Database Training Programs

The Oracle Corporation is an American global computer technology corporation, headquartered in Redwood City, California. The company primarily specializes in developing and marketing computer hardware systems and enterprise software products - particularly its own brands of database management systems. In 2011 Oracle was the second-largest software maker by revenue, after Microsoft.

An Oracle database is an ORDBMS, provides open comprehensive integrated approach to information management. The purpose of a database is to store and retrieve related information. A database server is the key to solving the problems of information management. In general, a server reliably manages a large amount of data in a multiuser environment so that many users can concurrently access the same data. All this is accomplished while delivering high performance. A database server also prevents unauthorized access and provides efficient solutions for failure recovery.

Oracle 11g offers the benefits of Grid Computing delivering higher quality of service, lower cost, and greater flexibility. Higher quality of service results from having no single point of failure, a robust security infrastructure, and centralized, policy-driven management.

This course is designed for freshers who wish to make their career in Oracle Data base Administration and Oracle Data base development.

Structure of CCSP ORACLE

MODULES

• Introduction to RDBMS
  
• Oracle Database 11g: Introduction to SQL 2.0
  
• Oracle Database 11g: Program with PL/SQL 2.0
  
• Oracle Database 11g: Administration Workshop I
  
• Oracle Database 11g: Administration Workshop II
  
• Soft Skill Training
  

The Oracle Corporation is an American global computer technology corporation, headquartered in Redwood City, California. The company primarily specializes in developing and marketing computer hardware systems and enterprise software products - particularly its own brands of database management systems. In 2011 Oracle was the second-largest software maker by revenue, after Microsoft.

An Oracle database is an ORDBMS, provides open comprehensive integrated approach to information management. The purpose of a database is to store and retrieve related information. A database server is the key to solving the problems of information management. In general, a server reliably manages a large amount of data in a multiuser environment so that many users can concurrently access the same data. All this is accomplished while delivering high performance. A database server also prevents unauthorized access and provides efficient solutions for failure recovery.

Oracle 11g offers the benefits of Grid Computing delivering higher quality of service, lower cost, and greater flexibility. Higher quality of service results from having no single point of failure, a robust security infrastructure, and centralized, policy-driven management.

This course is designed for Candidates OR Professionals who wish to upgrade their technical skills with Oracle 11g Data base administration or looking for appearing OCA/OCP global certification on Oracle 11g.

In this course the candidate will learn the basics of relational data bases and the powerful SQL Programming language.

Details of the course is given below.

Fundamentals

• Introductionto PL/SQL
• Retrieving Data Using the SQL SELECT Statement
• Using Single-Row Functions to Customize Output
• Using Conversion Functions and Conditional Expressions
• Reporting Aggregated Data Using the Group Functions
• Using Subqueries to Solve Queries
• Using the Set Operators
• Manipulating Data
• Using DDL Statements to Create and Manage Tables
• Creating Other Schema Objects
• Controlling User Access
• Managing Schema Objects
• Managing Objects with Data Dictionary Views
• Manipulating Large Data Sets
• Retrieving Data by Using Subqueries
• Regular Expression Support
• IterativeControl:Nested Loops Using Composite Data types
• User-DefinedRecords
• IndexingTablesofRecordsUsingCursorsandParameters
• IntroductiontoExplicitCursors
• UsingExplicitCursorAttributes
• CursorFORLoops
• CursorswithParameters
• UsingCursorsforUPDATE
• UsingMultipleCursorsExceptionHandling
• HandlingExceptions
• TrappingOracleServerExceptions
• TrappingUser-DefinedExceptions
• RecognizingtheScopeofExceptionsUsingandManagingProcedures
• CreatingProcedures
• UsingParametersinProcedures
• PassingParametersUsingandManagingFunctions
• CreatingFunctions
• UsingFunctionsinSQLStatements
• ReviewoftheDataDictionary
• ManagingProceduresandFunctions
• ReviewofObjectPrivileges
• UsingInvoker'sRightsandAutonomousTransactions
• UsingandManagingPackages
• CreatingPackages
• ManagingPackageConcepts
• AdvancedPackageConceptsGettingtheBestoutofPackages
• PersistentStateofPackageVariables
• UsingOracle-SuppliedPackagesImprovingPL/SQLPerformance
• UsingDynamicSQL
• ImprovingPL/SQLPerformanceUsingandManagingTriggers
• IntroductionToTriggers
• CreatingDMLTriggers,PartI
• CreatingDMLTriggers,PartII
• CreatingDDLandDatabaseEventTriggers
• ManagingTriggersRecognizingandManagingDependencies
• IntroductiontoDependencies
• UnderstandingRemoteDependenciesUsingthePL/SQLCompiler
• UsingPL/SQLInitializationParameters
• DisplayingCompilerWarningMessages
• UsingConditionalCompilation
• HidingYourSourceCode
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SQL server is an RDBMS from Microsoft and SQL- Structured Query Language is the language used to interact with the Database and tables. An RDBMS (Relational Database Management System) allows storing, manipulating and retrieving data stored in various tables.

In addition to the data storage, it has support for Stored Procedures, Stored functions, Triggers, indexes and views. One of the advantages of view is security as it can restrict the display of few data fields for specific users. Popular SQL server versions used in software development houses includes SQL server 2005, SQL server 2008 and SQL server 2012 and SQL Server 2014. Even though SQL server 2014 is the latest at the time of this writing, SQL server 2008 and SQL server 2012 are still being used largely.

The 'MS SQL Server and SQL' course is provided at all the centers of CMS Talent Development Centers across India.

Querying Microsoft SQL Server 2016 (70-461)

• Overview of SQL Server and RDBMS
• SQL commands
• working with SSMS
• Triggers
• Functions
• SP
• Data backup,
• SSIS
• SSRS
  

MYSQL

• MYSQL - Introduction
  
• Create Database, Drop Database
  
• DML,DDL,DCL,TCL Statement
  
• Select Query
  
• Where, Order by, Group By, Having Clause
  
• Join, Sub Query, Index, Aggregate Function
  
• Procedure, Function, Trigger

Big Data and Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. This brief Course provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.

BENEFITS OF PROGRAM

• In our PLUS program, you would be entitled to attend live classes on new topics such as updated software, latest programs etc for an entire year.
• Topics like PYTHON, machine learning, Kafka, Scala, Cassandra, R integration with Hadoop etc. This way you can stay updated with all the latest trends coming in the big data market.
• You would notice that we are one of the few providers that provide SPARK as part of our package. So basically, you would be learning all of these great programs at the same place and that too at a affordable price.
• Our sessions are conducted by industry expert faculty members, where you would have a complete hands-on experience during the program.

BIGDATA AND HADOOP PROGRAM

Big Data Hadoop classroom training designed by world’s leading data experts and prepare you for the Cloudera (CCA-175) preparation. Hadoop is a software framework for storing and processing Big Data. It is an open-source tool build on java platform and focuses on improved performance in terms of data processing on clusters of commodity hardware.

• Hadoop comprises of multiple concepts and modules like HDFS, Map-Reduce, HBASE, PIG, HIVE and SQOOP to perform the easy and fast processing of huge data.
• Hadoop conceptually different from Relational databases and can process the high volume, high velocity and high variety of data to generate value.

Big Data Hadoop Pro Training

• 15 days Classroom (75 hours) + 4 days Online Training (12 Hours) (Java, Unix Python)

Live Project

We will provide 2 data sets to work on real live projects.

Introduction to Big Data - Self Study (2 hrs.) 

• Overview of Big Data Technologies and its role in Analytics
• Big Data challenges & solutions
• Data Science vs Data Engineering
• Job Roles, Skills & Tools

Development Environment - Self Study(1 hrs.)

Setting up Development Environment

• Setting up Development environment on User's laptop to be able to develop and execute programs
• Setting up Eclipse ( Basics of Eclipse like, import, create project, add JARs) to understand basics of Eclipse for Map Reduce and Spark development
• Installing Maven & Gradle to understand building tools

Unix Python - LVC (4 Hrs.)

Case Study: XYZ Telecom need to set up approprioate directory Structure along with permissions on various files on Linux file system

• Setting up, accessing and verifying Linux server access over SSH
• Transferring files over FTP or SFTP
• Creating directory structure and Setting up permissions
• Understanding File name pattern and move using regular expressions
• Changing file owners, permissions
• Reviewing mock file generator utility written in Shell Script, enhancing it to be more useful

Case Study: Developing a simulator to generate mock data using Python

• Understand your domain requirement, describe need of required fields and possible values, file format etc
• Preparing configuration file that can be changed to fit any requirement
• Developing python script to generate mock data in configuration file

HDFS - Classroom(5 Hrs.)

Case Study: Handling huge data set in HDFS to make it accessible to right user and remove non-functional requirements like backups, cost, high availability etc.

• Understanding the problem statement and challenges persisting to such large data to perceive the need of Distributed File System
• Understanding HDFS architecture to solve problems
• Understanding configuration and creating directory structure to get a solution of the given problem statement
• Setup appropriate permissions to secure data for appropriate users

Case Study: Developing automation tool for HDFS file management

• Setting up Java Development with HDFS libraries to use HDFS Java APIs
• Coding to develope menu driven HDFS file management utility and schedule to run for file management in HDFS cluster

Java - LVC (6 hrs.)

• Case Study: Design and Develop Phone Book in Java
• Implementing design into Java Code using Eclipse
• Compiling and Executing Java Program
• Enhancing the code with each learning, like Inheritance, Method overloading
• Further enhancing the code to initialize PhoneBook from a Text File by using Java file reading

Sqoop & Map Reduce - Classroom (10 Hrs.)

Sqoop

Case Study: Develop automation utility to migrate huge RDBMS warehouse implemented in MySQL to Hadoop cluster

• Creating and loading data into RDBMS table to understand RDBMS setup
• Preparing data to experiment with Sqoop imports
• Importing using Sqoop Command in HDFS file system to understand simple imports
• Importing using Sqoop command in Hive table to import data into Hive partitioned table and perform ETL
• Exporting using Sqoop from Hive/ HDFS to RDBM to store the output of Hive ETL into RDBMS
• Wrapping Sqoop commands into Unix Shell Script To be able to build and use automated utility for day to day use

Map-Reduce

Case Study: Processing 4G usage data of a Telecom Operator to find out potential customers for various promotional offer

HIVE - Classroom (5 Hrs.)

Case Study: Process a structured data set to find some insights

• Finding out per driver total miles and hours driven
• Creating Table, Loading Data, Selecting Query to load, query and cleaning of data
• Which driver has driven maximum & minimum miles
• Joining Tables, Saving Query results to table to explore and use right type of table type, partition schema, buckets
• Discussing optimum file format for hive table
• Using right file format, type of table, partition scheme to optimize query performance
• Using UDFs to reuse domain specific implementations

Pig - Classroom (5 Hrs.)

Case Study: Perform ETL processing on Data Set to find some insights

• Loading and exploring Movie - 100K data set to load data set, explore it and associate schema to it
• Using grunt, Loading data set, defining schema
• Finding Simple Statistic from given Data Set to clean up the data
• Filtering and modifying data schema
• Finding gender distribution in users
• Aggregrating and looping
• Finding top 25 movies by rating, joining data sets and saving to HDFS to perform aggregration
• Dumping, Storing, joining, sorting
• Filtering function for complex condition to reuse domain specific functionalities & avoid rewriting code
• Using UDFs

Spark Project

Case Study: Build a model (using Python) to predict production error/ failure (huge servers - applications/ software) with good speed by using computation power efficiently while considering processor challenges

• Loading and performing pre-processing to convert unstructured data to some structured data format
• Cleaning data, filtering out bad records, converting data to more usable format
• Aggregating data based on Response Code to find out server' performace from logs
• Filtering, Joining and aggregating data to find top 20 Frequent Hosts that generates errors

Oozie - Classroom (5 Hrs.)

Case Study: Setting up Data processing pipeline to work as per schedule in Hadoop Eco System comprising of multiple components like sqoop job, hive scripts, pig scripts, spark jobs etc.

• Setting up Oozie workflow to tigger a script, then Sqoop Job followed by Hive Job
• Executing workflow to run complete ETL pipeline 

Hbase - Classroom (5 Hrs.)

Case Study: Find out top 10 customers by expenditure, top 10 most buying brands, and monthly sales from data stored in Hbase which is in Key value pair

• Designing Hbase Table Schema to model table structure, decide families in table as per data
• Deciding families in table as per data
• Bulk Loading & Programatically Loading data using Java APIs to populate data into Hbase table
• Querying and Showing data on UI to integret Hbase with UI/Reporting

Live Project 1 - Classroom (5 Hrs.)

Project: ETL processing of retail logs

• To find demand of a given product
• Trend and seasonality of a product
• Understand performance of the chain

Live Project 2 - Classroom (5 Hrs.)

Project: Creating 360 degree view (past, present and future) of the customer for a retail company - avoiding repetition or re-keying of information, to view customer history, establishing context and initiating desired actions

• Explorating and checking basic quality of data to understand data and need for filtering/pre-processing of data
• Loading data into RDBMS table to simulate real world scenarios where data is persisted in RDBMS table
• Developing & executing Sqoop Job to ingest Data in Hadoop Cluster to perfrom further actions
• Developing & executing Pig script to perform required ETL processing on ingested Data
• Developing & executing Hive Queries to get reports out of processed data

Live Project 3 - Classroom (10 Hrs.)

Project: Twitter Sentiment Analytics - Collect and real time data (JSON format), and perform sentiment analysis on continuously flowing streaming data

• Creating and setting up Twitter App to generate Twitter Auth tokens to access twitter by APIs
• Building Flume Source to pull twits from Twitter
• Setting up Flume agent with Kafka sink to persist twits into Distributed Kafka topic
• Setting up Flume agent with Kafka Source and HDFS as Sink to backup twits on HDFS for batch processing
• Building & Executing Spark Job to perform Sentiment Analytics in real time on each incoming twits
• Creating Hive table on Twits and perform basic queries to understand Hive Serdes and dealing with Semi-structured data in Hive
• Writing and Executing Impala Queries to understand and overcome Impala's limitation of not being able to use Hive Serdes

Live Project 4 - Classroom (10 Hrs.)

Project: Machine Learning with TensorFlow - Build a solution which can recognize images on search words and can run on distributed computing like Hadoop/ Spark etc. for a photo storage company

• Setting up development environment to be able to use TensorFlow java APIs
• Developing Java Code using TensorFlow Inception Model using Java API to develop image recognition program in Java
• Developing Python code to train TensorFlow model to learn to training TensorFlow model, for a domain specific problem

Project: Developing a Chat-bot to offer an artificially intelligent customer help desk for an insurance company

• Identify client's most frequently asked questions and answers
• Develop a training data set
• Build TensorFlow NLP model which can understand questions
• Train the model
• Implement & Run the model