Best Hadoop Training in Bangalore by Industry Experts
Hadoop Training & Certification
Duration of Training : 32 hrs
- 16 to 32 nodes Hadoop Cluster Building Setup on High End Enterprise Cisco UCS Blade Servers. We build real cluster setup from the scratch.
- Hadoop Cluster Build on Server with 144 CPU and 384 GB RAM
- Real Time Hadoop Trainer.
- Complete hands own training.
- 100% Practical Guaranteed.
Hadoop Certifications : Radical is accredited with Pearson Vue and Kriterion , We do conduct Exams in every month and we have 100% Passing record for all the students who completed course form Radical technologies .Most demanding Hadoop Exams areHortonworks and Cloudera certifications .
Exam Preparation : After the course we provide for all our candidates free exam preparation session , which will guide them to pass the Respective modules of Hadoop exams.
Registration Process : We never take any registration fee from the candidate without experiencing our training quality. Once you satisfied with the demo , you can register with full payment and avail discount . We have installment facility also.
Placement : Hadoop placement record crossed 500+ as per Jan 2016 record . 30 percent Freshers and 70 percent up-skilled resources placed in different MNC’s and Startups at various locations all over India and Overseas
BIGDATA HADOOP ADMIN SYLLABUS
1.Understanding Big Data and Hadoop
Introduction to big data, limitations of existing solutions
Hadoop architecture, Hadoop components and ecosystem
Data loading & reading from HDFS
Replication rules, rack awareness theory
Hadoop cluster administrator
Roles and responsibilities
2. Hadoop Architecture and Cluster setup
Hadoop server roles and their usage
Hadoop installation and initial configuration
Deploying Hadoop in a pseudo-distributed mode
Deploying a multi-node Hadoop cluster
Installing Hadoop Clients
Understanding working of HDFS and resolving simulated problems.
3. Hadoop cluster Administration & Understanding MapReduce
Understanding secondary name node
Working with Hadoop distributed cluster
Decommissioning or commissioning of nodes
Understanding schedulers and enabling them.
4. Backup, Recovery and Maintenance
Common admin commands like Balancer
Trash, Import Check Point
Distcp, data backup and recovery
Enabling trash, namespace count quota or space quota, manual failover or metadata recovery.
5. Hadoop Cluster: Planning and Management
Planning the Hadoop cluster
Cluster sizing, hardware
Network and software considerations
Popular Hadoop distributions, workload and usage patterns.
6. Hadoop 2.0 and it’s features
Limitations of Hadoop 1.x
Features of Hadoop 2.0
YARN framework, MRv2
Hadoop high availability and federation
Yarn ecosystem and Hadoop 2.0 Cluster setup.
7. Setting up Hadoop 2.X with High Availability and upgrading Hadoop
Configuring Hadoop 2 with high availability
Upgrading to Hadoop 2
Working with Sqoop
Working with Hive
Working with Hbase.
8. Understanding Cloudera manager and cluster setup, Overview on Kerberos
Hive administration, HBase architecture
HBase setup, Hadoop/Hive/Hbase performance optimization
Cloudera manager and cluster setup
Pig setup and working with grunt
Why Kerberos and how it helps.
For whom Hadoop is?
IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-
- Hadoop is open source (Cost saving / Cheaper)
- Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market
- It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.
- Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP
- There will be almost 4.4 million jobs in market on Hadoop by next year.
Please refer below mentioned links:
Can I Learn Hadoop If I Don’t know Java?
It is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.
Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.
- Solution for BigData Problem
- Open Source Technology
- Based on open source platforms
- Contains several tool for entire ETL data processing Framework
- It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.