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.
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.
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:
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.
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.
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.
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.
Hadoop Training Syllabus
Module 1 : Fundamental of Core Java
Module 2 : Fundamental of Basic SQL
Module 4 : Deep Dive in HDFS
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
2. Failure or Error Handling When File Reading Fails
3. File Write Cycle from HDFS
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
Default Sorting In MapReduce
– Data Filtering (Map-only jobs)
– Partial Sorting
Data Lookup Stratgies
– In MapFiles
– Total Sort (Globally Sorted Data)
– Secondary Sort
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 9D : Apache Pig Statements
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
how Hive Differs from Traditional RDBMS
Introduction to HiveQL
Data Types and File Formats in Hive
Common problems while working with Hive
Module 10A : Apache Hive
Managed and External Tables
Understand Storage Formats
– Sorting and Aggregation
– MapReduce In Query
– Joins, SubQueries and Views
5. Writing User Defined Functions (UDFs)
6. Data types and schemas
7. Querying Data
9. User-Defined Functions
Module 11 : Step by Step Process creating and Configuring eclipse for writing MapReduce
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
– 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
Module 13 : Apache Sqoop (SQL To Hadoop)
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 15 : Apache Spark : Introduction to Apache Spark
Introduction to Apache Spark
Features of Apache Spark
Apache Spark Stack
Introduction to RDD’s
What is Good and Bad In MapReduce
Why to use Apache Spark
Module 16 : Load data in HDFS using the HDFS commands
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.
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:
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.