Become an expert in Hadoop by acquiring knowledge on MapReduce, Hadoop architecture, Pig & Hive, Oozie, Flume and Apache workflow scheduler. Also, get familiar with Hbase, Zookeeper and Sqoop concepts, while working on industry-based, use-cases and projects.
Collabera Bigdata Hadoop developer course delivers key concepts and expertise necessary to create robust data processing applications using Apache Hadoop. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases. The course equips participants to work on the Hadoop environment with ease and learn vital components such as Zookeeper, Oozie, Flume, Sqoop, Spark, Mongo, Cassandra and Neo4J.
Where Does Collabera Tact Training Add Value?
As the trend of Big Data is getting bigger and popular with volume, variety and velocity, the demand of certified IT Hadoop professionals equipped with the right skills to process the Big Data through Hadoop are becoming higher among Fortune 500 companies. This implausible rush has significantly created an increase in the career scope for certified professionals in comparison to the non-certified peers.
Collabera TACT certification and enterprise-class intense training addresses this issue, by assuring to deliver key concepts and expertise necessary for developers to create robust data processing applications using Apache Hadoop. At the end of Collabera TACT training in Big Data & hadoop, participants will be able to:
• Learn to write complex MapReduce codes in both MRv1 & MRv2 (Yarn) and learn concepts of Hadoop framework and its deployment in a cluster environment.
• Perform analytics and learn high-level scripting frameworks Pig & Hive.
• Get in depth understanding of Big Data Ecosystem and its advance components like Oozie, Flume and apache workflow scheduler.
• Get acquainted with advance concepts Hbase, Zookeeper and Sqoop.
• Get hands-on experience in different configurations environment of Hadoop cluster.
• Learn about optimization & troubleshooting.
• Acquire in depth knowledge of Hadoop Architecture by learning about Hadoop Distribution File System operations principles (vHDFS 1.0 & vHDFS 2.0).
• Get hands-on practice with lab Exercises based on real life Industry based projects.
While there are no prerequisites for this course, however working knowledge of core java concepts and understanding of fundamental linux commands are expected.
Module 1 Learning Objectives – Introduction to Linux
Most of the Big Data software runs on Linux, so knowledge of Linux is a must for those interested to get into the various aspects of Big Data. Expertise in Linux is not required, but a basic knowledge of Linux is a must. The Linux sessions will cover just enough concepts around Ubuntu for an aspirant to quickly get started with Big Data.
Module 2 Learning Objectives – What is Big Data?
With the prerequisites complete, now is the time to jump into Big Data. Before jumping into the technical aspects the participants are given a holistic view about what Big Data is all about. This will help them to plan their carrier path and also work efficiently in the work environments.
Module 3 Learning Objectives – HDFS (Hadoop Distributed File System)
Data is everywhere and we are constantly generating a lot of data which needs to be stored. HDFS stands for Hadoop Distributed File System and allows for storing huge amounts of data in a cost effective manner. This session will cover what HDFS is all about, the architecture, how to interface with it.
Module 4 Learning Objectives – MapReduce
Once the data has been stored in HDFS, now it the time to process the data. There are many ways to process the data and MapReduce which has been introduced by Google is one of the earliest and the most popular mode. We will look into how to develop, debug, optimize and deploy MapReduce programs in different languages.
Module 5 Learning Objectives – Pig
MapReduce from the previous session is a big verbose and it’s difficult to write programs in MapReduce. That why, Yahoo started a software called Pig for data processing. Programs in Pig are compact and are easy to write. This is the reason for most of the companies to pick Pig when compared to MapReduce programming. This session will look at the Pig programming model.
Module 6 Learning Objectives – Hive
Similar to Pig by Yahoo, Hive was developed by Facebook as an alternate to MapReduce processing model. Similar to Pig, Hive also provides developer productivity when compared to MapReduce. The good thing about Hive is that it provides and SQL like interface and so it makes it easy to write programs against Hive.
Module 7 Learning Objectives – NoSQL (HBase)
NoSQL are the data bases for the Big Data. There are more than 125+ NoSQL databases and they have been categorized in the following types – KeyValue databases (Accumulo, Dynamo, Riak etc) – Columnar databases (HBase, Cassandra etc) – Document databases (Mongo, Couch etc) – Graph databases (Neo4j, Flock etc) In this session, we will look into what NoSQL is all about, their characteristics, what NoSQL performs better when compared to RDBMS. We will also look at HBase in detail.
Module 8 Learning Objectives – Big Data Ecosystem
Hadoop started the Big Data revolution, but there are a lot of softwares besides Hadoop which either address the limitations of Hadoop or try to augment Hadoop. In this session we will look at some of them.
– Zookeeper, Oozie, Flume, Sqoop, Spark, Mongo, Cassandra, Neo4J.
Module 9 Learning Objectives – Big Data
Administration Learning Objectives The course is mainly geared from a developer perspective, so it mainly deals with how to use particular software than on the installation aspect of it. This section will briefly touch upon the administrative aspects of Big Data.
– Theory on how the Big Data Virtual machine has been created.
– Introduction to Cloud
– Demo of the creation of the Cloudera CDH cluster on the Amazon AWS cloud.
Module 10 Learning Objectives – Proof of concepts
In the above sessions it was all about how the individual softwares work. In the POC (Proof Of Concepts) we will see how the individual softwares can be integrated and what can be done as a whole. The POC will be close to real life use cases as in the case of Amazon, eBay, Google and other big companies. The POCs will give the participants an idea how the Big Data softwares have to be integrated and also how they are used to solve some actual problems. For the POC section there will be close to 3 hours of discussion and practice. An Internet connection is required for the participants to work on the POC.
Our Instructors are certified professionals and owns a deep experience from the Industry, this helps us delivering a great learning experience.
2 Mbps of internet speed is preferable to attend the LIVE classes.
Yes, Collabera TACT’s Virtual Machine can be installed on local machine.
Your system should have 4GB RAM, a 64 – bit OS and a Virtualization Technology enabled processor.
The Hadoop developer course at Collabera TACT is a 8-weekend course.
The recorded session for the class will be available on the LMS for your reference. We also have a support team, so in case you need any clarification on concepts or help in debug or installation etc, the support team will help you on it.
The access to the training infrastructure services will be for first 120 days OR 4 months.
Yes, we do have group discount option. You can contact firstname.lastname@example.org to know more about the group discounts.
Yes, we offer course completion certificate after you successfully complete the training program.