Big data hadoop training

by Manoj Singh rathore Digital Marketing Head and Founder
Appropriated figuring structures and separate Big Data at lightning speed, thusly improving the business execution incredibly. No enormous shock McKinsey Global Institute measures inadequacy of 1.7 million Big Data specialists over next 3 years. 

Considering this extending opening in the intrigue and supply with help of this Big Data Engineering setting it up/ITES specialists can sack advantageous possibilities and lift their calling by expanding searched for capacities in the wake of completing this Big Data Engineering course. In this Big Data planning members will expand convenient scope of capacities on Data Engineering using SQL, NoSQL (MongoDB), Hadoop condition, including most extensively used sections like HDFS, Sqoop, Hive, Impala, Spark and Cloud Computing. For expansive hands-on preparing, in both Big Data web getting ready and study lobby planning contenders will pick up induction to the virtual lab and a couple of assignments and endeavors for Big Data affirmation. 

The course fuses RDBMS-SQL, NoSQL, Spark, nearby hands-on compromise of Hadoop with Spark and using Cloud Computing for tremendous scale AI and Machine Learning models. 

At end of the program contenders are allowed Big Data Certification by industry experts on productive completing of endeavors that are given as a significant part of the arrangement. This is an expansive Big Data building getting ready close by NoSQL/MongoDB, Spark and Cloud in Bangalore and Delhi NCR, with versatility of heading off to the gigantic data electronic planning and through self-guided accounts mode as well. 

An absolutely industry relevant Big Data Engineering getting ready and an unprecedented blend of assessment and development, making it entirely capable for wannabes who need to develop Big Data Analytics and Engineering capacities to head-start in Big Data Science! 

Colossal Data Certification Course 'Ensured Big Expert' term: 120 hours (Atleast 60 hours live getting ready + Practice and Self-study, with ~8 hrs of step by step self-study) 

Who Should do this course? 

IT/ITES, Business Intelligence, Database specialists/programming building (or some other circuit branches) graduates who are not just scanning for ordinary Hadoop getting ready for Data Engineering work, yet need Big Data Engineering accreditation reliant on valuable Hadoop-Spark and Cloud Computing aptitudes. 



₹ 25,000 


₹ 20,000 



Combo Deals! 

Discover extra, save more. 

See our combo offers here. 

Course Duration 120 hours 

Classes 20 

Devices Cloudera Hadoop VM, Spark, MongoDB, AWS/AZURE/GCP 

Learning Mode Live/Video Based 

Have Questions? 

Contact us and we will get back with answers. 



Relevant examinations 



What is Big Data and Data structuring? 

Importance of Data working in the Big Data world 

Employment of RDBMS (SQL Server), Hadoop, Spark, NOSQL and Cloud handling in Data planning 

What is Big Data Analytics 

Key phrasings (Data Mart, Data item house, Data Lake, Data Ocean, ETL, Data Model, Schema, Data pipeline, etc) 

What are Databases and RDBMS 

Make data model (Schema — Meta Data — ER Diagram) and database 

Data Integrity Constraints and sorts of Relationships 

Working with Tables 

Preface to SQL Server and SQL 

SQL Management Studio and Utilizing the Object Explorer 

Essential thoughts — Queries, Data types and NULL Values, Operators, Comments in SQL, Joins, Indexes, Functions, Views, Sorting, isolating, sub addressing, compressing, mixing, adding, new factor creation, circumstance when enunciation use, etc. 

Data control — Reading and Manipulating a Single and various tables 

Data based articles creation(DDL Commands) (Tables, Indexes, sees, etc) 

Upgrading your work 

From beginning to end to data control work out 

Motivation for Hadoop 

Hindrances and Solutions of existing Data Analytics Architecture 

Relationship of standard data the administrators structures with Big Data Evaluate key framework necessities for Big Data assessment 

Hadoop Ecosystem and focus parts 

The Hadoop Distributed File System — Concept of data storing 

Explain different sorts of pack setups(Fully circled/Pseudo, etc.) 

Hadoop Cluster Overview and Architecture 

A Typical undertaking pack — Hadoop Cluster Modes 

HDFS Overview and Data amassing in HDFS 

Get the data into Hadoop from neighborhood machine(Data Loading ) — the a different way 

Practice absolute data stacking and supervising them using bearing line(Hadoop headings) and HUE 

Guide Reduce Overview (Traditional way Vs. MapReduce way) 

Organizing Hadoop into an Existing Enterprise 

Stacking Data from a RDBMS into HDFS, Hive, Hbase Using Sqoop 

Conveying Data to RDBMS from HDFS, Hive, Hbase Using Sqoop 

Apache Hive — Hive Vs. PIG — Hive Use Cases 

Discussion about the Hive data accumulating rule 

Explain the File setups and Records associations supported by the Hive condition 

Perform exercises with data in Hive 

Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts 

Hive Script, Hive UDF 

Join datasets using a collection of procedures, including Map-side joins and Sort-Merge-Bucket joins 

Use moved Hive features like windowing, points of view and ORC records 

Hive Persistence positions 

Stacking data in Hive — Methods 

Serialization and Deserialization 

Planning external BI mechanical assemblies with Hadoop Hive 

Use the Hive examination limits (rank, dense_rank, cume_dist, row_number) 

Use Hive to process ngrams on Avro-structured records 

Impala and Architecture 

How Impala executes Queries and its importance 

Preface to Data Analysis Tools 

Apache PIG — MapReduce Vs Pig, Pig Use Cases 

PIG's Data Model 

PIG Streaming 

Pig Latin Program and Execution 

Pig Latin : Relational Operators, File Loaders, Group Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF 

PIG Macros 

Parameterization in Pig (Parameter Substitution) 

Use Pig to motorize the arrangement and utilization of MapReduce applications 

Use Pig to apply structure to unstructured Big Data 

Preface to Apache Spark 

Spilling Data Vs. In Memory Data 

Guide Reduce Vs. Streak 

Strategies for Spark 

Streak Installation Demo 

Survey of Spark on a gathering 

Streak Standalone Cluster 

Conjuring Spark Shell 

Making the Spark Context 

Stacking a File in Shell 

Playing out Some Basic Operations on Files in Spark Shell 

Holding Overview 

Passed on Persistence 

Blaze Streaming Overview 

Fundamentals of Scala that are required for programming Spark applications 

Basic forms of Scala, for instance, factor types, control structures, gatherings, and that is just a glimpse of something larger 

Comprehension and Loading data into RDD 

Hadoop RDD, Filtered RDD, Joined RDD 

Changes, Actions and Shared Variables 

Shimmer Operations on YARN 

Plan File Processing 

Shimmer Structured Query Language 

Associating with Spark SQL 

Instating Spark SQL and execute Basic Queries 

Inspect Hive and Spark SQL Architecture 

Shimmer Streaming, its Architecture and consideration 

Different Transformations in Spark Streaming, for instance, Stateless and Stateful, Input Sources 

throughout each and every day Operations and Streaming UI 

Preface to MLib 

Data Types and working with vectors 

Models for usage of Spark MLLib 

Confinements of RDBMS and Motivation for NoSQL 

Nosql Design targets and Advantages 

Sorts of Nosql databases (Categories) — Cassandra/MongoDB/Hbase 

Top theory 

How data set away in a NoSQL data accumulating 

NoSQL database questions and update tongues 

Requesting and looking in NoSQL Databases 

Reducing data through lessen work 

Bundling and scaling of NoSQL Database 

Audit and Architecture of MongoDB 

Significance cognizance of Database and Collection 

Chronicles and Key/Values, etc. 

Preface to JSON and BSON Documents 

Presenting MongoDB on Linux 

Usage of various MongoDB Tools available with MongoDB pack 

Introduction to MongoDB shell 

MongoDB Data types 

Sludge thoughts and undertakings 

Question rehearses in MongoDB 

Data showing thoughts and approach 

Comparability among RDBMS and MongoDB data showing 

Model association between reports (one-one, one-many) 

Model tree structures with parent references and with adolescent references 

Troubles in showing 

Model data for Atomic errands and reinforce search 

Request building 

Programming interface and drivers for MongoDB, HTTP and REST interface, 

Present Node.js, conditions 

Node.js find and show data, Node.js saving and deleting data 

Requesting thoughts, Index types, Index properties, complete 

MongoDB watching, prosperity check, fortifications and Recovery options, Performance Tuning 

Data Imports and Exports to and from MongoDB 

Introduction to Scalability and Availability 

MongoDB replication, Concepts around sharding, Types of sharding and Managing shards 

Expert — Slave Replication 

Security thoughts and Securing MongoDB 

Generation of MongoDB application 

What is Cloud Computing? Why it has any kind of effect? 

Ordinary IT Infrastructure versus Cloud Infrastructure 

Cloud Companies (Microsoft Azure, GCP, AWS ) and their Cloud Services (Compute, amassing, sorting out, applications, mental, etc.) 

Use Cases of Cloud enrolling 

Layout of Cloud Segments: IaaS, PaaS, SaaS 

Layout of Cloud Deployment Models 

Layout of Cloud Security 

Preamble to AWS, Microsoft Azure Cloud and OpenStack. Resemblances and differentiations between these Public/Private Cloud commitments 

Making Virtual machine 

Layout of open Big Data things and Analytics 

Organizations in Cloud 

Limit organizations 

Procedure Services 

Database Services 

Examination Services 

Simulated intelligence Services 

Administer Hadoop Ecosystem and Spark, NOSQL in the Cloud Services 

Making Data pipelines 

Scaling Da

Sponsor Ads

About Manoj Singh rathore Professional   Digital Marketing Head and Founder

400 connections, 57 recommendations, 2,065 honor points.
Joined APSense since, November 6th, 2012, From New Delhi, India.

Created on Nov 3rd 2019 06:23. Viewed 314 times.


No comment, be the first to comment.
Please sign in before you comment.