Big Data Analytics training with Certification & Job Assurance at Hyderabad

Big Data Analytics course is one of the top most words which has come into limelight since the demand for the new and creative jobs grew in IT sector. As Innomatics research labs at Kukatpally, Hyderabad has always been top pioneer in fulfilling a learners need for the demanded courses, having an eye over the demand. So, Innomatics research labs entered into catering students with Big data analytics training in Hyderabad.

Big Data Analytics course training enables the students to acknowledge themselves with the major big data concepts including Hadoop and spark. Despite big data being a part of data science, big data has also got various courses in it which a student can choose as per their profession.

What is Big Data?

Generally, big data is useful for analyzing and processing all the structured and unstructured data at maximum speed. Big data, the term itself generates the meaning viz, the data which is vast and big in size, which is infinite. Say, monthly transactions by a giant supermarket D-mart is a huge in number when seen or google generating websites every year globally, whose data is infinite in number and hard to maintain. Here comes the need of big data, which customized and processes everything in a systematic way and at faster pace.

Why Big Data Course?

Usually, gathering of data and generating it has become a huge task and also a top most priority for all the industries. This is why, the most renowned companies like IBM, Infosys, TCS are hiring a vast number of data analytics every year. Also, the IBM report says that, by 2020 there would be uncountable jobs for big data analysts globally.

Demand for Big Data Analytics Course in Market

And, when it comes to the job market for the big data analytics as well as professionals, this segment in IT sector has got endless demand and huge hype over the market industrialists. As these big data analysts and professionals are a package of skills including business acumen, math and statistics, professional communication skills, and also programming and collaborative skills, this has created the most hype for the big data analysts and professionals in almost every company.

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Big Data Analytics
DEMO/NEW BATCH details

Schedule Date Timings
WeekDay Batch 13/05/2019 (MONDAY) 7 PM
WeekEnd Batch 18/05/2019 (SUNDAY) 11 AM

Understanding of big data analytics

Lets keep it simple. There are 5 zettabytes of data in the world and 90% of the data was generated in the past 2 years. Moreover, there might be 1075 zettabytes of data by 2025. There is data on everything and everywhere. The increasing digital components are generating huge amounts of data. Your mobile devices leaves a trace and there are trillions of photographs stored across the world in the digital form as data. The text file documents, sensor data, server data, website data, email data, social media data, audio and video files are the sources of unstructured data. It is also segregated in structured form and semi-structured form as well. Big data is dynamic by itself.

Firstly, the data need to be captured from different sources which could be structured or unstructured or semi-structured. Secondly, the gathered data needs to be aggregated according to its cluster. Later, the clustered groups are analysed for further separation into logical components to communicate, which means business decisions.

What is the use of this enormous data?

Huge data = huge business. This large sets of data helps to understand the customers from different perspectives and helps to make better decisions by predicting the purchasings and activities of the customers that can happen in the near future. The collected data used to develop strategies and optimized processes to enhance business, as data is an asset which you can sell or monetize. Big data analytics enables to invent new smart products and services with the dynamics in the technology according to time and space. Social media platform provides customer insights that are transparent and simpler to analyze the behaviour of the customer.

Importance of Innomatics Research Labs in Big Data Analytics Training in Hyderabad

Innomatics research labs intend to produce the best and finest professionals from our institute as Big data Developer, Big data Architect, Big data Engineer, Admin and so on. The significance of big data meets the demand for the skillful experts and we apprehend the desire of many professionals as well as industries who are keen to be part of the big data business, as we produced nearly 200 certified professionals with in few years and accommodated them in top industries. Our industry expert trainers can train professionals and beginners with flexibility, dedication towards every individual. The professional friendly atmosphere maintains the institute reputation ahead among many other institutes in hyderabad. 

Innomatics research labs kukatpally, Hyderabad with peculiar training methods of its own and 100% job assurance with huge success rate, destined to be the best software institute for Big Data Analytics training in Hyderabad. Apart from Big Data Analytics training we offer ADVANCED DATA SCIENCE training, AMAZON WEB SERVICES (AWS) training, ROBOTIC PROCESS AUTOMATION (RPA) course training, and DIGITAL MARKETING course training programs with certification from Innomatics research labs and other top MNCs.

Big Data Analytics Course Curriculum

1. Introduction to Big Data & Hadoop

  • Introduction to Big Data & Challenges
  • Limitations & Solutions of Big Data Architecture
  • Hadoop: Features, Ecosystem, 2.x Core Components, Distributions
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework

2. Hadoop Architecture and HDFS

  • Hadoop: 2.x Cluster Architecture, Cluster Modes, 2.x Configuration Files
  • Federation and High Availability Architecture
  • Hadoop Production Cluster
  • Common Hadoop Shell Commands
  • Single & Multi Node Cluster set up
  • Basic Hadoop Administration

3. Hadoop MapReduce Framework

  • Traditional vs MapReduce way
  • MapReduce: Need, Anatomy of Program, Combiner & Partitioner
  • YARN: Components, Architecture, MapReduce Application Execution Flow, Workflow
  • Input Splits, Relation between Input Splits and HDFS Blocks

4. Apache Hive

  • Introduction to Apache Hive, Hive vs Pig
  • Hive: Architecture and Components, Metastore, Limitations of Hive, Partition, Bucketing, Tables (Managed Tables and External Tables)
  • Traditional Database vs Hive Data Types and Data Models, Importing Data, Querying Data & Managing outputs

5. Apache Pig

  • Introduction to Apache Pig
  • Pig: MapReduce vs Pig, Components, Execution, Data Types & Models, Latin Programs, UDF, Streaming, Shell & Utility commands, Testing Pig scripts with Punit

6. Apache Sqoop

  • Sqoop Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises

7. No SQL

  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail
  • Columnar Databases in Detail (HBASE and CASSANDRA)
  • TTL, Bloom Filters and Compensation

8. Flume

  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE

9. Oozie

  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles., to show how to schedule Sqoop Job, Hive, MR and PIG

10. Apache Spark

  • Spark Overview, Linking with Spark, Initializing Spark, Basics, Passing Functions to Spark
  • Using the Shell, Resilient Distributed Datasets (RDDs), RDD Operations , Parallelized Collections, External Datasets, Working with Key Value Pairs
  • Transformations, Actions, RDD Persistence, Storage Level choice, Removing Data, Shared Variables, Broadcast Variables, Accumulators, Deploying to a Cluster

Why Choose Innomatics

18+ Industry experts
Learn with highly qualified academic institutions and tutors with huge experience.
Rigorous efforts taken to make a student to be a job qualified both skillfully as well as subject wise
Also conducting of various assignments, workshops and meet ups for collaborations
Certified and guaranteed placements for both IT and NON-IT
700+ people got trained since the period of establishment
100% Placement Assistance
Handouts, Exercises and Assignments on subject
In house Internship on our projects & products

At innomatics research labs Kukatpally, Hyderabad, We discuss the following tools briefly.

1. APACHE HADOOP
A software library framework that allows distribution and processing of large data sets across clusters of computers using simple programming models with little reduction in performance designed for computation and storage that expands from one single server to thousands of machines used to support advanced analytics initiatives, including predictive analysis, data mining and machine learning applications.

2. MAP REDUCING FRAMEWORK
The core component of hadoop framework for data processing as it splits the data into different number of parts and reduces the parts into components to run the program on all parts at once. It does wonders when it comes to big data with parallel process and value analysis output.

3. APACHE SPARK
It is known for flash speed cluster technology, designed for fast computation which can efficiently compute massive parallels. It is inexpensively reliable and flexible memory framework that can integrate well with big data languages such as Python, Scala, Java and other languages. Apache spark is capable of machine learning, interactive analysis and streaming data.

4. APACHE SQOOP
It can efficiently transfer huge/bulk data between Apache Hadoop and structured data bases as relational databases. It imports data from relational databases such as MySQL, Oracle to Hadoop and export data from hadoop file system to relational databases.

5. FLUME (injection tool)
It is a distributed service available for efficient collection, aggregation and moving large amounts of log data. It is simply flexible architecture based on streaming data flows and robust fault tolerant with tunable reliability mechanisms. It is used as extensible data model that allows online analytic applications.

6. HIVE (injection tool)
Hive is a data warehouse software that facilities reading, writing and managing large data sets residing in distributed storage and provides database query interface to hadoop. Designed to maximise performance, scalability, fault-tolerance and extensibility with loose-coupling with it’s input formats.

7. PIG
Pig is a self optimizing, extensible, and easily programmable tool that supports both structured and unstructured data. It can be used on behalf of Java. It operates by parsing, checking, optimizing the script to submit to hadoop and monitors job progress.

8. OOZIE
A workflow scheduler for hadoop that runs plain Java classes, pig work flows, interact with the HDFS and can run jobs sequentially and parallelly. The scalable, reliable, extensible system integrated with rest of the hadoop stack supporting several types of hadoop jobs (such as java mapreduce, streaming mapreduce, pig, hive, sqoop) and system specific jobs (such as Java programs and shell scripts).
Developers use oozie for mailing lists, reporting bugs, retrieving code from the version control system to make contributions.

9. NO SQL
A completely different framework of databases that allows high-performance, agile processing of information at a vast scale. The database is unstructured in nature and centers around distributed databases by trading off stringent consistency requirements for speed and agility.

10. SCALA
A multi paradigm programming language with the combination of object-oriented and functional programming which is highly scalable. The programming language is robust and stable as well as influenced by the Java programming language. Scala is used to write web applications, working with streaming data, parallel batch processing, multiple computation, and analyses data with Apache Spark.

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