Hadoop Training in Marathahalli
Hadoop is an open source and demanding data analytics technology, emerging in the market nowadays. Hadoop has the capability to manage storage and perform data manipulation effectively. Its main purpose is to resolve Big Data problems and achieve data solutions. Distributed data processing across clusters of computers using simple programming models will be possible using Hadoop. MapReduce can be considered as “heart of Hadoop” as it possesses massive scalability. It performs data collection, data processing, data storage and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms. Collection of massive unstructured data from multiple sources can be parsed from raw data to user preferred format.
Hadoop Job Opportunities
- Make yourself strong in Hadoop and any database like MongoDB, Oracle, SQL Server to find opportunities for “Hadoop developer” jobs.
- Start learning Python, Web services and AWS Solution architect along with Hadoop, you can make yourself eligible for “AWS Hadoop developer” jobs.
- If you have strong Hadoop skills with Splunk, Unix, then you can find opportunities for “Big Data Engineer” jobs.
- If you have excellent administration skills in Hadoop, you can get job as Hadoop admin.
- If you have strong knowledge in Big Data – Hadoop and MongoDB, you can get job as Big Data MongoDB developer.
- JP Morgan, Altisource, Accenture, Akamai, Ocwen, Mphasis, Capgemini, Oracle, IBM, TCS are some of the companies hire for Hadoop developers.
- If you are a newbie to Hadoop, then you need comprehensive training and real time experience in any RDBMS for at least 3 years. This can help you meet the expectations of current market trends and demands.
Expand your Hadoop job opportunities and maximize the chances by acquiring the best support and training from TIB Academy.
Training marathahalli gives training on trending software courses. They have expertise trainers who handle classes effectively. They provide live classroom training with real time problems. They are also providing placement assistance like resume preparation and all. Their training fee is low.
Prerequisites for Hadoop
- Core Java, RDBMS and Linux knowledge.
- If you are already familiar with the above, this course will be easier for you to learn. Otherwise, our experienced professionals are here to teach you and coach you right from the Hadoop fundamentals.
Are you a beginner? Evaluate yourself with the following basic prerequisite questions.
- What is meant by Object Oriented Programming?
- What is collections in Core Java?
- List out few operations of String class in Java?
- What are the DML operations in RDBMS?
- What are the types of joins in RDBMS and explain each.
Our Hadoop Training and Support
TIB Academy is the best Hadoop training institute in Marathahalli. Our trainers are highly experienced professionals. Currently, they are all working in top rated MNCs and Corporates, carrying years of real time industry experience in their particular technologies. In this Hadoop training in Marathahalli, you will be experiencing a differentiated learning environment. Our Hadoop syllabus includes Hadoop installation, MapReduce algorithms, MongoDB, HDFS, Flume, Zookeeper, SQOOP and lot more. For the detailed Hadoop course syllabus, please check below.
Usually, our Hadoop training sessions are scheduled during weekday mornings (7AM – 10AM), weekday evenings (7PM – 9:30PM) and weekends (flexible timings). We do provide Hadoop classroom training and Hadoop online training, both on weekdays and weekends based upon the student’s preferred time slots.
You will surely enhance your technical skills and confidence with this Hadoop training. Our connections and networks in the job market will help you to achieve your dream job easily. Compared to other training institutes, we are offering the best Hadoop course in Marathahalli, Bangalore, where you can get the best Hadoop training and placement guidance for reasonable and affordable cost.
Hadoop Training in Marathahalli Syllabus
Session 1 : Introduction to Big Data
- Importance of Data
- ESG Report on Analytics
- Big Data & It’s Hype
- What is Big Data?
- Structured vs Unstructured data
- Definition of Big Data
- Big Data Users & Scenarios
- Challenges of Big Data
- Why Distributed Processing?
Session 2: Hadoop
- History Of Hadoop
- Hadoop Ecosystem
- Hadoop Animal Planet
- When to use & when not to use Hadoop
- What is Hadoop?
- Key Distinctions of Hadoop
- Hadoop Components/Architecture
- Understanding Storage Components
- Understanding Processing Components
- Anatomy Of a File Write
Anatomy of a File Read
Session 3 : Understanding Hadoop Cluster
- Handout discussion
- Walkthrough of CDH setup
- Hadoop Cluster Modes
- Hadoop Configuration files
- Understanding Hadoop Cluster configuration
- Data Ingestion to HDFS
Session 4 – MapReduce
- Meet MapReduce
- Word Count Algorithm – Traditional approach
- Traditional approach on a Distributed system
- Traditional approach – Drawbacks
- MapReduce approach
- Input & Output Forms of a MR program
- Map, Shuffle & Sort, Reduce Phases
- Workflow & Transformation of Data
- Word Count Code walkthrough
Session 5 – MapReduce
- Input Split & HDFS Block
- Relation between Split & Block
- MR Flow with Single Reduce Task
- MR flow with multiple Reducers
- Data locality Optimization
- Speculative Execution
Session 6 – Advanced MapReduce
- Hadoop Data Types
- Custom Data Types
- Input Format & Hierarchy
- Output Format & Hierarchy
- Side Data distribution – Distributed cache
Session 7 – Advanced MapReduce
- Map side Join using Distributed cache
- Reduce side Join
- MR Unit – An Unit testing framework
Session 8 – Mockup Interview Session
Session 9 – Pig
- What is Pig?
- Why Pig?
- Pig vs Sql
- Execution Types or Modes
- Running Pig
- Pig Data types
- Pig Latin relational Operators
- Multi Query execution
- Pig Latin Diagnostic Operators
Session 10 – Pig
- Pig Latin Macro & UDF statements
- Pig Latin Commands
- Pig Latin Expressions
- Pig Functions
- Pig Latin File Loaders
- Pig UDF & executing a Pig UDF
Session 11 – Hive
- Introduction to Hive
- Pig Vs Hive
- Hive Limitations & Possibilities
- Hive Architecture
- Hive Data Organization
- Hive QL
- Sql vs Hive QL
- Hive Data types
- Data Storage
- Managed & External Tables
Session 12 – Hive
- Partitions & Buckets
- Storage Formats
- Built-in Serdes
- Importing Data
- Alter & Drop Commands
- Data Querying
Session 13 – Hive
- Using MR Scripts
- Hive Joins
- Sub Queries
Session 13 – Resume Preparation
Session 14 – HBase & Introduction to MongoDB
- Introduction to NoSql & HBase
- Row & Column oriented storage
- Characteristics of a huge DB
- What is HBase?
- HBase Data-Model
- HBase vs RDBMS
- HBase architecture
- HBase in operation
- Loading Data into HBase
- HBase shell commands
- HBase operations through Java
- HBase operations through MR
- Introduction to MongoDB
- Basic Commands used in it
Session 15 – ZooKeeper & Oozie
- Introduction to Zookeeper
- Distributed Coordination
- Zookeeper Data Model
- Zookeeper Service
- Zookeeper in HBase
- Introduction to Oozie
- Oozie workflow
Session 16 – Sqoop & Flume
- Introduction to Sqoop
- Sqoop design
- Sqoop Commands
- Sqoop Import & Export Commands
- Sqoop Incremental load Commands
- Introduction to Flume
- Architecture & its Components
- Flume Configuration & Interceptors
Session 17 – Hadoop 2.0 & YARN
- Hadoop 1 Limitations
- HDFS Federation
- NameNode High Availability
- Introduction to YARN
- YARN Applications
- YARN Architecture
- Anatomy of an YARN application
Session 18 – Hands On Using Ubuntu
- Installing Hadoop 2.2 on the Ubuntu
- Installing Eclipse and Maven
- Setting up the configuration files
- Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
- Installation of NoSql database – HBase
- Hadoop Commands
Session:19 Introduction to Spark
- What is Big Data?
- What is Spark?
- Why Spark?
- Spark Ecosystem
- A note about Scala
- Why Scala?
- MapReduce vs Spark
- Hello Spark!
Session 20 – Project Discussion
- Java to MapReduce Conversion
- MapReduce Project
Session 21 – Project Discussion
- Hive Project
- Pig Project
Session 22 – Mockup Interview Session
Bigdata - Hadoop Interview Questions
- What is meant by under replicated blocks?
- Write the HDFS command to get the hadoop fs statistics.
- What is the difference between put and copyFromLocal hdfs commands? Give an example.
- What is called as safe-mode in namenode. When the namenode goes to safe mode. Give the command to turn on and turn off the safe mode.
- What is called as speculative execution?
- What is the communication channel between client and Namenode/datanode?
- If there are 10 HDFS blocks to be copied from one machine to another. However, the other machine can copy only 7.5 blocks, is there a possibility for the blocks to be broken down during the time of replication?
- What is difference between managed and external tables in Hive?
- Can we manually insert 5 rows hive table? If so, explain the process an working of inserting operation in hive.
- What is a metastore in Hive?
- Is it possible to access hadoop hdfs from hive shell?
- What is the difference between partitioning and bucketing in hive?
- What are the various diagnostic operators available in Apache Pig? Explain each.
- What are the different execution modes available in Pig?
- Does pig support parameterization?
- How can you execute a free form SQL query in Sqoop to import the rows in a sequential manner?
- What is the use of sqoop eval tool?
- I have around 300 tables in a database. I want to import all the tables from the database except the tables named Table298, Table 123, and Table299. How can I do this without having to import the tables one by one?
- I have 20000 records in a table. I want copy them to two separate files (records equally distributed) into HDFS (using Sqoop). How do we achieve this, if table does not have primary key or unique key?
- If the source data gets updated every now and then how will you synchronise the data in HDFS that is imported by Sqoop?