Unlock the Values from Massive Datasets.

Learn Fundamentals to Advanced Levels of Big-Data Methods BIG-DATA

Training by Certified Industry Experts.

BIG-DATA Training

Course Amount: $699

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BIG-DATA Training Online 


Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages between similar organizations and result in business benefits, such as more effective marketing and increased revenue.

New methods of working with big data, such as Hadoop and Map Reduce, offer alternatives to traditional data warehousing. Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and R-Hadoop.

A powerful data analytics engine can be built and process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics.

Upskill and get certified in Big Data with our one-of-a-kind big data training course, crafted to help you become adept at Big Data Technology.

KEY Highlights

Get noticed by top companies through our professional job assistance.
35 hours of in-depth training programs with realtime scenarions.
Get trained by the highly experienced certified industry experts.
Upgraded sylabus with Industry oriented concepts for in-depth knowledge.
15+ In- demand tools & skills will help in  Technical Assistance.
Every session will be live and provides you hands-on experience training.

Training Advantages

35 contact hours

Industry case studies

Hands-on Projects

Real time training

Course Outcomes

Core Concepts

Core computer science concepts from leading industry experts. 

Application Testing

Build an end-to-end application and test it with exciting features.


Earn an industry-recognized course completion certificate.

BIG-DATA Certification Course Contents



  • What is File System?
  • Block, Linux commands
  • How is the system working?
  • File system, OS, CPU, Applications
  • Shell Script basics
  • How to automate Sqoop
  • hive using a shell script.

Scala for Spark

  • Installation
  • Java 8
  • Scala 2.11.8
  • Intelli-J
  • What is JVM?
  • Java Vs Scala
  • Scala basics
  • Primitive DataTypes
  • Scala Type Hierarchy
  • Strings, List
  • Arrays
  • tuples for loop match
  • switch
  • functions
  • recursive functions
  • nested functions
  • Higher-order functions
  • anonymous function
  • most frequently used functions in Spark
  • map, flat map,
  • distinct group by the filter

  • What is data?
  • Why Big Data came into the picture?
  • Common Big Data problems.
  • Normal file system
  • bigdata ecosystems functioning.
  • Storage problems
  • processing problems.
  • Frameworks – programming languages.
  • Different type of popular Big Data frameworks.
  • Where Hadoop recommended?
  • Hadoop & ecosystems.
  • Hadoop internal concepts.
  • HDFS

  • HDFS Daemons (Name node, Data node, Secondary Name node),
  • HDFS Block Vs Unix file system
  • Block,
  • HDFS Replication importance,
  • HDFS File Write Process,
  • HDFS File Read Process,
  • Secondary name node,
  • Namenode High Availability,
  • Why Namenode very Fast.
  • HDFS Shell Commands.
  • Input split. Why MapReduce slow?
  • How HDFS solve Storage Problems.
  • YARN

  • Introduction – YARN
  • Daemons (Resource Manager, NodeManager)
  • Why YARN needed?
  • Responsibilities of Resource Manager
  • Node Manager and Application Master on YARN Cluster
  • What is Container in YARN?
  • Mapreduce input split,
  • speculative execution,
  • debug yarn logs.
  • Creating JAR file in IDE Block Vs Split – Partition
  • Hash Partitioner
  • Custom Partitioner – Combiner
  • How Application Master functioning.
  • Yarn life cycle.
  • Mapreduce Vs Spark.
  • Why MapReduce is very Slow.
  • HIVE

  • Introduction to HIVE
  • Hive Vs RDBMS
  • Data types (Primitive, Collection)
  • Create Tables (Managed, external)
  • DML operations (load,insert,export)
  • Managed Vs external tables
  • QL Queries (select, where, group by, having, sort by, order by)
  • File Formats (Text, ORC, Parquet)
  • Partitioning,
  • Bucketing,
  • Partitioning Vs Bucketing-Views
  • different types of joins (inner, outer)
  • Map Side join
  • Bucketing join
  • Series (CSVSerde, JsonSerde, MultiDelimitSerde)
  • How to update data using Hive.
  • Automate Hive script using Shell

  • Import data from Oracle
  • MySQL
  • MsSQL data using Sqoop.
  • Change the delimiter and file format of data during import using Sqoop.
  • Compression techniques
  • Store data in Hive optimization
  • Best practice.
  • Export structure data to Sqoop.
  • Limitations, problems in Sqoop.

  • Kafka producer API
  • Consumer API
  • Streaming API
  • Kafka Topics
  • Zookeeper
  • Brokers
  • leaders
  • partitions
  • Kafka installation in windows.
  • Kafka wordcount program.
  • How to process JSON
  • Avro file using Kafka.
  • Spark Kafka integration.
  • Hbase: How to create a table
  • Column family
  • load data, get, put, scan.
  • Update data.
  • Phoenix: Create a table and run SQL commands on top of Hbase using Phoenix. Run OLAP, OLTP commands using Phoenix.
  • Spark -Phoenix integration run final year project.
  • Cassandra installation and run SQL queries,
  • problems using Cassandra.
  • AWS

  • What is Cloud Computing?
  • AWS vs Azure.
  • Different type of AWS Services.
  • RDS: Create Oracle, MySQL, MsSQL, insert data and run DML & DDL queries. EC2: Create windows and Ubuntu OS.
  • Image (AMI) importance.
  • Install different software in ubuntu.
  • EMR & S3: Run Hadoop, Hive, Spark, Sqoop command using EMR.
  • Access S3 data using S3 in CLI. Buckets.
  • IAM: Groups, users, policies.

  • Spark installation in windows.
  • Why & Where use Spark?
  • Hadoop Vs Spark.
  • RDD API – DAG Graph
  • Lineage Graph
  • Transformations
  • Actions, Word count program.
  • RDD vs Hadoop input Split
  • Key-pair RDDs, Properties
  • lineage in-memory Different ways to create RDD.
  • Joins, tuning Hands-on.
  • Convert rdd to dataframe api
  • Catalyst optimizer data frame API.
  • DSL commands: select, filter, group by, join, distinct, order by, where.
  • Run SQL queries on top of multiple datatype datasets (JSON, CSV, XML, parquet, orc) Get data from Oracle, MySQL, MsSQL, optimization techniques.
  • Submit a project in the production environment.
  • check logs and analyze using web UI.
  • Process Hbase.
  • Cassandra and Redshift data using Spark.
  • DataSet API importance in spark.
  • Case classes.
  • process complex JSON data.
  • Spark Streaming: Wordcount program
  • Datastream API. How to get logs from the webserver.
  • How to run SQL queries on top of streaming data.
  • Structure streaming using spark.
  • Get twitter data using Spark.
  • Process and implement sentiment analysis using Spark.
  • Get data from different sources using Spark.

  • Installation of FLINK
  • Spark Vs Flink
  • Flink Table library
  • DataSet API
  • Get oracle data using FLINK.
  • Process CSV,
  • JSON data using Flink.
  • Performance optimization
  • Encoder

  • 3 POC (proof of concept)
  • Profile Upgradation
  • Interview tips and Clouder Certification guidance.
  • How to learn other technologies like artificial intelligence, IoT,
  • How to get sample code.
  • Big Data Training FAQ’S


    How can I believe that Big Data has a good career scope?

    There are various indications in real-time scenarios to show the demand for Big Data in today’s IT world. Obviously, the amount of data generated in each and every organization is extremely large. Therefore, it has become inevitable to preserve such a big volume of data. Big Data comes into the picture to handle this kind of heavy data load and has been aiding a plethora of IT organizations around the world in the case of valuable decision making, business improvement and also to establish a great leap in data management against their competitors.

    How do you rate Big Data skills for a great career?

    In reality, many organizations in the United States find it very hard to get qualified candidates in Big Data. Skill Gap has become a predominant issue in Big Data due to lack of training.

    As of the latest survey, there are nearly 190,000 data scientists and 1.5 million managers are in demand in the United States. If you are interested or passionate about Big Data, it is highly recommended to take up Big Data training, which will add value to your profile.

    What are the different Job roles in Big Data analytics?

    Based on the analytical and practical skills of the candidates, we have five different types of job roles in Big Data analytics as follows,

    1. Solutions expert
    2. Analytics salesperson
    3. Storyteller
    4. Expert programmer or Tools expert
    5. Expert Modeler

    What are the various ways to learn Big Data in the most efficient and affordable way?

    Two simple ways to learn Big Data are

    1. In-class training and
    2. Online training

    The reason to suggest these training modes is, both the above training methods will give a comprehensive understanding and knowledge of the Big Data topic you are interested in. Yet another important thing to keep in mind is the training institute or mentors you choose to learn Big Data. You have a list of established Big Data training institutes in the United States here. Depending upon your convenience, you could either choose, In-class training or online training.

    Are there any prerequisites to gain knowledge in Big Data?

    Big Data concepts are large and quite complex in certain aspects. With respect to the type of big data course, your pre-requisites will be sought. For instance, to learn Hadoop, a basic knowledge of Linux/GNU operating system and programming languages such as Scala, Java or Python are recommended.


    Customer Testimonials

    Nothing makes us happier than satisfied clients. Let us share some successful client stories with you.

    This is an excellent practical course, giving the opportunity to get acquainted with both theoretical and practical work in Business Analysis.

    Henry James


    The best training professionals I found for automation testing training, faculties are highly experienced, explained every concept in-depth with real-time project scenarios.


    San Diego

    Happy to start off my selenium career here, this is by the far the best institute I have ever got for Selenium. Very eloquent tutorial. Outstanding Training with great examples.



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