Big Data Corporate Training

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Mazenet’s Big Data training program provides you with in-depth knowledge of designing, developing and deploying Big Data application and Data Science in real-time. Corporate IT professionals will have a strong proficiency in Hadoop, Apache Spark, Scala, etc. specially designed by industry experts.

Key Features

Course description

Big Data Hadoop

The collection of open-source software utilities that could solve problems involving massive data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

Apache Spark and Scala

Scala is named for its scalability on JVM and it is used for writing Apache Spark. To work on Spark projects, Big Data developers use Scala as the most prominent language. The syntax is much simpler when compared to Java and C++.

Hadoop Administration Python Spark using PySpark

A collaboration of Apache spark and python, which helps data scientists interface with Resilient Distributed Datasets in apache spark and python. For any big data processing, we would need a framework like Hadoop to process data efficiently.

Splunk Power User & Admin

"Understand Splunk Power User/ Admin concepts. Apply various Splunk techniques to visualize data using different graphs and dashboards Implement Splunk in the organization to Analyze and Monitor systems for operational intelligence Configure alerts and reports for monitoring purposes Troubleshoot different application logs issues using SPL (Search Processing Language) Implement Splunk Indexers, Search Heads, Forwarder, Deployment Servers & Deployers.

Apache Kafka

It is written in Scala and Java, Which is an open-source stream-processing software platform.It helps to handle data pipeline for high speed filtering and pattern matching on the fly.

Apache Solr

The search platform is written in Java. It is highly reliable, scalable and fault-tolerant. Its major features are full-text search, real-time indexing, and dynamic clustering.

ELK Stack

ELK Stack consists of Elasticsearch, Logsearch, and Kibana, each is an individual project. It is well built to work together and work exceptionally.

Comprehensive Hive

Comprehensive Hive training will help participants understand concepts like Loading, Querying and Importing data in Hive.

MapReduce Design

Building effective algorithms and analytics for Hadoop and other systems. It helps in processing data that is scattered over hundreds of computers. It is recently popularized by Google and Hadoop.

Apache Storm

An open-source distributed real-time computational system, which is free and capable of processing streaming data at an unprecedented speed.

Comprehensive Pig

In this module, you will learn the basics of Pig, types of use cases where Pig van is used, tight coupling between Pig and MapReduce, and Pig Latin scripting.

Mastering Apache Ambari

The main objective of Apache Ambari is to make the management of Hadoop easier for developers and administrators. By mastering Apache Ambari, one can become Hadoop Administrator.

Comprehensive Hbase

It runs on top of HDFS (Hadoop Distributed File System) to provide Google’s Bigtable essentials to Hadoop. It is an open-source, non-relational, distributed database model.

Comprehensive MapReduce

This Framework will allow us to perform distributed and parallel processing on large data set. This training will help you to solve use cases. Companies as Facebook, Twitter uses MapReduce.

Course Preview

  • Hadoop Configuration and Installation
  • HDFS
  • Hadoop based Projects
  • Hadoop cluster Architecture
  • Hadoop cluster configuration
  • Hadoop cluster modes
  • Basics of Hadoop Eco-System
  • Single node and Multi-Node Cluster
  • Hadoop Shell Commands
  • Map Reduce Architecture
  • Necessity of MapReduce
  • Map Reduce Programs in Java
  • Input Splits
  • HDFS Blocks
  • YARN workflow
  • Traditional Way Vs MapReduce way
  • Counters
  • Joining Data Sets
  • Distributed Cache
  • Streaming
  • Distributed Joins
  • MR Unit
  • Real-Time Example
  • Introduction to Hive
  • HQL
  • Introduction to H-Base and No-SQL Data Base
  • H-Base Architecture
  • Comparison of SQL and HQL
  • Hive Datatypes
  • Hive Tables
  • Importing and Querying Data in Hive
  • Running Hive Scripts
  • HBase Vs Traditional Database
  • Partitions
  • Introduction to Pig
  • Pig Architecture
  • Pig data types
  • Pig Vs MapReduce
  • Coupling Pig and MapReduce
  • Pig Latin
  • Pig Scripting
  • Pig UDF
  • Pig Streaming
  • Pig Script testing
  • Importing Pig Jars
  • Pros and Cons of Pig
  • Real-time Example

FAQs

Big data training helps the employees to learn multiple ways to store data for efficient processing and analysis. It upskills your employees to store, manage, process, and analyze massive amounts of data to create a data lake.

Big Data and Hadoop, being the most in-demand technology today, is the latest way to generate valuable data. The data has been accelerating in order to generate the data in large velocity, volume, and variety as well.

Yes, you can learn Hadoop without Java knowledge whilst you have knowledge in OOPS (Object Oriented Programming Language). Knowing Java will help you in many circumstances as every field has been including Java programs.