IBM DataStage Certification Training

• IBM DataStage, its architecture and features
• Creating a sample DataStage job
• Aspects of DataStage parallelism, file storage and transformer stage
• Copy, Sort, Filter, Head, Tail, Aggregator, Merge and Lookup stages
• Difference between Lookup, Join and Merge stages
• Development, debugging and extraction using Teradata Connector
• DataStage Design implementation
• Preparing for IBM Certified Solution Developer – InfoSphere DataStage

• Software Developers, Architects and other professionals
• Data Analysts and ETL Developers
• Those looking for a career in Business Intelligence

You don’t need any specific knowledge to take up this ETL online training course. A basic knowledge of relational databases can be helpful.

• Most companies estimate that they’re analyzing a mere 12 percent of the data they have – Forrester Research
• Global Big Data Analytics market to reach $40.6 billion in four years – ResearchandMarkets
• A Senior ETL IBM DataStage Developer in the United States can earn $122,000 – Indeed

This DataStage training will get you up and running in deploying IBM ETL tool that is used for business analysis and reporting. IBM InfoSphere DataStage is a very versatile and scalable tool that can be used to work on any data source like MS Excel text files, CSV or any databases for data extraction. Data integration process creation is carried out using a graphical editor which removes the complexity of writing code. Getting the right IBM InfoSphere DataStage training and skills will help you apply for the best jobs in the industry, and it’s most commonly used by financial houses, retail chains, etc.

Introduction to the IBM Information Server Architecture, the Server Suite components, the various tiers in the Information Server.

Understanding the IBM InfoSphere DataStage, the Job life cycle to develop, test, deploy and run data jobs, high performance parallel framework, real-time data integration.

Introduction to the design elements, various DataStage jobs, creating massively parallel framework, scalable ETL features, working with DataStage jobs.

Understanding the DataStage Job, creating a Job that can effectively extract, transform and load data, cleansing and formatting data to improve its quality.

Learning about data parallelism – pipeline parallelism and partitioning parallelism, the two types of data partitioning – Key-based partitioning and Keyless partitioning, detailed understanding of partitioning techniques like round robin, entire, hash key, range, DB2 partitioning, data collecting techniques and types like round robin, order, sorted merge and same collecting methods.

Understanding the various job stages – data source, transformer, final database, the various parallel stages – general objects, debug and development stages, processing stage, file stage types, database stage, real time stage, restructure stage, data quality and sequence stages of InfoSphere DataStage.

Understanding the parallel job stage editors, the important types of stage editors in DataStage.

Working with the Sequential file stages, understanding runtime column propagation, working with RCP in sequential file stages, using the sequential file stage as a source stage and target stage.

Understanding the difference between dataset and fileset and how DataStage works in each scenario.

Creating of a sample DataStage job using the dataset and fileset types of data.

Learning about the various properties of Sequential File Stage and Dataset stage.

Creating a lookup file set, working in parallel or sequential stage, learning about single input and output link.

Studying the Transformer Stage in DataStage, the basic working of this stage, characteristics -single input, any number of outputs and reject link, how it differs from other processing stages, the significance of Transformer Editor, and evaluation sequence in this stage.

Deep dive into Transformer functions – String, type conversion, null handling, mathematical, utility functions, understanding the various features like constraint, system variables, conditional job aborting, Operators and Trigger Tab.

Understanding the looping functionality in Transformer Stage, output with multiple rows for single input row, the procedure for looping, loop variable properties.

Connecting to the Teradata Enterprise Stage, properties of connection.

Generating data using Row Generator sequentially in a single partition, configuring to run in parallel.

Understanding the Aggregator Stage in DataStage, the two types of aggregation – hash mode and sort mode.

Deep learning of the various stages in DataStage, the importance of Copy, Filter and Modify stages to reduce number of Transformer Stages.

Understanding Parameter Set, storing DataStage and Quality Stage job parameters and default values in files, the procedure to deploy Parameter Sets function and its advantages.

Project 1 :  Making sense of financial data

Industry :  Financial Services

Problem Statement : Extract value from multiple sources & varieties of data in the financial domain

Description : In this project you will learn how to work with disparate data in the financial services domain and come up with valuable business insights. You will deploy IBM InfoSphere DataStage for the entire Extract, Transform, Load process to leverage it for a parallel framework either on-premise or on the cloud for high performance results. You will work on big data at rest and big data in motion as well.

Highlights :

• Creating DataStage jobs for ETL process
• Deploying DataStage Parallel Stage Editor
• Data Partitioning for getting consistent results

Project 2 : Enterprise IT data management

Industry :  Information Technology

Problem Statement :  Software enterprises have a lot of data and this needs to made sense of in order to derive valuable insights from it

Description : This project involves working with the data warehouse existing in a company deploying the IBM DataStage onto it for the various processes of extract, transform, and load. You will learn how DataStage manages high performance parallel computing. You will learn how it implements extended metadata management and enterprise connectivity. This also includes combining heterogeneous data.

Highlights :

• Enforce workload & business rules
• DataStage deployed on heterogeneous data
• Integrating real-time data at scale.

Project 3 : Medical drug discovery and development

Industry :  Pharmaceutical

Problem Statement :  A pharmaceutical company wants to speed the process of drug discovery and development through using ETL solutions.

Description :  This project deals with the domain of drug molecule discovery and development. You will learn how DataStage helps to make sense of the huge data warehouse that resides within the pharmaceutical domain which includes data about patient history, existing molecules, and the effect of the existing drugs and so on. The ETL tool DataStage will help to make the process of drug discovery that much easier.

Highlights :

• Combining various types of data with ETL process
• Converting the data and transferring it for analysis
• Making the data ready for visualization & insights.

Project 4 :  Finding the oil reserves in ocean

Industry :  Oil and Gas

Problem Statement :  Finding new oil reserves is a very herculean task. There are huge amounts of data that need to be parsed in order to find where oil exists in the ocean. This is where there is a need for an ETL tool like DataStage.

Description :  This project deals with the process of deploying ETL tool like Datastage to parse petabytes of data for discovering new oil. This data could be in the form of geological data, sensor data, streaming data and so. You will learn how DataStage can make sense of all this data.

Highlights :

• Working with cloud or on-premise data
• Deploying DataStage for static or streaming data
• Converting data into the right format for analysis