Prwatech Offers Apache Spark Training in Bangalore

Apache Spark Scala is mainly a cluster computing system that is fast and has been developed for the processing of Big Data. It is written in the programming language of Scala and is said to run well with Hadoop or mesos or even alone at times. We offer you the Apache Spark training in Bangalore to understand you its execution in data processing. The spark has been considered even much faster than Hadoop, and hence we train our candidates about it as a part of the data processing system of Big Data.

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23
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Objectives of The Course : Apache Spark Scala Training Bangalore

After you complete your course, you will be able to understand about Scala and its implementation in details. If we wish to go to the Apache Spark Scala Training in Bangalore, then we can offer you the best one as we help you to clear your doubts in various difficult concepts such as Scala programming and OOPS.

  • Some of the other course objectives that the students learn here are:
  • Working with Spark operations on the system of Spark Shell.
  • Knowing and understanding the working of Spark RDD in details.
  • Implementation of the Spark works on Hadoop and others.
  • Implementation of algorithms in the working of Spark API.
  • Understanding of Spark GraphX API and also the implementation of Graph Algorithm.
  • Implementation of accumulators and broadcast variable for tuning performance.
  • Analyzing the architecture of Spark SQL and Hive, and many others.

Reasons to Go for The Course

There are some reasons for why students are going to Apache Spark Scala Training in Bangalore and many other cities respectively.
The course is a great start for the candidates who wish to make their career in data processing computing and also for those who wish to gain the latest knowledge about Big Data. The main focus of the course here is Apache Spark Scala training and many other projects that may be related to the particular training program.
Today maximum of the organizations is looking forward to hiring candidates who can manage a bigger size of data and that also quite efficiently. This is the reason candidates are approaching towards certification courses that can train them with the latest of data processing programs that are fast and also efficient in nature. We offer the course mainly for the candidates who are:

  • Enthusiasts in the field of Big Data,
  • Ideal for developers, engineers and also for software architects,
  • Also for analytics professionals and data scientists.

Apart from what is there in the training course, we provide a strong team of faculties who are expert in the field and are updated with the latest working of Big Data operations such as that of Apache Spark. The course is a combination of both theory as well as practical knowledge, and hence the candidates can get trained up well in the concept.
Apache Spark is the latest programming regarding data processing programming, and hence we make sure to provide the course for the candidates who wish to grow high on this path.

Learning objectives – In this module, you will learn about the: basic concepts of UNIX, LINUX, Java, HDFS, Big Data, Hadoop, Hadoop data loading techniques, Solving big data problems using Hadoop, MapReduce, Hadoop cluster and role of Hadoop cluster.

Topics: Introduction to UNIX, Introduction to LINUX, Introduction to Java, Introduction to HDFS, DWH Concepts, Pig & Hive, Map reduce.

Learning Objectives – In this module you will learn about Multiple Hadoop Server roles, Understanding Pseudo cluster, Installation of Hadoop, Understanding Map-Reduce, files configuration and parameters.

Hadoop Installation, Hadoop Configuration, Understanding pseudo-distributed mode, deploying multi-node cluster, Role of Hadoop server, Rack knowledge, Write and Read Anatomy, Data Processing.

Learning Objectives – In this module you will be Understanding Hadoop Cluster setup, concepts of managing and planning Hadoop cluster, Troubleshooting Hadoop cluster, Monitoring Hdoop cluster, Executing MapReduce Jobs.

Topics: Concepts of Hadoop Cluster, Scheduling jobs, Monitoring cluster, Cluster Size, Knowledge about Hardware and Software portions, Schedulers in Hadoop, Troubleshooting cluster, Schedulers Configuring and run Map Reduce.

Learning Objectives – This module will help you to understand Clustering basics, Clustering administration tasks like adding or deleting data nodes, Node recovery, Configuring in Hadoop, Backup in Hadoop, Recovery in Hadoop, Troubleshooting node failures and Upgrade Hadoop.

Topics: Maintaining Hadoop Backup, data nodes white list and blacklist in a cluster, quota’s setup, upgrading Hadoop cluster, DISTCP, Diagnostics, Recovery, Cluster Troubleshooting, Rack Configuration.

Learning Objectives – In this module our main focus is to understand Hadoop 2.0 New Features, HDFS, YARN, Hadoop 2.0 setup, MRv2, Secondary NameNode setup, NameNode check pointing.

Topics: Configuring Secondary NameNode, , Deploying Hadoop 2.0 in pseudo-distributed mode, Hadoop 2.0, YARN, MRv2, Hadoop 2.0 Cluster setupdeploying a multi-node Hadoop 2.0 cluster.

HQL and Hive with Analytics

Learning Objectives- After completing with the basics, In this module you will strengthen concepts about Hadoop security, Managing Hadoop security, HDFS High Availability, HDFS setup and Log Management and Quorum Journal Manager

Topics: Hadoop Platform Security, Configuring Kerberos, Auditing and Alerts, Configuring HDFS, Monitoring, Log Management and Service Monitoring.

Learning Objectives – This module will assist you in learning Oozie Workflow Scheduler, deploying HBase, Hive Administration, effectively load data, read and write from HBase.

Topics: No SQL, H base, Zookeeper, HBase Architecture, Sqoop, Flume, HBase setup, Oozie, Yarn and Hue.

Learning Objectives – In this module you will work on a real-world case and learn about implementing, planning, designing and deploying Hadoop cluster. You will also learn about Hadoop eco-system components.

Topics: Implementing, planning, designing and deploying Hadoop cluster, Hadoop ecosystem components, troubleshooting cluster problems, AWS cluster.

This module in the Big Data Hadoop Course helps the learners in understanding Hadoop 2.0 features like MRv2, YARN and HDFS Federation.

The topics covered are New Features of Hadoop 2.0, High Availability of NameNode.

This module helps the readers in understanding how different Hadoop ecosystem elements work together towards Hadoop implementation for solving Big Data issues.

Rs. 14,000 + Tax

per 2 weeks

35 Hours
Practical 40 Hours
15 Seats
Course Badge
Course Certificate

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