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key features

  • 70+ Hours of High Quality e-learning Content
  • Course comprising of 418 lectures
  • Weekly query resolution sessions
  • Get Completion Certificate from Scholars Pro
  • 24X7 support over Email
  • Certified Trainers and Industry Experts
  • 200+ Training's Conducted
  • 2000+ Learners
  • 9 Courses in Big Data Hadoop Bundle
  • 24X7 e-Learning Access

Why Big data Bundle?

According to the anticipation of IDC Big Data Technology and Services Market will increase at 26.4% annually to $41.5 Billion through 2018.
 
The demand for Big Data Hadoop and related technologies are estimated to excel the supply by more than 50% by 2018. The exponential growth in data across various industries and advanced processing capabilities present Big Data and Hadoop Professionals with sought after positions in big data analysis.
 
Hadoop is not just a product or a platform; it is an ecosystem of open products, technologies, tools, and platforms that work together to derive insights from unstructured or semi-structured data distributed over many computers. Every day, 2.5 quintillion bytes of data get created globally from different transactions and devices.
 
These following 4 most popular and useful Big Data technologies packaged as self-paced online learning modules spanning 232 lectures consisting of 37.5 hours of content will enable you to confidently handle Big Data problems in multiple scenarios.

Who should join Big Data Architect Masters Program Certification?

  • The course modules have been designed for the individuals craving to expertise the concepts of Big Data. They get to know the Hadoop platform and tools that are required for processing Big Data Sets. 
  • If you are an IT professional with a minimum of 2 years’ experience in programming with an interest in developing a career in analytics, this course is a good place to start.
  • If you are from other technology backgrounds and have a need to work on big data to derive insights, this course will be of help.
  • Professionals working in Data Warehousing / Mining technologies will be able to advance their career in Big Data Engineering and Data Science roles.

Prerequisites for Big Data Bundle Training

Though not mandatory, basic knowledge of Java or other programming language is recommended.

Learning Path

Using MapReduce – the most popular open source parallel programming model for processing large datasets, you will learn how to do efficiently process large amounts of data spread across Hadoop Clusters. With 71 lectures consisting of 13 hours of content that are accessible 24X7, you will be able to setup your own Hadoop cluster and perform multiple parallel processing real world assignments.

Course Curriculum

Hive is a data warehousing tool built on top of Hadoop that helps you to take advantage of distributed computing. With 86 lectures consisting of 15 hours of content, you will be able to write complex analytical queries, use partitioning and bucketing concepts to optimize queries and understand how Hive, HDFS and MapReduce work together.

Course Curriculum

Apache HBase is an open-source distributed database that is created on the top of the Hadoop distributed file system (HDFS). 41 lectures consisting of 4.5 hrs of course content, all accessible 24/7. After course completion, the candidates would be able to setup the database for their application, create as well as access the data from Hbase. Moreover, integrate the HBase with MapReduce for the tasks of data processing and develop an understanding of the role of HBase in the Hadoop Ecosystem.   

Course Curriculum

Apache Pig can execute its Hadoop jobs in MapReduce. With 34 lectures consisting of 5 hours of content that are accessible 24X7, you can work with unstructured data to extract information, transform and store in a usable form, write intermediate level Pig scripts and optimize operations to work on large data sets.

Course Curriculum
The Oozie framework helps you to manage thousands of jobs in an orchestrated manner. With 23 lectures consisting of 3 hours of content that are accessible 24X7,you will install & setup Oozie, configure workflows to run Hadoop jobs, create time-triggered & data-triggered Workflows as well as build & optimize data pipelines using Bundles.
Course Curriculum

Using Scala & Spark together will help you to effectively analyze data in an interactive environment with fast feedback. With 51 lectures consisting of 8.5 hours of content that are accessible 24X7,you will learn functional programming constructs in Scala, use the different libraries & features of Spark for various analytics & machine learning tasks and build Scala applications.

Course Curriculum

Flume and Sqoop are helpful in importing data to HDFS, Hive nand HBase from a variety of other sources very efficiently. The candidates would be able to develop a complete understanding of the technology with 2 hrs of lectures which is enriched with 16 lectures. The modules are accessible 24/7.  After the completion of training the candidates would be able to use Flume to devour data to HBase and HDFS, know how to optimize Sqoop to import data from MSQL to HDFS as well as Hive. They would be able to ingest the data from various sources, including HTTP, Twitter and MySQL.

Course Curriculum

The modules consist of 52 lectures delivered in 8 hrs that is accessible by the candidates online 24/7. Through the Sparks training, the candidates would learn how to work with the single interactive work atmosphere that consist large amount of data. They learn how to run machine learning algorithms along with other functions. They learn the implementation of complex algorithms using Spark and know how to work efficiently with different datasets and employ different features and Libraries of Sparks.

Course Curriculum

We offer 44 lectures consisting 5.5 hrs of content that are accessible 24/7. The modules focus on understanding the queries and managing the clusters using Cassandra Cluster Manager, learn about the architecture and the storage components. Nevertheless, they will learn all about this high performance technology, both theoretically and practically to work efficiently on the live project. 

Course Curriculum

FAQs

WHAT IS THE BACKGROUND REQUIRED TO LEARN THESE HADOOP PACKAGES?

This bundle is designed for individuals seeking to gain expertise in the concepts of big data, tools and the platform of Hadoop needed for processing large data sets. However, basic understanding of Java or some programming is recommended.

WHO CAN ENROLL IN THESE BUNDLED COURSES?

These courses are ideal for engineers, scientists, researchers, and business analysts who are keen to grow their career in Big Data and Analytics.

I AM NOT FROM TECHNICAL BACKGROUND. WILL LEARNING HADOOP PACKAGES HELP ME?

It is not mandatory that you need a technical background to learn Hadoop. If you have a need to derive insights from large volume of data in your current job or if you like to switch to a career in Data Science or Analytics, thesemodules will help you.

I AM FRESH OUT OF COLLEGE. WILL A CERTIFICATION IN BIG DATA & HADOOP HELP ME GET A JOB?

A certification in Big Data & Hadoop can definitely complement your job search. However having a few years’ experience will be an added advantage for most of the practical implementation of Hadoop.

I AM A SYSTEM/DATABASE ADMINISTRATOR. WILL LEARNING HADOOP HELP ME?

Definitely! In fact you can consider Hadoop Administration roles who manages infrastructure, Hadoop Clusters etc.

DO I NEED TO KNOW JAVA FOR LEARNING HADOOP?

While a basic working knowledge of Java will be of help, it is not mandatory to know Java programming for learning Hadoop. Some of the Hadoop components like Pig, Hive etc. can be learned without knowledge of Java. There are some rare requirements to use Java which can be learned easily.

WHAT ALL WILL L LEARN FOR THIS TRAINING?
The purpose of these modules is to provide high level understanding and the capabilities:
  • Hadoop & MapReduce for Big Data Problems
  • Hive for Big Data Processing
  • HBase – The Hadoop Database
  • Pig for Wrangling Big Data
  • Oozie: Workflow Scheduling for Big Data Systems
  • Scalable Programming with Scala & Spark
  • Flume & Sqoop for Ingesting Big Data
  • From 0 to 1 : Spark for Data Science in Python
  • From 0 to 1 : The Cassandra Distributed Database
WHAT ALL ARE COVERED IN “HADOOP & MAPREDUCE FOR BIG DATA PROBLEMS” MODULE?
  • Access 71 lectures & 13 hours of content 24/7
  • Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud
  • Understand HDFS, MapReduce & YARN & their interaction
  • Use MapReduce to recommend friends in a social network, build search engines & generate bigrams
  • Chain multiple MapReduce jobs together
  • Write your own customized partitioner
  • Learn to globally sort a large amount of data by sampling input files
WHAT ALL ARE COVERED IN “HIVE FOR BIG DATA PROCESSING” MODULE?
  • Access 86 lectures & 15 hours of content 24/7
  • Write complex analytical queries on data in Hive & uncover insights
  • Leverage ideas of partitioning & bucketing to optimize queries in Hive
  • Customize Hive w/ user defined functions in Java & Python
  • Understand what goes on under the hood of Hive w/ HDFS & MapReduce
WHAT ALL ARE COVERED IN “HBASE – THE HADOOP DATABASE” MODULE?
  • Access 41 lectures & 4.5 hours of content 24/7
  • Set up a database for your application using Hbase.
  • Integrate HBase w/ MapReduce for data processing tasks
  • Create tables, insert, read $ & delete data from HBase.
  • Get a complete understanding of HBase & its role in the Hadoop ecosystem.
  • Explore CRUD operations  in the shell, & with JAVA API
WHAT ALL ARE COVERED IN “PIG FOR WRANGLING BIG DATA” MODULE?
  • Access 34 lectures & 5 hours of content 24/7
  • Clean up server logs using Pig
  • Work w/ unstructured data to extract information, transform it, & store it in a usable form
  • Write intermediate level Pig scripts to munge data
  • Optimize Pig operations to work on large data sets
WHAT ALL ARE COVERED IN “OOZIE: WORKFLOW SCHEDULING FOR BIG DATA SYSTEMS” MODULE?
  • Access 23 lectures & 3 hours of content 24/7
  • Install & set up Oozie
  • Configure Workflows to run jobs on Hadoop
  • Create time-triggered & data-triggered Workflows
  • Build & optimize data pipelines using Bundles
WHAT ALL ARE COVERED IN “SCALABLE PROGRAMMING WITH SCALA & SPARK” MODULE?
  • Access 51 lectures & 8.5 hours of content 24/7
  • Use Spark for a variety of analytics & machine learning tasks
  • Understand functional programming constructs in Scala
  • Implement complex algorithms like PageRank & Music Recommendations
  • Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings
  • Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX
  • Write code in Scala REPL environments & build Scala applications w/ an IDE
WHAT ALL ARE COVERED IN “FLUME & SQOOP FOR INGESTING BIG DATA” MODULE?
  • Access 16 lectures & 2 hours of content 24/7
  • Use Flume to ingest data to HDFS & HBase
  • Optimize Sqoop to import data from MySQL to HDFS & Hive
  • Ingest data from a variety of sources including HTTP, Twitter & MySQL
WHAT ALL ARE COVERED IN “FROM 0 TO 1 : SPARK FOR DATA SCIENCE IN PYTHON” MODULE?
  • Access 52 lectures & 8 hours of content 24/7
  • Use Spark for a variety of analytics & machine learning tasks
  • Implement complex algorithms like PageRank & Music Recommendations
  • Work w/ a variety of datasets from airline delays to Twitter, web graphs, & product ratings
  • Employ all the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming & GraphX
WHAT ALL ARE COVERED IN “FROM 0 TO 1 : THE CASSANDRA DISTRIBUTED DATABASE” MODULE?
  • Access 44 lectures & 5.5 hours of content 24/7
  • Set up & manage a cluster using the Cassandra Cluster Manager (CCM)
  • Create keyspaces, column families, & perform CRUD operations using the Cassandra Query Language (CQL)
  • Design primary keys & secondary indexes, & learn partitioning & clustering keys
  • Understand restrictions on queries based on primary & secondary key design
  • Discover tunable consistency using quorum & local quorum
  • Learn architecture & storage components: Commit Log, MemTable, SSTables, Bloom Filters, Index File, Summary File & Data File
  • Build a Miniature Catalog Management System using the Cassandra Java driver

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