Domain Based Project With Real Time Data 100 Assignments