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Best Big Data Training in Bangalore
Bigdata Hadoop Training program at SDLC Training is designed to give participants the skills & knowledge to gain a competitive advantage in starting / enhancing a career in Bigdata Hadoop industry. Participants receive up-to-date training in multiple areas in Bigdata Hadoop and a thorough understanding of real-world projects.
SDLC Training is one of the pioneers in software training. We delivered the training for many individuals, groups, and corporations. We prioritize to give our best quality of training and further assistance. You have complete freedom to customize course topics and time. Online training is a good experience and a generation ahead. Register yourself for unbelievable prices.
The Bigdata Hadoop course has been constituted towards the needs of current industry standards. While we will continue to emphasize our own basic academics, we are also aware that our students require Bigdata Hadoop competencies that enhance employment and livelihood opportunities. Clearly the perspective of will be to continuously demonstrate the quality in the delivery of technology.
Apache Hadoop enables organizations to analyze massive volumes of structured and unstructured data and is currently very hot trend across the software tech industry. Hadoop will be adopted as default enterprise data hub by most of the enterprise soon.
This course will provide you an excellent kick start in building your fundamentals in developing big data solutions using Hadoop platform and its ecosystem tools. The course is well balanced between theory and hands-on lab (more than 15 lab exercises) spread on real world uses cases like retail data analysis, sentiment analysis, log analysis, real time trend analysis etc.
We are committed to provide high quality Bigdata Hadoop Training in Bangalore that helps the students and professionals in areas of Bigdata Hadoop through its innovative programs and outstanding faculty.
Post Successful completion of Bigdata Hadoop Training Program leads to placement assistance and participation in campus placements by SDLC.
Who Should Attend?
Architects and developers, who wish to write, build and maintain Apache Hadoop jobs.
The participants should have basic knowledge of java, SQL and Linux. It is advised to refresh these skills to obtain maximum benefit from this workshop.
What participants will learn?
The attendees will learn below topics through lectures and hands-on exercises:
- Understand Big Data, Hadoop 2.0 architecture and its Ecosystem
- Deep Dive into HDFS and YARN Architecture
- Writing map reduce algorithms using java APIs
- Advanced Map Reduce features & Algorithms
- How to leverage Hive & Pig for structured and unstructured data analysis
- Data import and export using Sqoop and Flume and create workflows using Oozie
- Hadoop Best Practices, Sizing and capacity planning
- Creating reference architectures for big data solutions
Our Training Process
Who should join this Course
People who want to make a career in Bigdata Hadoop or just want to upgrade your skills by learning Bigdata Hadoop should definitely join this course. Either you are a student or an IT professional or someone who is looking for a job, Our best Bigdata Hadoop Training in Bangalore will not just fit in your budget but will also convert you into a professional Bigdata Hadoop developer/programmer.
MODULE 1- INTRODUCTION TO BIGDATA
- Evolution of Big Data
- The 6 V’s challenges of traditional technology
- Comparison of Big Data with Traditional technologies
- What is Big Data?
- Examples of Big Data
- Reasons for Big Data Evolution
- Why Big Data deserves your attention
- Use cases of Big Data
- Different ways of analytical techniques using Bigdata
MODULE 2- INTRODUCTION TOHADOOP
- What is Hadoop
- History of Hadoop
- Hadoop Ecosystem
- Problems with Traditional Large-Scale Systems and Need for Hadoop
- Understanding Hadoop Architecture
- Fundamental of HDFS (Blocks, Name Node, Data Node, Secondary Name Node)
- Rack Awareness
- Read/Write from HDFS
- HDFS Federation and High Availability
MODULE 3- STARTING HADOOP
- Setting up single node Hadoop cluster(Pseudo mode)
- Understanding Hadoop configuration files
- Hadoop Components- HDFS, MapReduce
- Overview Of Hadoop Processes
- Overview Of Hadoop Distributed File System
- The building blocks of Hadoop
- Hands-On Exercise: Using HDFS commands
MODULE 4- MAPREDUCE-1(MR V1)
- Understanding Map Reduce
- Job Tracker and Task Tracker
- Architecture of Map Reduce
- Map Function
- Reduce Function
- Data Flow of Map Reduce
- How Map Reduce Works
- Anatomy of Map Reduce Job (MR-1)
- Submission & Initialization of Map Reduce Job
- Assigning & Execution of Tasks
- Monitoring & Progress of Map Reduce Job
- Hadoop Writable and Comparable
- Map Reduce Types and Formats
- Understand Difference Between Block and Input Split
- Role of Record Reader
- Different File Input Formats
- Map Reduce Joins
MODULE 5- MAPREDUCE-2(YARN)
- Limitations of Current Architecture
- YARN Architecture
- Application Master, Node Manager&Resource Manager
- Job Submission and Job Initialization
- Task Assignment and Task Execution
- Progress and Monitoring of the Job
- Failure Handling in YARN
- Task Failure
MODULE 6- HIVE
- Introduction to Apache Hive
- Architecture of Hive
- Installing Hive
- Hive data types
- Types of Tables in Hive
- Buckets& Sampling
- Executing hive queries from Linux terminal
- Executing hive queries from a file
- Creating UDFs in HIVE
- Hands-On Exercise
MODULE 7- PIG
- Introduction to Apache Pig
- Install Pig
- Data types
- Working with various PIG Commands covering all the functions in PIG
- Working with un-structured data
- Working with Semi-structured data
- Creating UDFs
- Hands-On Exercise
MODULE 8- SQOOP
- Introduction to SQOOP& Architecture
- Installation of SQOOP
- Import data from RDBMS to HDFS
- Importing Data from RDBMS to HIVE
- Exporting data from HIVE to RDBMS
- Hands on exercise
MODULE 9- HBASE
- Introduction to HBASE
- Installation ofHBASE
- Exploring HBASE Master & Region server
- Exploring Zookeeper
- CRUD Operation of HBase with Examples
- HIVE integration with HBASE
- Hands on exercise on HBASE
MODULE 10- OVERVIEW SESSIONS ON
MODULE 11- FAQS, REAL TIME ENVIRONMENT & REAL TIME SCENARIOS
- Real Time Q/A
- Real Time Environment
MODULE 12-REAL TIME PROJECT
- Working on Real Time Project
We will be providing raw data & requirements for the project & you will have to work. Finally we will have one Project execution session where we will be explaining the steps for execution.
|Demo Class : Free Demo Session, Flexible Timings||Free Class : Attend 3 Free Classes to check training Quality|
|Regular : 2 Hour per day||Fast Track : 2 – 3 Hours per day: 10 days|
|Weekdays : Available||Weekend : Available|
|Online Training : Available||Class Room Training : Available|
|Course Fee : Talk to our Customer Support||Duration : 30 Hours|
How will I do the Lab Practice?
We have the technically updated lab to give you the best hands-on project experience.
Who are the instructors?
Our instructors were the best industry and domain knowledge professionals with 5+ years of experience in BigData Hadoop training in Bangalore.
What if I miss a class?
We will provide you the backup classes if you miss any session. You can continue the missed classes from next batch.
How can I request for a demo class?
You can either walk-in to our SDLC training institute in Marathahalli, or you can send the query to us from the website then we can arrange the Bigdata Hadoop training demo session for you.
What are the payment options?
You can pay directory or you can transfer the money online. We also accept cards.
Will I get the required software from institute?
Definitely you can get or access the software from our server or we can provide the required software to you depending on the course.
Is there any offer or discount I can avail?
Yes, you can find the best offers and discounts which are vary time to time you can check with us.
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SDLC Training is an IT training institute that offers tailor-made courses to students and corporates who intend to hone their IT Skills. Our training portfolio covers programs that span across the Software Development Life Cycle, and hence the name.
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