Hadoop Training Institute In Bangalore with Course Certification

Hadoop Training Institute In Bangalore with Course Certification 

 

BEST HADOOP TRAINING INSTITUTE IN BANGALORE ,BTM ,MARATHAHALLI 

If you are searching for the best Hadoop training in Bangalore then you have come to the correct place, Upshot technologies in BTM, Bangalore. Because, we are the Best training centre in teaching Hadoop in marathahalli, Bangalore. 

About Hadoop: 

  Open source framework used to store, process and analyse Big Data.   Big Data is nothing but a huge volume of data including structured and unstructured data.   Created by Doug Cutting and Mike Cafarella in 2011 and released in 2012.   Part of Apache project by Apache Software Foundation (ASF).   Uses distributed and parallel computing to perform all its tasks successfully.   Its distributed file system allows high data transfer rate and thus enabling faster and efficient processing.   In recent years, Hadoop has emerged as one of the important pillars for Big Data analytics. 

COURSE DESCRIPTION 

Upshot Technologies is the one of the premier training institutes in Bangalore with huge expertise and experience in teaching Hadoop. Due to our experience, we are now providing the best Hadoop training in Bangalore. Some of the benefits of joining the best training institute are given below: 

Syllabus   Specially designed considering the requirements of the IT industry.   Extensive including Big Data, Data analytics and hosting on different clouds.   Prepared by a team of Hadoop experts who prepare the study materials.   Consists of two parts namely Theoretical basis (classroom) and Practical sessions.   Includes many case studies and real-time projects. 

Trainers   Working Professionals with superior skills and in-depth knowledge.   Have extensive experience in Hadoop and are committers in ASF for Hadoop.   Compassionate teachers who cares for the education and welfare of the students.   Provides counselling and advices to our students whenever required. 

Infrastructure   State-of-the-art computer lab with Hadoop being installed in all the systems.   Smart classrooms with projectors and video-conferencing kits.   Spacious and calm study halls and libraries for our students.   Lab assistants are always available to support the students to practice.   Free Wi-fi connectivity to help our students stay up-to-date. 

Placement care   100% placement guarantee for all successful students.   A dedicated team to ensure that all of our students got a job after completing the course.   Help to prepare an impressive Resume.   Provide a lot of interview preparation study materials.   Conduct mock tests and interviews to familiarize our students. 

There are also other perks in choosing the Best Hadoop training institute such as 

  Flexible batch timings to admit students, freshers and employed professionals.   Affordable fees structure to help the as many students as possible.   Access to a huge repository containing information about Hadoop.   1-to-1 training and Corporate training can be arranged if informed earlier. 

SYLLABUS 

Hadoop Training Syllabus 

Module 1 : Fundamental of Core Java 

Module 2 : Fundamental of Basic SQL 

Module 3: Introduction to BigData, Hadoop (HDFS and MapReduce) :   BigData Introduction   Hadoop Introduction   HDFS Introduction   MapReduce Introduction 

Module 4 : Deep Dive in HDFS   HDFS Design   Fundamental of HDFS (Blocks, NameNode, DataNode, Secondary Name Node)   Read/Write from HDFS   HDFS Federation and High Availability   Parallel Copying using DistCp   HDFS Command Line Interface 

Module 4A : HDFS File Operation Lifecycle (Supplementary)   1. File Read Cycel from HDFS – DistributedFileSystem – FSDataInputStream   2. Failure or Error Handling When File Reading Fails   3. File Write Cycle from HDFS – FSDataOutputStream   4. Failure or Error Handling while File write fails 

Module 5 : Understanding MapReduce :   JobTracker and TaskTracker   Topology Hadoop cluster   Example of MapReduce – Map Function – Reduce Function   Java Implementation of MapReduce   DataFlow of MapReduce   Use of Combiner 

Module 6 : MapReduce Internals -1 (In Detail)   How MapReduce Works   Anatomy of MapReduce Job (MR-1)   Submission & Initialization of MapReduce Job (What Happen ?)   Assigning & Execution of Tasks   Monitoring & Progress of MapReduce Job   Completion of Job 

Module 7 : Advanced MapReduce Algorithm   File Based Data Structure – Sequence File – MapFile   Default Sorting In MapReduce – Data Filtering (Map-only jobs) – Partial Sorting   Data Lookup Stratgies – In MapFiles   Sorting Algorithm – Total Sort (Globally Sorted Data) – InputSampler – Secondary Sort 

Module 8 : Advanced MapReduce Algorithm -2   MapReduce Joining – Reduce Side Join – MapSide Join – Semi Join   2. MapReduce Job Chaining – MapReduce Sequence Chaining – MapReduce Complex Chaining 

Module 9 : Apache Pig   What is Pig ?   Introduction to Pig Data Flow Engine   Pig and MapReduce in Detail   When should Pig Used ?   Pig and Hadoop Cluster   Pig Interpreter and MapReduce   Pig Relations and Data Types   PigLatin Example in Detail   Debugging and Generating Example in Apache Pig 

Module 9A : Apache Pig Coding   Working with Grunt shell   Create word count application   Execute word count application   Accessing HDFS from grunt shell 

Module 9B : Apache Pig Complex Datatypes   Understand Map, Tuple and Bag   Create Outer Bag and Inner Bag   Defining Pig Schema 

Module 9C : Apache Pig Data loading   Understand Load statement   Loading csv file   Loading csv file with schema   Loading Tab separated file   Storing back data to HDFS. 

Module 9D : Apache Pig Statements 

  ForEach statement   Example 1 : Data projecting and foreach statement   Example 2 : Projection using schema   Example 3 : Another way of selecting columns using two dots .. 

Module 9E : Apache Pig Complex Datatype practice   Example 1 : Loading Complex Datatypes   Example 2 : Loading compressed files   Example 3 : Store relation as compressed files   Example 4 : Nested FOREACH statements to solved same problem. 

Module 10 : Fundamental of Apache Hive Part-1   What is Hive ?   Architecture of Hive   Hive Services   Hive Clients   how Hive Differs from Traditional RDBMS   Introduction to HiveQL   Data Types and File Formats in Hive   File Encoding   Common problems while working with Hive 

Module 10A : Apache Hive   HiveQL   Managed and External Tables   Understand Storage Formats   Querying Data – Sorting and Aggregation – MapReduce In Query – Joins, SubQueries and Views   5. Writing User Defined Functions (UDFs)   6. Data types and schemas   7. Querying Data   8. HiveODBC   9. User-Defined Functions 

Module 11 : Step by Step Process creating and Configuring eclipse for writing MapReduce Code Module 12 : NOSQL Introduction and Implementation   What is NoSQL ?   NoSQL Characterise or Common Traits   Categories of NoSQL DataBases – Key-Value Database – Document DataBase – Column Family DataBase – Graph DataBase   4. Aggregate Orientation : Perfect fit for NoSQl   5. NOSQL Implementation   6. Key-Value Database Example and Use   7. Document DataBase Example and Use   8. Column Family DataBase Example and Use   9. What is Polyglot persistence ? 

Module 12A : HBase Introduction   Fundamentals of HBase   Usage Scenerio of HBase   Use of HBase in Search Engine   HBase DataModel – Table and Row – Column Family and Column Qualifier – Cell and its Versioning – Regions and Region Server   5. HBase Designing Tables   6. HBase Data Coordinates   7. Versions and HBase Operation – Get/Scan – Put – Delete 

Module 13 : Apache Sqoop (SQL To Hadoop)   Sqoop Tutorial   How does Sqoop Work   Sqoop JDBCDriver and Connectors   Sqoop Importing Data   Various Options to Import Data – Table Import – Binary Data Import – SpeedUp the Import – Filtering Import – Full DataBase Import Introduction to Sqoop 

Module 14 : Apache Flume   Data Acquisition : Apache Flume Introduction   Apache Flume Components   POSIX and HDFS File Write   Flume Events   Interceptors, Channel Selectors, Sink Processor 

Module 14A : Advanced Apache Flume   Sample Twiteer Feed Configuration   Flume Channel – Memory Channel – File Channel   3. Sinks and Sink Processors   4. Sources   5. Channel Selectors   6. Interceptors 

Module 15 : Apache Spark : Introduction to Apache Spark   Introduction to Apache Spark   Features of Apache Spark   Apache Spark Stack   Introduction to RDD’s   RDD’s Transformation   What is Good and Bad In MapReduce   Why to use Apache Spark 

Module 16 : Load data in HDFS using the HDFS commands 

Module 17 : Importing Data from RDBMS to HDFS   Without Specifying Directory   With target Directory   With warehouse directory 

Module 18 : Sqoop Import & Export Module   Importing Subset of data from RDBMS   Changing the delimiter during Import   Encoding Null values   Importing Entire schema or all tables 

CERTIFICATION 

Hadoop is an open source framework developed by a non-profit corporation (ASF). So, it has no official certification available but there are some other private Certifications available right now. These are accepted by lot of companies. For example, CCA Spark and Hadoop Developer Certification by Cloudera Inc. and HDP Certified Developer and HDP Certified Java Developer Certifications by Hortonworks. Our Hadoop training covers the basics of all these exams and you can easily clear the certification exams with the knowledge you learned and the guidance of our placement cell. But you won’t need these certifications to get a job because you would be placed as soon as you had successfully completed our Hadoop training. 

LEARNING OUTCOMES 

After the completion of our Hadoop training, you will have numerous job opportunities from all over the world. Some of the designations you will be recruited for, are listed below: 

  Hadoop Developer   Technical Consultant – Hadoop   Software Development Engineer – Hadoop   BigData Hadoop Consultant 

Apart from these, there are other career options such as promotions, switching job to a MNC and even teaching Hadoop at institutes or online platforms based on your availability.