DataStage Course Overview
Highlights of Datastage Online training :-
* Very in depth course material with Real Time Scenarios for each topic with its Solutions for DataStage Online Training’s.
* We Also provide Case studies for DataStage Online Training.
* We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts.
* We provide you with your recorded session for further Reference.
* We also provide Normal Track, Fast Track and Weekend Batches also for DataStage Online Training.
* We also provide Cost Effective and Flexible Payment Schemes.
What is Datastage?
- Design jobs for Extraction, Transformation, and Loading (ETL)
- Ideal tool for data integration projects – such as data warehouses, data marts, and system migrations
- Import, export, create and managed metadata for use within jobs
- Schedule, run and monitor jobs all within DataStage
- Administer your DataStage development and execution environments
![](images/DataStage-Architecture.jpg)
What Is a Job?
- Executable DataStage program
- Created in DataStage Designer, but can use components from Manager
- Built using a graphical user interface
- Compiles into Orchestrate shell language (OSH)
Job Development Overview:
- In Manager, import metadata defining sources and targets
- In Designer, add stages defining data extractions and loads
- And Transformers and other stages to defined data transformations
- Add links defining the flow of data from sources to targets
- Compiled the job
- In Director, validate, run, and monitor your job
What are Job controlling Options?
Manually write job control
- The code generated in Basic
- Use the job control tab on the job properties page
- Generates basic code which you can modify
Job Sequencer
- Build a controlling job much the same way you build other jobs
- Comprised of stages and links
- No basic coding
What are Aggregator Functions:
- Sum
- Min, max
- Mean
- Missing value count
- Non-missing value count
- Percent coefficient of variation
Wrappers vs. Buildop vs. Custom:
- Wrappers: Wrappers good if you cannot or do not want to modify the application and performance is not critical.
- Buildops: Buidops good if you need custom coding but do not need dynamic (runtime-based) input and output interfaces.
- Custom (C++ coding using framework API): Custom is good if you need custom coding and need dynamic input and output interfaces
What are Different Roles In Datatage?
- Administrator – add/delete projects, set defaults
- Manager – import metadata, backup projects
- Designer – assemble jobs, compile, and execute
- Director – execute jobs, examine job run logs
What Will We Learn In Datastage Training?
- Design the jobs that extract, integrate, aggregate, load, and transform the data for your data warehouse or data mart.
- Create and reuse metadata and job components.
- Run, monitor, and schedule these jobs.
- Administer your development and execution environments.
Who should go for this DataStage training Course?
- Software developers, architects, and other professionals
- Data analysts and ETL Developers
- Those looking for a career in Business Intelligence
What Are The Prerequisites To Learn Datastage?
Our Experienced trainers will teach from basics, if you have a basic idea of relational databases is an added advantage for this course.
Scheduling Demo With Trainer:
If you would like to take the online demo for Datastage trainer can you please make an inquiry or fill the form for demo registration, one of our executives will arrange a meeting with the expert trainer.
Course Finished Certificate :
After finish, the course we provide Datastage course finished certificate of kits technologies looks like
DataStage Course Curriculum
- An Introduction of Data warehousing
- Purpose of Data warehouse
- Data ware Architecture
- OLTP vs. Data warehouse Applications
- Data Marts
- Data warehouse Lifecycle
- SDLC
- Introduction of Data Modeling
- Entity Relationship Model
- Dimensions and Fast Tables
- Logical Modeling
- Physical Modeling
- Schemas like Star Schema & Snowflake Schemas
- Fact less Fact Tables
- Introduction of Extraction, Transformation and Loading
- Types of ETL tools
- Key tools in the market
- Windows server
- Oracle
- .NET
- Data stage 7.5X2 & 8x
- Server jobs & Parallel jobs
- Administrator client
- Designer client
- Director client
- Import/export manager
- Multi-client manager
- Console for IBM information server
- Web console for IBM information server
- Data stage Introduction
- IBM Information server Architecture
- IBM Data Quality Architecture
- Enterprise Information Integration
- Web Sphere Data Stage Components
- About Web Sphere Data Stage Designer
- Partitioning Methods
- Partitioning Techniques
- Designer Canvas
- Central Storage
- Job Designing
- Creating the Jobs
- Compiling and Run the Jobs
- Exporting and importing the jobs
- Parameter passing
- System (SMP) & Cluster system (MPP)
- Importing Method (Flat file, Txt, Xls and Database files)
- OSH Importing Method
- Configuration file
- Oracle Database
- Dynamic RDBMS
- ODBC
- SQL Server
- Teradata
- Sequential File
- Dataset
- Lookup File set
- Peek
- Head
- Tail
- Row Generator
- Column Generator
- Slowly changing dimension stage
- Slowly changing dimensions implementation
- Aggregator
- Copy
- Compress
- Expand
- Filter
- Modify
- Sort
- Switch
- Lookup
- Join
- Marge
- Change Capture
- Change Apply
- Compare
- Difference
- Funnel
- Remove Duplicate
- Surrogate Key Generator
- Pivot stage
- Transformer
- Shared Containers
- Local Containers
- About DS Director
- Validation
- Scheduling
- Status
- View logs
- Monitoring
- Suppress and Demote the Warnings
- Peek view
- Create Project
- Delete Project
- Protect Project
- Environmental variables
- Auto purge
- RCP
- OSH
- Commands Execute
- Multiple Instances
- Job Sequence Settings
- Job Activity
- Job sequencer
- Start loop Activity
- End loop Activity
- Notification Activity
- Terminator Activity
- Nested Condition Activity
- Exception handling Activity
- Execute Command Activity
- Wait for file Activity
- User variable Activity
- Adding Check Points
- Restart able
- Data Quality
- Data Quality Stages
- Investigate Stage
- Standardize Stage
- Match Frequency Stage
- Reference Match Stage
- Unduplicated Match Stage
- Survive Stage