Hadoop Online Training

Hadoop online training by IT professionals:

Hadoop online training is offered by Prime OnlineTraining facility. We are a team of experts of Hadoop training and have a excellent experience of teaching and training hadoop. Our online Hadoop training center is one of the best IT institute in India. Our students are very happy With our Online Training and they get quick jobs in Europe, Australia, Singapore, UK and USA . Learning Hadoop at your home is a one stop solution to learn technical knowledge with Your flexible Timings.

Hadoop Introduction:

Hadoop is referred as an open source software. By hadoop, large sets of data are transferred among many computers on the server by using simple programming methods. Hadoop is a rapidly evolving ecosystem of components for implementing the Google Map Reduce algorithms in a scalable fashion on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with less scalable solutions or standard SQL-based approaches.

Hadoop is outlined to rise up from a single server to thousands of computers with a very high percentage of availability. Handling failures in the application are determined by the software itself instead of some hardware. Our online course training provides you an ample knowledge of operating the Hadoop software through various challenges in the IT field.

Hadoop online training concepts:

Basics of Hadoop:

  1. what is the Motivation for Hadoop
  2. Large scale system training
  3. Survey of data storage literature
  4. Literature survey of data processing
  5. Overview Of Networking constraints
  6. New approach requirements

Basic concepts of Hadoop

  1. Hadoop Introduction
  2. Distributed file system of Hadoop
  3. Map reduction of Hadoop works
  4. Hadoop cluster and its anatomy
  5. Hadoop demons
  6. Master demons
  7. Name node
  8. Tracking of job
  9. Secondary node detection
  10. Slave daemons
  11. Tracking of task
  12. Hadoop Distributed File System (HDFS)
  13. Spilts and blocks
  14. Input Spilts
  15. HDFS spilts
  16. Replication of data
  17. Awareness of Hadoop racking
  18. High availably of data
  19. Block placement and cluster architecture
  20. Hadoop case studies
  21. Practices & Tuning of performances
  22. Development of mass reduce programs
  23. Local mode
  24. Running without HDFS
  25. Pseudo-distributed mode
  26. All daemons running in a single mode
  27. Fully distributed mode
  28. Dedicated nodes and daemon running

Hadoop administration

  1. Setup of Hadoop cluster
  2. Cluster of a Hadoop setup.
  3. Configure and Install Apache Hadoop on a multi node cluster.
  4. In a distributed mode, configure and install Cloud era distribution.
  5. In a fully distributed mode, configure and install Horton works distribution
  6. In a fully distributed mode, configure the Green Plum distribution.
  7. Monitor the cluster
  8. Get used to the management console of Horton works and Cloud era.
  9. Name the node in a safe mode
  10. Data backup.
  11. Case studies
  12. Monitoring of clusters

Hadoop Development :

  1. What is MapReduce Program
  2. Sample the mapreduce program.
  3. API concepts and their basics
  4. Driver code
  5. Mapper
  6. Reducer
  7. Hadoop AVI streaming
  8. Performing several Hadoop jobs
  9. Configuring close methods
  10. files Sequencing
  11. Record reading
  12. Record writer
  13. Reporter and its role
  14. Counters
  15. Output collection
  16. Assessing HDFS
  17. Tool runner
  18. Use of distributed CACHE
  19. Several MapReduce jobs (In Detailed)
    • SEARCH USING MAPREDUCE
    • GENERATING THE RECOMMENDATIONS USING MAPREDUCE
    • PROCESSING THE LOG FILES USING MAPREDUCE
  20. Mapper Identification
  21. Reducer Identification
  22. Exploring the problems using this application
  23. Debugging the MapReduce Programs
  24. MR unit testing
  25. Logging
  26. Debugging strategies
  27. Advanced MapReduce Programming
  28. Secondary sort
  29. Output and input format customization
  30. Mapreduce joins
  31. Monitoring & debugging on a Production Cluster
  32. Counters
  33. Skipping Bad Records
  34. Running the local mode
  35. MapReduce performance tuning
  36. Reduction network traffic by combiner
  37. Partitioners
  38. Reducing of input data
  39. Using Compression
  40. Reusing the JVM
  41. Running speculative execution
  42. Performance Aspects
  43. CASE STUDIES

CDH4 Enhancements :
1. Name Node Availability
2. Name Node federation
3. Fencing
4. MapReduce

HADOOP ANALYST
1. Hive Concepts
2. Hive and its architecture
3. Install and configure hive on cluster
4. Type of tables in hive
5. Functions of Hive library
6. Buckets
7. Partitions
8. Joins ( Inner joins and Outer Joins )
9. Hive UDF

PIG
1.Basics Of Pig
2. Install and configure PIG
3. PIG Library Functions
4. Pig Vs Hive
5. Writing of sample Pig Latin scripts
6. Modes of running
1. Grunt shell
2. Java program
7. PIG UDFs
8. Macros of Pig
9. Debugging the PIG

IMPALA
1. Difference between Pig and Impala Hive
2. Does Impala give good performance?
3. Exclusive features
4. Impala and its Challenges
5. Use cases

NOSQL
1. Introduction to HBase
2. Explain HBase concepts
3. Overview Of HBase architecture
4. Server architecture
5. File storage architecture
6. Column access
7. Scans
8. HBase cases
9. Installation and configuration of HBase on a multi node
10. Create database, Develop and run sample applications
12. Access data stored in HBase using clients like Python, Java and Pearl
13. Map Reduce client
14. HBase and Hive Integration
15. HBase administration tasks
16. Defining Schema and its basic operations.
17. Cassandra Basics
18. MongoDB Basics

Ecosystem Components
1. Sqoop
2. Configure and Install Sqoop
3. Connecting RDBMS
4. Installation of Mysql
5. Importing the data from Oracle/Mysql to hive
6. Exporting the data to Oracle/Mysql
7. Internal mechanism

Oozie
1. Oozie and its architecture
2. XML file
3. Install and configuring Apache
4. Work flow Specification
5. Action nodes
6. Control nodes
7. Job coordinator
Avro, Scribe, Flume, Chukwa, Thrift
1. Concepts of Flume and Chukwa
2. Use cases of Scribe, Thrift and Avro
3. Installation and configuration of flume
4. Creation of a sample application

Challenges of Hadoop
1. Hadoop recovery
2. Hadoop suitable cases.

HIGHLIGHTS

  • 100% Certification Assurance
  • Big Data University (Ibm) Certification Free
  • Technical Support
  • Interview Questions
  • Sample Resumes

Our Hadoop Online Training batches start every week and we accommodate your flexible timings.

PrimeTrainings provides online training for various courses like SAP All Modules, Oracle 11g DBAAndroidASP.Net  …

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