- Write MapReduce programs and deploy Hadoop clusters
- Learn the Hadoop Architecture and Hadoop basics for beginners
- Develop YARN programs on the Hadoop 2.X version
- Integrate MapReduce and HBase to do advanced usage and Indexing
- Understand RDD in Apache Spark
- Learn what is Hadoop, HDFS and MapReduce framework
- Develop applications for Big Data using Hadoop Technology
- Work on Big Data analytics using Hive, Pig and YARN
- Learn fundamentals of Spark framework and its working
- Learn Hadoop development best practices
What is Hadoop and Big Data?
This Apache Hadoop Developer Certification Training will help you get a detailed idea about Big Data and Hadoop. Some of the topics included are introduction to the Hadoop ecosystem, understanding of HDFS and MapReduce including MapReduce abstraction. Learn to install, implement various components of Hadoop like Pig, Hive, Flume, Sqoop and YARN.
Hadoop does not require much coding. All you have to do is enrol in a Hadoop certification course and learn Pig and Hive, both of which require only the basic understanding of SQL Financial services companies use analytics to assess risk, build investment models, and create trading algorithms, Hadoop has been used to help build and run those applications. Retailers use it to help analyse structured and unstructured data to better understand and serve their customers. In this Big Data course, you will master MapReduce, Hive, Pig, Sqoop, Oozie and Flume and work with Amazon EC2 for cluster setup, Spark framework and RDD, Scala and Spark SQL, Machine Learning using Spark, Spark Streaming, etc.
Hadoop Development Training Demo
Hadoop Development Course Curriculum
- Hadoop 2.x Cluster Architecture
- Federation and High Availability
- A Typical Production Cluster setup
- Hadoop Cluster Modes
- Common Hadoop Shell Commands
- Hadoop 2.x Configuration Files
- Cloudera Single node cluster
- How Mapreduce Works
- How Reducer works
- How Driver works
- Combiners
- Practitioners
- Input Formats
- Output Formats
- Shuffle and Sort
- Mapside Joins
- Reduce Side Joins
- MRUnit
- Distributed Cache
- What is Big Data
- Where does Hadoop fit in
- Hadoop Distributed File System – Replications
- Block Size
- Secondary Namenode
- High Availability
- Understanding YARN – ResourceManager
- NodeManager
- Difference between 1.x and 2.x
What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,
- Introduction to Pig
- Deploying Pig for data analysis
- Pig for complex data processing
- Performing multi-dataset operations
- Extending Pig
- Pig Jobs
- Hive Introduction
- Hive for relational data analysis
- Data management with Hive
- Optimization of Hive
- Extending Hive
- Hands on Exercises – working with large data sets and extensive querying
- UDF, query optimization
- Selecting a File Format
- Tool Support for File Formats
- Avro Schemas
- Using Avro with Hive and Sqoop
- Avro Schema Evolution
- Compression
- What is Hbase
- Where does it fits
- What is NOSQL
- Multi Node Cluster Setup using Amazon ec2
- Creating 4 node cluster setup
- Running Map Reduce Jobs on Cluster
- Delving Deeper Into The Hadoop API
- More Advanced Map Reduce Programming
- Joining Data Sets in Map Reduce
- Graph Manipulation in Hadoop