Become an expert in Hadoop by getting hands-on 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.
As new job opportunities are arising for IT professionals in the field of “Big Data & Hadoop,” there is an enormous scope for them. According to the recent study, in 2018, there will be 181,000 Big Data roles within the U.S. By 2020, the Big Data & Hadoop market is estimated to grow at a compound annual growth rate (CAGR) 58% surpassing $16 billion.
Big Data Hadoop Developer certification offered by Cognixia brings out the key ideas and proficiency for managing Big Data with Apache’s open source platform – Hadoop. Not only does it impart in-depth knowledge on core ideas through the course, it also facilitates executing it on wide-ranging industry use-cases. This imparts new opportunities to organizations of all sizes and equips professionals to write codes on MapReduce framework. The course also consists of advanced modules like Yarn, Zookeeper, Oozie, Flume and Sqoop.
Learn to write complex codes in MapReduce on both MRv1 & MRv2 (Yarn) and understand Hadoop architecture.
Perform analytics and learn high-level scripting frameworks Pig & Hive.
Get full understanding of Hadoop system and its advance elements like Oozie, Flume and apache workflow scheduler.
Get familiar with other concepts: Hbase, Zookeeper and Sqoop.
Get hands-on expertise in numerous configurations surroundings of Hadoop cluster.
Learn about optimization & troubleshooting.
Acquire in-depth knowledge on Hadoop architecture by learning about Hadoop Distribution file system (vHDFS one.0 & vHDFS a pair of.0).
Get to work on Real Life Project on Industry standards.
Any individual who wants to pursue their career in Big Data and Hadoop should have a basic understanding of Core Java. However, it is not mandatory as Cognixia offers complementary Java (self paced) tutorials that will assist you to brush up your Java skills.
Project 1: “Twitter Analysis”
The general observation is that 80% of the data is unstructured, while the remaining 20% is said to be in structured form. With the help of RDBMS, we can store/process only the structured data while Hadoop enables us to store or process unstructured data as well.
Today Twitter has become a significant source of data and a reliable one at analyzing what the consumer is thinking about (sentimental analysis). This helps in figuring out the trending topics/ discussions. During this case study we will be gathering data from Twitter, using various means, for some interesting analysis.
Project 2: “Click Stream Analysis”
E-commerce websites have been observed to impact the economy of their region in a huge way. This trend has been observed globally. Every e-commerce website keeps a record of user-activity and stores it as clickstream. This activity is used to analyze the browsing patterns of a particular user thus helping the sites to recommend products, with high accuracy, when the user visits the website the next time. This also helps the e-commerce websites to design personalized promotional emails for its users.
In this case study we will see how we can analyze the clickstream and user-data by using Pig and Hive. We will be gathering the user data with the help of RDBMS and will capture the user-behavior (clickstream) by using Flume in HDFS. Thereafter, we will analyze this data using Pig and Hive. We will also be automating the Click Stream Analysis by putting workflow engine Oozie to use.
Introduction/ Installation of Virtual Box and the Big Data VM, Introduction to Linux, Why Linux?, Windows and the Linux equivalents, Different flavors of Linux, Unity Shell (Ubuntu UI), Basic Linux Commands (enough to get started with Hadoop).
3V (Volume- Variety- Velocity) characteristics, structured and unstructured data, application and use cases of Big Data, limitations of traditional large scale systems, how a distributed way of computing is superior (cost and scale)?, opportunities and challenges with Big Data.
HDFS Overview and Architecture, Deployment Architecture, Name Node, Data Node and Checkpoint Node (aka Secondary Name Node), Safe mode, Configuration files, HDFS Data Flows (Read v/s Write).
CRC Check Sum, Data Replication, Rack awareness and Block placement policy, Small files problem.
Command Line Interface, File System, Administrative, Web Interface.
Load Balancer, Dist cp (Distributed Copy), HDFS Federation, HDFS High Availability, Hadoop Archives.
MapReduce overview, Functional Programming paradigms, How to think in a MapReduce way?
Legacy MR v/s Next Generation MapReduce, ( aka YARN/ MRv2), Slots v/s Containers, Schedulers, Shuffling, Sorting, Hadoop Data Types, Input and Output Formats, Input Splits – Partitioning (Hash Partitioner v/s Customer Partitioner), Configuration files, Distributed Cache
Adhoc Querying, Graph Computing Engines.
Stand alone mode (in Eclipse), Pseudo Distributed mode (as in the Big Data VM), Fully Distributed mode (as in Production), MR API, Old and the New MR API, Java Client API, Hadoop data types and custom Writable.
Different input and output formats, Saving Binary Data using Sequence Files and Avro Files, Hadoop Streaming (developing and debugging non Java MR programs – Ruby and Python).
Sorting, Term Frequency, Inverse Document Frequency, Student Database, Max Temperature, Different ways of joining data, Word Co-occurrence.
Click Stream Analysis using Pig and Hive, Analyzing the Twitter data with Hive, Further ideas for data analysis
HBase Data Modeling, Bulk loading data in HBase, HBase Coprocessors – Endpoints (similar to Stored Procedures in RDBMS), HBase Coprocessors – Observers (similar to Triggers in RDBMS).
PageRank, Inverted Index.
Introduction and Architecture, Different modes of executing Pig constructs, Data Types, Dynamic invokers Pig streaming Macros, Pig Latin language Constructs (LOAD, STORE, DUMP, SPLIT, etc), User Defined Functions, Use Cases.
NoSQL Databases – 1 (Theoretical Concepts), NoSQL Concepts, Review of RDBMS
Need for NoSQL, Brewers CAP Theorem, ACI D v/s BASE, Schema on Read vs. Schema on Write, Different levels of consistency, Bloom filters.
Key Value, Columnar, Document, Graph.
HBase Architecture, Master and the Region Server, Catalog tables (ROOT and META), Major and Minor Compaction, Configuration Files, HBase v/s Cassandra.
Java API, Client API, Filters, Scan Caching and Batching, Command Line Interface, REST API.
Introduction to RDD, Installation and Configuration of Spark, Spark Architecture, Different interfaces to Spark, Sample Python programs in Spark.
Usecase of YARN, YARN Architecture, YARN Demo.
Usecase of Oozie, Oozie Architecture, Oozie Demo.
Usecase of Flume, Flume Architecture, Flume Demo.
Usecase of Sqoop, Sqoop Architecture, Sqoop Demo.
Cloudera Hadoop cluster on the Amazon Cloud (Practice), Using EMR (Elastic Map Reduce), Using EC2 (Elastic Compute Cloud).
Stand alone mode (Theory), Distributed mode (Theory), Pseudo distributed, Fully-distributed.
Hadoop industry solutions, Importing/exporting data across RDBMS and HDFS using Sqoop. Getting real-time events into HDFS using Flume, Creating workflows in Oozie, Introduction to Graph processing, Graph processing with Neo4J, Using the Mongo Document Database, Using the Cassandra Columnar Database, Distributed Coordination with Zookeeper
We provide 42 hours of live online training, including live POC and assignments.
Live and interactive online sessions with an industry expert instructor.
Expert technical team available for query resolution.
We provide lifetime Learning Management System (LMS) access, which you can access from across the globe.
We strive to offer the best price to our customers with the guarantee of quality service levels.
After completing the course, you will appear for an assessment from Cognixia. Once you pass, you will be awarded a course completion certificate.
Our instructors/trainers are Cloudera and Hortonworks-certified professionals. They have industry experience of more than 12 years and are Subject Matter Experts in Big Data.
To attend the live virtual training, at least 2 Mbps of internet speed would be required.
Yes, Cognixia’s Virtual Machine can be installed on any local system. The training team of Collabera will assist you with this.
To install the Hadoop environment, one needs to have 8GB RAM, 64-bit OS, 50 GB free space on hard disk, and a Virtualization Technology-enabled processor within their systems.
The online live training course will be conducted over 8 weekends (15-16 sessions).
Candidates need not worry about missing any training session. They will be able to view the recorded sessions available on the LMS. We also have a technical support team to assist candidates in case they have any query.
The access to the Learning Management System (LMS) will be for lifetime, which includes class recordings, presentations, sample code, and projects.
Overall it was a good session. Our trainer had a very good knowledge on all the technologies and tools. Thanks to cognixia and Sitaram.
The training sessions were very conceptual and interactive. Thanks to the Organizers and Presenter for providing us with the valuable training on Big data Hadoop in a very professional and efficient manner.
Hadoop developer course Trainer is very good at BIG Data technologies. He explained all the concepts in-detail and it was easy to understand. I learned a lot and it will be very useful for my career.
The training & the Trainer was good. He taught us the concepts of big data well.
Training standards professional, covering each and every concept from the core of it and based more on hands on practice.
It was a great experience with Cognixia when it comes to upgrading the skills on emerging technologies.
The training on Big Data Hadoop Developer acquires in-depth knowledge on Hadoop architecture by learning about Hadoop Distribution file system.
The course curriculum of Big Data Hadoop Developer is quite informative and moreover, the support received from the technical team was quite appreciable.
The training program on Big Data Hadoop Developer provides in-depth knowledge of core ideas and executing it on wide-ranging industry use-cases.
The course content is quite interesting and informative and also provides hands-on expertise on Pig & Hive, Oozie, Hadoop architecture, Flume, Map Reduce and Apache workflow scheduler.