Self-Paced Big Data Hadoop Developer Certification
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.
Big Data Hadoop Developer Course Summary
The Collabera Big Data Hadoop Developer course delivers the 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 efforts based on various 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.
By the end of this course, participants will have the leadership toolkit to shape and steer GenAI portfolios across their organization.
Strategic Application
Most Big Data software runs on Linux, so a knowledge of Linux is a must for those interested in getting 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.
With the pre-requisites complete, now is the time to jump into Big Data. Before jumping into the technical aspects, participants are given a holistic view about Big Data. This will help them plan their career path and also work efficiently in various work environments.
Data is everywhere and we are constantly generating a lot of data which needs to be stored. HDFS stands for Hadoop Distributed File System, which allows for the storage of huge amounts of data in a cost-effective manner. This session will cover what HDFS is all about, the architecture, and how to interface with it
Once data has been stored in HDFS, now is 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 modes. We will look into how to develop, debug, optimize, and deploy MapReduce programs in different languages.
MapReduce from the previous session is a big verbose, and it’s quite difficult to write programs in MapReduce. That’s why, Yahoo started a software called Pig for data processing. Programs in Pig are compact and are easy to write. This is the reason most companies pick Pig compared to MapReduce for programming. This session will look at the Pig programming model.
Similar to Pig by Yahoo, Hive was developed by Facebook as an alternate to the MapReduce processing model. Like Pig, Hive also provides developer productivity when compared to MapReduce. The good thing about Hive is that it provides an SQL-like interface, so it’s easier to write programs.
NoSQL are the databases for Big Data. There are more than 125+ NoSQL databases and they have been categorized into 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, and what NoSQL performs better when compared to RDBMS. We will also look at HBase in detail.
Hadoop started the Big Data revolution, but there are a lot of software programs besides Hadoop, which either address the limitations of Hadoop or try to augment Hadoop. In this session, we will look at some of them.
Key Topics:
– Zookeeper, Oozie, Flume, Sqoop, Spark, Mongo, Cassandra, Neo4J
The course is mainly geared from a developer’s perspective, so it mainly deals with how to use particular software than the installation aspect of it. This section will briefly touch upon the administrative aspects of Big Data.
Key Topics:
– 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
In the above sessions, it was all about how individual software programs work. In the Proof of Concepts (POC) module, we will see how the individual software programs 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 of how Big Data software programs have to be integrated, and also how they are used to solve 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.
hands-on learning model
Access to sanitized process maps, KPI definitions, candidate initiative lists, and basic cost baselines (time, cycle time, error or rework rates)
Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.
One of our specialists will contact you within one business day.
Asked Questions
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Yes, the course completion certificate is provided once you successfully complete the training program. You will be evaluated on parameters such as attendance in sessions, an objective examination, and other factors. Based on your overall performance, you will be certified by Cognixia.
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