Title : BIG DATA - Professional Course
Hours : 60 Hrs
Batches : Weekday ( Mon - Fri )|Weekend ( Sat & Sun )
60 Hours of
Class Room Training
30 Sessions of
Class Room Training
90 Hours of
50 Coding Tasks
For Interview Purpose
We provide a Professional level course in Big Data that lets you master the concepts of the Hadoop framework. With our Hadoop training, you’ll learn how the components of the Hadoop ecosystem, such as Hadoop, Yarn, MapReduce, HDFS, Pig, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. Also, learn how to implement real-life projects in banking, social media, insurance, and e-commerce.
You should be able to
- Become a Big Data Engineer where highly paid jobs are available.
- Execute big data integration and processing on Hadoop and Spark platforms.
- Describe the connections between data management operations and the big data processing patterns in large-scale analytical applications.
IT professionals or anyone who is looking towards building a career in Big Data and Hadoop are ideal participants for the Big Data and Hadoop training.
Additionally, it is suitable for participants who are
- Data Management / Data Warehousing professionals.
- Software Graduates.
11:30 AM to 1:30 PM | Monday to Friday | 5 Days/Week | 6 Weeks Course
11:30 AM to 1:30 PM | Saturday & Sunday | 2 Days/Week | 15 Weeks Course
- Class-1 : Big Data Introduction
- Class-2 : Understanding value of Data
- Class-3 : Big Data Concepts and Benefits
- Class-4 : Data Storage & Analysis
- Class-5 : Querying Data
- Class-6 : Environmental Setup
- Class-7 : Introduction to Hadoop Ecosystem
- Class-8 : Introduction to HDFS
- Class-9 : HDFS Architecture and Components
- Class-10 : Block Replication Architecture
- Class-11 : Introduction to YARN
- Class-12 : HADOOP Exercise using Java/Python
- Class-13 : MapReduce
- Class-14 : Sqoop
- Class-15 : MapReduce using Java/Python
- Class-16 : Introduction Hive and Impala
- Class-17 : Working with Hive and Impala
- Class-18 : Data Formats, & File Partitioning, Advanced Hive
- Class-19 : Apache Flume and Hbase
- Class-20 : Introduction to Pig and Datasets Development
- Class-21 : Basics of Apache Spark
- Class-22 : RDDs in Spark
- Class-23 : Implementation of Spark Applications
- Class-24 : Spark - Parallel Processing, RDD Techniques
- Class-25 : Machine Learning Introduction
- Class-26 : Supervised Learning in Machine Learning
- Class-27 : Unsupervised Learning in Machine Learning
- Class-28 : Tabluea Introduction
- Class-29 : Data Visualization
- Class-30 : Real World Case study