Big DATA / Hadoop
H2K Infosys is offering comprehensive BIG DATA online training, it is essential for those who wish to make a career in BIG DATA. It is mandatory for candidates who have knowledge on object oriented languages.
Is Big Data / Hadoop the big new word on the data streets? Of-course with the nature of IT world and applications evolving data has become the key resource. Every data in the various formats that it comes can be useful for the business. However, this isn’t easy. It comes with challenges like: single machine dependency, excessive amount of processing to fit this data into traditional Databases, performance issues, systems not being immune to failures etc.
Hadoop framework solves all this and brings in a level of efficiency and transparency into big data. Hadoop uses parallel and distributed processing to store data in multiple clusters across many systems. It is scalable, cheaper, faster, fault tolerant among various other things.
Hadoop has a huge scope for evolution and the demand for Big Data Hadoop professionals is and will be on a rise. Our Hadoop Big DATA course is just what you need to make you one of them.
Hadoop is an open source computing framework that processes large data sets, in a distributed computing environment. High volume of data can be processed with ease by using parallel computing. Parallel computing is the back bone of Hadoop. Map Reduce development infrastructure, enables us to read, write and perform basic aggregation like operation on the stored data, in batches.
Hadoop File System (HDFS) was made to store the huge amount of data. Companies like Yahoo, Facebook, Amazon, NYSE, LinkedIn, eBay, Sears and Walmart generate 30000 gigabytes of data every second. This data is now being used to generate various analytics. So it has to be stored. Traditional databases cannot handle data of this size. The Map Reduce infrastructure helps to run different kinds of diagnostics on them.
- Teacher led online interactive sessions on Hadoop that covers everything starting from what is not working with the existing systems and how to achieve and maintain a practical implementation of it.
- A detailed explanation and practical examples with special emphasis on HDFS and MapReduce.
- Pig, Apache Hive, Apache HBase, and various other Big Data Hadoop related projects are going to be dealt with in an easy to understand manner.
- Resume, job and interview guidance.
- Recorded session to make reviewing easy.
- Hands on assignments for thorough understanding of concepts.
- Practical real time examples.
- Study material to make the learning experience complete.
- Offline support from the faculty via chat or email for clarifications.
H2Kinfosys Hadoop Big Data Training Advantages:
- Instructor Led - Face2Face True Live Online class
- Core Java Class videos are also free.
- More interaction with student to faculty and student to student.
- Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
- Mock interviews / group discussions / interview related questions.
- Test Lab is in Cloud Technology - to practice on software tools if needed.
- Pay one time fee & attending live classes multiple times until student is comfortable with every topic.
- Work on real time project related examples.
- The teaching methods / tools / topics we chosen are based on the current competitive job market.
- More H2Kinfosys training Advantages.
- 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
Please fill the contact form, we can email you the details.