Hadoop is an Apache project (i.e. an open-source software) to store & process Big Data. Hadoop
stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards,
Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File
As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume.
What are the objectives of our Big Data Hadoop Live Course?
This course is designed by industry experts to make you an expert Big Data Practitioner. This course offers:
• In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System),
• YARN (Yet Another Resource Negotiator) & MapReduce
• Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Kafka, Sqoop, Flume, Oozie, and HBase
• The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
Why should you go for this course?
Big Data is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take benefit of these opportunities, you need a structured training with the latest curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real world big data projects using different Big Data and Hadoop tools as a part of solution strategy. Additionally, you need the guidance of a Hadoop expert who is currently working in the industry on real world Big Data projects and troubleshooting day to day challenges while implementing them.
It will be an online live (Live Stream) class, so you can attend this class from any geographical location. It will be an interactive live session, where you can ask your doubts to the instructor (similar to offline classroom program).
It is a weekend Live classes Batch scheduled on every
This course will help you to become a Big Data expert. It will hone your skills by offering you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. This course you will be trained by our expert instructors to:
Learning Objectives: In this module, you will understand what Big Data is, the limitations of the
traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop
Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.
You will also learn Hadoop Cluster Architecture, important
configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to
setup Single Node and Multi-Node Hadoop Cluster.
Learning Objectives: In this module, you will understand Hadoop MapReduce framework
comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced
MapReduce concepts like Input Splits, Combiner & Partitioner.
You will also learn Advanced MapReduce concepts such as Counters,
Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.
Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use
Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF,
Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset. We will start Apache Hive as well.
Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types,
loading and querying data in Hive, running hive scripts and Hive UDF.
Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as
UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire in-
depth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.
Learning Objectives: This module will cover advance Apache HBase concepts. We will see demos on
HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in
monitoring a cluster & why HBase uses Zookeeper.
Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark
Learning Objectives: In this module, you will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You
will be running application on Spark Cluster & comparing the performance of MapReduce and
In this module, you will understand how multiple Hadoop ecosystem
components work together to solve Big Data problems. This module will also cover Flume & Sqoop
demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
Scheduling Jobs with Oozie Scheduler
Demo of Oozie Workflow
Oozie Web Console
Oozie for MapReduce
Combining flow of MapReduce Jobs
Hive in Oozie
BigData Engineer | Ex- American Express, Siemens | 6+ years of Exp.
Sagar has 6+ years of technical experience and has served in AMEX and Siemens. He has sound knowledge of Big data tech stack, java, scala, python, scalable data-pipelines, Dynamic scheduling of jobs for better resource utilization. He is very passionate about data and handling the same with new technology coming to the market.
When can i access the recorded session of the class (if someone misses the live class)?
The recorded session of the class will be uploaded in 2 working days.