A perfect blend of in-depth Hadoop theoretical knowledge and strong practical skills via implementation of real-time Hadoop projects to give you a headstart and enable you to bag top Hadoop jobs in the Big Data industry.
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Why should you learn Hadoop?
Upcoming Batches for this Hadoop Course
Limited seats available
Pick a time that suits you and grab your seat now in the best Big Data Hadoop Certification Training Course.
|Self-Pacedv/s Live Course||Whenever you’d like||40 Hrs||
Rs. 4990 | $91
|13 Jul – 18 Aug||8.00 PM – 11.00 PM IST (Sat-Sun)||40 Hrs||
Rs. 12990 | $236
|15 Jul – 9 Aug||09.00 PM – 11.00 PM IST (Mon-Fri)||40 Hrs||
Rs. 12990 | $236
|3 Aug – 8 Sept||10.00 AM – 01.00 PM IST (Sat-Sun)||40 Hrs||
Rs. 12990 | $236
What will you take home from this Big Data Hadoop Online course?
- Shape your career as Big Data shapes the IT World
- Grasp concepts of HDFS and MapReduce
- Become adept in the latest version of Apache Hadoop
- Develop a complex game-changing MapReduce application
- Perform data analysis using Pig and Hive
- Play with the NoSQL database Apache HBase
- Acquire an understanding of the ZooKeeper service
- Load data using Apache Sqoop and Flume
- Enforce best practices for Hadoop development and deployment
- Master handling of large datasets using the Hadoop ecosystem
- Work on live Big Data projects for hands-on experience
- Comprehend other Big Data technologies like Apache Spark
What to do before you begin your Hadoop online training?
Although if you’d like, you can brush up on your Java skills with our complementary Java course right in your LMS.
Hadoop Training Course Curriculum
- What is Big Data
- Necessity of Big Data and Hadoop in the industry
- Paradigm shift - why the industry is shifting to Big Data tools
- Different dimensions of Big Data
- Data explosion in the Big Data industry
- Various implementations of Big Data
- Different technologies to handle Big Data
- Traditional systems and associated problems
- Future of Big Data in the IT industry
- Why Hadoop is at the heart of every Big Data solution
- Introduction to the Big Data Hadoop framework
- Hadoop architecture and design principles
- Ingredients of Hadoop
- Hadoop characteristics and data-flow
- Components of the Hadoop ecosystem
- Hadoop Flavors – Apache, Cloudera, Hortonworks, and more
Setup and Installation of single-node Hadoop cluster
- Hadoop environment setup and pre-requisites
- Hadoop Installation and configuration
- Working with Hadoop in pseudo-distributed mode
- Troubleshooting encountered problems
Setup and Installation of Hadoop multi-node cluster
- Hadoop environment setup on the cloud (Amazon cloud)
- Installation of Hadoop pre-requisites on all nodes
- Configuration of masters and slaves on the cluster
- Playing with Hadoop in distributed mode
- What is HDFS (Hadoop Distributed File System)
- HDFS daemons and architecture
- HDFS data flow and storage mechanism
- Hadoop HDFS characteristics and design principles
- Responsibility of HDFS Master – NameNode
- Storage mechanism of Hadoop meta-data
- Work of HDFS Slaves – DataNodes
- Data Blocks and distributed storage
- Replication of blocks, reliability, and high availability
- Rack-awareness, scalability, and other features
- Different HDFS APIs and terminologies
- Commissioning of nodes and addition of more nodes
- Expanding clusters in real-time
- Hadoop HDFS Web UI and HDFS explorer
- HDFS best practices and hardware discussion
- What is MapReduce, the processing layer of Hadoop
- The need for a distributed processing framework
- Issues before MapReduce and its evolution
- List processing concepts
- Components of MapReduce – Mapper and Reducer
- MapReduce terminologies- keys, values, lists, and more
- Hadoop MapReduce execution flow
- Mapping and reducing data based on keys
- MapReduce word-count example to understand the flow
- Execution of Map and Reduce together
- Controlling the flow of mappers and reducers
- Optimization of MapReduce Jobs
- Fault-tolerance and data locality
- Working with map-only jobs
- Introduction to Combiners in MapReduce
- How MR jobs can be optimized using combiners
- Anatomy of MapReduce
- Hadoop MapReduce data types
- Developing custom data types using Writable & WritableComparable
- InputFormats in MapReduce
- InputSplit as a unit of work
- How Partitioners partition data
- Customization of RecordReader
- Moving data from mapper to reducer – shuffling & sorting
- Distributed cache and job chaining
- Different Hadoop case-studies to customize each component
- Job scheduling in MapReduce
- The need for an adhoc SQL based solution – Apache Hive
- Introduction to and architecture of Hadoop Hive
- Playing with the Hive shell and running HQL queries
- Hive DDL and DML operations
- Hive execution flow
- Schema design and other Hive operations
- Schema-on-Read vs Schema-on-Write in Hive
- Meta-store management and the need for RDBMS
- Limitations of the default meta-store
- Using SerDe to handle different types of data
- Optimization of performance using partitioning
- Different Hive applications and use cases
- The need for a high level query language - Apache Pig
- How Pig complements Hadoop with a scripting language
- What is Pig
- Pig execution flow
- Different Pig operations like filter and join
- Compilation of Pig code into MapReduce
- Comparison - Pig vs MapReduce
- NoSQL databases and their need in the industry
- Introduction to Apache HBase
- Internals of the HBase architecture
- The HBase Master and Slave Model
- Column-oriented, 3-dimensional, schema-less datastores
- Data modeling in Hadoop HBase
- Storing multiple versions of data
- Data high-availability and reliability
- Comparison - HBase vs HDFS
- Comparison - HBase vs RDBMS
- Data access mechanisms
- Work with HBase using the shell
- The need for Apache Sqoop
- Introduction and working of Sqoop
- Importing data from RDBMS to HDFS
- Exporting data to RDBMS from HDFS
- Conversion of data import/export queries into MapReduce jobs
- What is Apache Flume
- Flume architecture and aggregation flow
- Understanding Flume components like data Sources and Sinks
- Flume channels to buffer events
- Reliable & scalable data collection tools
- Aggregating streams using Fan-in
- Separating streams using Fan-out
- Internals of the agent architecture
- Production architecture of Flume
- Collecting data from different sources to Hadoop HDFS
- Multi-tier Flume flow for collection of volumes of data using AVRO
- The need for and the evolution of YARN
- YARN and its eco-system
- YARN daemon architecture
- Master of YARN – Resource Manager
- Slave of YARN – Node Manager
- Requesting resources from the application master
- Dynamic slots (containers)
- Application execution flow
- MapReduce version 2 application over Yarn
- Hadoop Federation and Namenode HA
- Introduction to Apache Spark
- Comparison - Hadoop MapReduce vs Apache Spark
- Spark key features
- RDD and various RDD operations
- RDD abstraction, interfacing, and creation of RDDs
- Fault Tolerance in Spark
- The Spark Programming Model
- Data flow in Spark
- The Spark Ecosystem, Hadoop compatibility, & integration
- Installation & configuration of Spark
- Processing Big Data using Spark
Awesome Big Data projects you’ll get to build in this Hadoop course
Weblogs are web server logs where web servers like Apache record all events along with a remote IP, timestamp, requested resource, referral, user agent, and other such data. The objective is to analyze weblogs to generate insights like user navigation patterns, top referral sites, and highest/lowest traffic-times.
IVR Data Analysis
Learn to analyze IVR(Interactive Voice Response) data and use it to generate multiple insights. IVR call records are meticulously analyzed to help with optimization of the IVR system in an effort to ensure that maximum calls complete at the IVR itself, leaving no room for the need for a call-center.
Sentiment analysis is the analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions in relation to entities such as individuals, products, events, services, organizations, and topics. It is achieved by classifying the observed expressions as opinions that may be positive or negative.
Titanic Data Analysis
Titanic was one of the most colossal disasters in the history of mankind, and it happened because of both natural events and human mistakes. The objective of this project is to analyze multiple Titanic data sets to generate essential insights pertaining to age, gender, survived, class, and embarked.
Learn to analyze US crime data and find the most crime-prone areas along with the time of crime and its type. The objective is to analyze crime data and generate patterns like time of crime, district, type of crime, latitude, and longitude. This is to ensure that additional security measures can be taken in crime-prone areas.
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Big Data Hadoop Course Reviews
Is this Big Data Hadoop course for you?
Big Data is the truth of today and Hadoop proves to be efficient in processing it. So while anyone can benefit from a career in it, here are the kind of professionals who go for this Hadoop course:
- Software developers, project managers, and architects
- BI, ETL and Data Warehousing professionals
- Mainframe and testing professionals
- Business analysts and analytics professionals
- DBAs and DB professionals
- Professionals willing to learn Data Science techniques
- Any graduate focusing to build a career in Apache Spark and Scala
Still can’t decide? Let our Big Data experts answer your questions
Learn Hadoop the way you like
|Features||Self-Paced Pro Course
Rs. 4990 | $91
|Live Instructor-Led Course
Rs. 12990 | $236
|Course mode||Video Based||Live Online with Trainer|
|Course Objective||Express Learning||Job readiness|
|Extensive hands-on practicals||In recordings & in LMS||Live with instructor & in LMS|
|No. of Projects||One||Five|
|Doubt Clearance||Through discussion forum||In regular sessions|
|Complementary Courses||Java||Java & Storm|
|Discussion Forum Access||✓||✓|
|100% Interactive Live Classes||✗||✓|
|Support for real-life project||✗||✓|
|Complementary Job Assistance||✗||✓|
|Resume & Interview Preparation||✗||✓|
|Personalized career guidance from instructor||✗||✓|
Rs. 4990 | $91
Rs. 12990 | $236
We’re here to help you find the best Hadoop jobs
Once you finish this online Big Data course, our Hadoop job grooming program will help you build your resume while also furthering it to prospective employers. Our mock interviews will help you better understand the interview psychology so you go in prepared.
Big Data and Hadoop Training FAQs
If you miss any session, you need not worry as recordings will be uploaded in LMS immediately as the session gets over. You can go through it and get your queries cleared from the instructor during next session. You can also ask him to explain the concepts that you did not understand and were covered in session you missed. Alternatively you can attend the missed session in any other batch running parallely.
Instructor will help you in setting virtual machine on your own system at which you can do practicals anytime from anywhere. Manual to set virtual machine will be available in your LMS in case you want to go through the steps again. Virtual machine can be set on MAC or Windows machine also.
All the Hadoop training sessions will be recorded and you will have lifetime access to the recordings along with the complete Hadoop study material, POCs, Hadoop project etc.
To attend online Hadoop training, you just need a laptop or PC with a good internet connection of around 1 MBPS (But the lesser speed of 512 KBPS will also work). The broadband connection is recommended but you can connect through data card as well.
If you have any doubts during sessions, you can clear it with the instructor immediately. If you get queries after the session, you can get it cleared from the instructor in the next session as before starting any session, instructor spends around 15 minutes in doubt clearing. Post training, you can post your query over discussion forum and our support team will assist you. Still if you are not comfortable, you can drop mail to instructor or directly interact with him.
Recommended is a minimum of an i3 processor, a 20 GB disk, and 4 GB RAM in order to learn Hadoop, although students have learnt Hadoop on 2 GB RAM as well.
Our training includes multiple workshops, POCs and projects. Those will prepare you to a level where you can start working from day 1 wherever you go. You will be assisted in resume preparation. The mock interviews will help you get ready to face real interviews. We will also guide you with job openings matching your resume. These things will help you get your dream Big Data job in the industry.
You will be skilled with practical and theoretical knowledge that the industry looks for and will become a certified Hadoop professional who is ready to take on Big Data Projects in top organizations.
DataFlair has blend of students from across the globe. Apart from India, we provide Hadoop training in the US, UK, Singapore, Canada, UAE, France, Brazil, Ireland, Indonesia, Japan, Sri Lanka, etc to cover the complete globe.
Both voice and chat will be enabled during the Big Data Hadoop training sessions. You can talk with the instructor or can also interact via chatting.
This is completely online training with a batch size of 10-12 students only. You will be able to interact with trainer through voice or chat and individual attention will be provided to all. The trainer ensures that every student is clear of all the concepts taught before proceeding ahead. So there will be complete environment of classroom learning.
Yes, you will be provided DataFlair Certification. At the end of this course, you will work on a real time Project. Once you are successfully through the project, you will be awarded a certificate.
Big data is the latest and the most demanding technology with continuously increasing demand in the Indian market and abroad. Hadoop professionals are among the highest paid IT professionals today with salary $135k (source: indeed job portal). You can check our blog related to Why should I learn Big Data?
You will be doing real-time Hadoop projects in different domains like retail, banking, and finance, etc. using different technologies like Hadoop HDFS, MapReduce, Apache Pig, Apache Hive, Apache HBase, Apache Oozie, Apache Flume and Apache Sqoop.
The Hadoop course from DataFlair is 100% job oriented that will prepare you completely for interview and Big Data job perspective. Post Big Data course completion, we will provide you assistance in resume preparation and tips to clear Hadoop interviews. We will also let you know for Hadoop jobs across the globe matching your resume.
Yes, you can attend the Hadoop demo class recording on our Big data Hadoop course page itself to understand the quality and level of Big Data training we provide and that creates the difference between DataFlair and other Hadoop online training providers.
Hadoop is one of the hottest career options available today for all the software engineers to boost their professional career. In the US itself there are approximately 12,000 jobs currently for Hadoop developers and demand for Hadoop developers are increasing day by day rapidly far more than the availability.