Big Data/Hadoop Training
Learn Big Data/Hadoop and become an expert in the fastest growing technology which has taken data storage and processing to the next level.
- Our extensive 40+ hours of Big Data/Hadoop course cover both basic and advanced topics to help you become an expert Hadoop Professional.
- By attending our Big Data/Hadoop online course, you will understand the core concepts of Big Data and Hadoop, and will be proficient in Hadoop Distributed File System (HDFS), MapReduce, Pig, Hive, HBase, SQOOP, and Flume.
- We provide you with assignments, training material and recorded videos for a complete learning experience.
- Our Instructors are working professionals who give you real world knowledge and examples, and are the best in their field.
Hadoop is a revolutionary open-source framework for software programming that took the data storage and processing to next level. With its tremendous capability to store and process huge clusters of data, it unveiled opportunities to business around the world with artificial intelligence. It stores data and runs applications on clusters in commodity hardware which massively reduces the cost of installation and maintenance. It provides huge storage for any kind of data, enormous processing power and to have all kinds of analytics such as real-time analytics, predictive analytics data and so on at a click of a mouse.
The volume of data handled by organizations keeps growing exponentially with each passing day! This ever-demanding scenario calls for powerful big data handling solutions such as Hadoop for a truly data-driven decision-making approach.
Acquiring proper training on Hadoop technology would be a boon to professionals in terms of using Hadoop resources effectively and save huge time and effort.
Big Data/ Hadoop course is for Students/Non-IT beginners who want to become an expert in the fastest growing technology.
- Architects, Software Administrators, Java(Any IT) Developers and testers who want to build effective data processing applications by querying Apache Hadoop.
- Technical Managers involved in the development process also take active participation in Hadoop Developer classes.
- Business Analysts, Database Administrators, and SQL Developers
- Software Engineers with a background in ETL/Programming and Managers dealing with latest technologies and data management.
- .NET Developers and Data Analysts who develop applications and perform big data analysis using the Hortonworks Data Platform for Windows will also find this helpful.
- Leading multinational companies are hiring for Hadoop technology – Big Data & Hadoop market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 (Forbes).
- Streaming Job Opportunities – McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts (McKinsey Report).
- Hadoop skills will boost salary packages – Average annual salary of Big Data Hadoop Developers is around $135k (Indeed.com Salary Data).
- Future of Big Data and Hadoop looks bright – The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data is generated.
A Hadoop Developer is responsible for programming and development of business applications and software on the Hadoop Platform. A Hadoop Developer is also involved in designing, developing, installing, configuring, and maintaining the Hadoop application as well as performing analysis.
Students who start as Hadoop Developers evolve into Hadoop Administrators by the end of a certification course and in the process, guarantee a bright future.
Become a certified Hadoop professional to bag a dream job offer.
- Our Big Data/Hadoop course is an instructor-led live online class.
- Our online course covers everything from Introduction to Big Data and Hadoop to advanced topics to help you become proficient in Big Data/Hadoop.
- A detailed explanation and practical examples with special emphasis on HDFS and MapReduce.
- What is Apache Spark, and understanding its Architecture.
- Introduction to Spark-core, understanding the Basic component of Spark -RDD
- Creating RDDs, Operations in RDD
- Creating functions in Spark and passing parameters
- Understanding RDD Transformations and Actions, Understanding RDD Persistence and Caching
- Examples for RDDs
- Examples of Spark SQL
- Work on Pig, Apache Hive, Apache HBase, and various other Big Data Hadoop related topics in an easy to understand manner.
- Hands on assignments for thorough understanding of concepts.
- Real-time Proof of Concept.
- Included in our training are free Core Java class videos.
- Practice on software tools to gain hands-on experience.
- Work on real time project related scenarios and examples to give you the feel of real work environment.
- Group discussions, Mock interview sessions, and Interview questions to prepare you to attend interviews confidently.
- Access to Instructor through email to address any questions.
- Lifetime access to the Big Data/Hadoop course to help you get comfortable with all the concepts and information.
- Training Material and Recorded Videos for a complete learning experience.
- Guidance on Interview and Resume Preparation.
Try our Free Demo Session Today!
Read more about why you should do your course from H2K Infosys here
- What is Big Data?
- What are the challenges for processing big data?
- What technologies support big data?
- What is Hadoop?
- Why Hadoop?
- History of Hadoop
- Use cases of Hadoop
- RDBMS vsHadoop
- When to use and when not to use Hadoop
- Ecosystem tour
- Vendor comparison
- Hardware Recommendations & Statistics
Significance of HDFS in Hadoop
- Features of HDFS
- 5 daemons of Hadoop
- Name Node and its functionality
- Data Node and its functionality
- Secondary Name Node and its functionality
- Job Tracker and its functionality
- Task Tracker and its functionality
- Data Storage in HDFS
- Introduction about Blocks
- Data replication
- Accessing HDFS
- CLI (Command Line Interface) and admin commands
- Java Based Approach
- Fault tolerance
- Download Hadoop
- Installation and set-up of Hadoop
- Start-up & Shut down process
- HDFS Federation
- Map Reduce Story
- Map Reduce Architecture
- How Map Reduce works
- Developing Map Reduce
- Map Reduce Programming Model
- Different phases of Map Reduce Algorithm
- Different Data types in Map Reduce
- how Write a basic Map Reduce Program
- Driver Code
- Creating Input and Output Formats in Map Reduce Jobs
- Text Input Format
- Key Value Input Format
- Sequence File Input Format
- Data localization in Map Reduce
- Combiner (Mini Reducer) and Partitioner
- Hadoop I/O
- Distributed cache
- Introduction to Apache Pig
- Map Reduce Vs. Apache Pig
- SQL vs. Apache Pig
- Different data types in Pig
- Modes of Execution in Pig
- Grunt shell
- Loading data
- Exploring Pig
- Latin commands
- Hive introduction
- Hive architecture
- Hive vs RDBMS
- HiveQL and the shell
- Managing tables (external vs managed)
- Data types and schemas
- Partitions and buckets
- Architecture and schema design
- HBase vs. RDBMS
- HMaster and Region Servers
- Column Families and Regions
- Write pipeline
- Read pipeline
- HBase commands
- Sqoop syntax
- Database connection
- Importing data
- Flume syntax
- Database connection
- Importing data
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