Best Hadoop Training in Marathahalli

Courses Info

Hadoop Training in Marathahalli

Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Marathahalli you’ll learn concepts in expert level with practical manner.

What is Hadoop?

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a parallel distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes of data which is not feasible with traditional systems.

Why Hadoop?

Today we live in a DATA world. Anything and everything that we do in the internet is becoming a source of business information for the organizations across the globe. The world has seen an exponential growth of data in the last decade or so and more so since last 3 years. Hence, the industry has started to look out for the ways to handle the data and get some business value out of it through data analytics. One such jail-break is “HADOOP”.

Yes, Hadoop is here to stay and lead the industry in helping the business with numerous ways to store, retrieve and analyze data.

What we do at TIB Academy for Hadoop?

Today we have been presented with an excellent opportunity to align ourselves with what the industry needs. All that industry needs is a Data Scientist / Analyst and that’s exactly what we at training in Marathahalli aim to do. We train aspiring data scientist / data analyst with best faculties available in the market whom have real time hands on experience in Hadoop area and who do project along with industry leading Cloudera Engineers. By giving the best Hadoop Training in Marathahalli we are getting opportunities to work with Cloudera Inc indirectly.

Whom Hadoop is suitable for?

Hadoop is suitable for all IT professionals who look forward to become Data Scientist / Data Analyst in future and become industry experts on the same. This course can be pursued by Java as well as non- Java background professionals (including Mainframe, DWH etc.)

Whom do we train?

We train professionals across all experience 0 -15 years and we have separate modules like Developer module, Project manager module etc.. We customize the syllabus covered according to the role requirements in the industry.

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Hadoop Syllabus

  1. Basics/Introduction : Multi-Threading , OpenMP (Open Multiprocessing) and MPI (Message Passing Interface), Performance tuning and optimization:-Matrix Multiplication, Uniqueword count problem, Distributed computing concepts, Designing and caveats, Distributed Debugging, Network challenges.
  1. Hadoop Overview : Why Hadoop?, Data Storage and Analysis, Hadoop v/s RDBMS, Brief history of Hadoop, Architecture of Hadoop, Overview of HDFS (Hadoop Distributed File System) and MR (Map Reduce) framework, Overview of problems solved by Hadoop, Data Mining, Web Mining, Natural Language Processing, K-means clustering, Sentimental Analysis.
  1. Setting up Hadoop: Pseudo Mode, Cluster, Common Errors when running Hadoop cluster, Incompatible name space IDs, Protocol version mismatch, Safe mode exception.
  1. HDFS- Hadoop Distributed File System: HDFS Design and Architecture, HDFS Concepts, Interacting HDFS using command line, Interacting HDFS using JAVA APIs, Running MR application on Local File system, pseudo mode, cluster mode, Creating and deleting directories on HDFS, Creating and deleting files on HDFS, Reading and writing files on HDFS, Moving files with in HDFS, Renaming files, Submitting Jobs, Data flow while reading and writing.
  1. Map Reduce Programming Model : Developing Map Reduce Application, Configuring environment, Writing Junit test cases for Mapper, Reducer and Driver, Using local runner, Running on pseudo mode, Running on cluster mode, Launching the job, Using Command Line, Using Java API, Tuning the job, Using Combiner, Using Compression, Using HProf (Profiler), Phases in Map Reduce Framework, Partitioning, Shuffling and Sorting, How Hadoop runs a Map Reduce Job, Job Submission, Job Initialization, Task Assignment, Task Execution, Map Reduce Input and Output Formats, Text IO, Sequence  IO, KeyValue IO, DataBase IO, Advance Concepts, Chain Mapper and Chain Reducer, Using Counters, Distributed Cache
  1. Hadoop Programming Languages : Hive, Installation, Creating tables, Writing Hive Queries, Pig, Installation, Concepts, Data processing operators, Writing UDFs.
  1. No SQL Data BasesMongodb: Concepts- SQL v/s NoSQL
  1. Case Studies / Projects
  1. Flume/zookeeper/sqoop.
  1. HDFS Monitoring and Logging: Logging, Metrics, Audit Logging.
  2. HDFS Maintenance: Commissioning and Decommissioning Node, Upgrading to new cluster.
Download Syllabus


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