DataStage is an ETL (Extract, Transform, Load) tool that forms part of the IBM InfoSphere. Widely used for the development and maintenance of Data Warehouses and Data Marts, this tool can extract data from various sources, perform transformations based on business requirements, and load the processed data into selected data warehouses. Originally launched by VMark in the mid-90s, it was later acquired by IBM in 2005 and renamed to IBM WebSphere DataStage, and subsequently to IBM InfoSphere.
In the realm of Business Intelligence (BI), DataStage plays a significant role in managing information. It provides a Graphical User Interface (GUI) for carrying out the ETL work. The ETL work is executed through jobs, which are individual units of implementable work that describe the data flow between various stages.
A job consists of several stages connected via links. Each stage serves a specific purpose, such as linking to source and target systems, carrying out data transformations, connecting to multiple file systems, etc. Links are used to connect these stages within a job and define the flow of data.
What is DataStage?
DataStage is an ETL tool that extracts data, applies transformations, and loads data from source to target systems. It supports various data sources such as sequential files, indexed files, relational databases, external data sources, archives, enterprise applications, etc., helping businesses gain valuable insights through business intelligence.
DataStage Features
DataStage can extract and load data from any source to any target.
Jobs designed on one platform can run on other platforms as well, if the job was designed for uniprocessing, it can be executed on an SMP machine.
Node Configuration is a technique used to create logical C.P.U. nodes, where a Logical C.P.U. represents a Node.
Partition Parallelism is a technique for distributing data across nodes based on partition techniques.
It can be utilized to build and load Data Warehouses that operate in batch, real-time, or as a Web service.
It handles complex transformations and manages multiple integration processes.
DataStage ETL Jobs
Server jobs - Run on a single node on the DataStage server engine and handle less volume of data with slower processing capabilities.
Parallel jobs - Run on multiple nodes on the DataStage parallel engine, handling large volumes of data with high processing speed.
Sequence jobs - Used for complex designs, allowing multiple jobs to run together. They enable programming controls in job workflow, such as branching and looping, providing different courses of action depending on whether a job succeeds or fails.
DataStage Overview
DataStage is a high-performance ETL (Extract, Transform, Load) tool developed by IBM for data integration tasks. It allows users to create workflows that extract data from various sources, apply transformations, and load the processed data into target systems.
Key Features
High-Performance: DataStage is designed for high-volume, high-speed ETL processing.
Data Integration: It can work with various data sources and targets, including relational databases, big data platforms, and cloud services.
Transformations: DataStage supports a wide range of transformations such as sorting, aggregating, filtering, and cleaning data.
Visual Development: The tool provides a visual development interface for easy creation and management of ETL workflows.
DataStage Architecture
The DataStage architecture consists of three main components:
Control Flow Engine: This component handles the control flow, coordinating the execution of tasks in a workflow.
Data Flow Engine: It processes data, performing transformations and moving data between stages.
Modules: These are reusable pieces of code that perform specific functions such as reading data from a database or writing data to a file.
DataStage Workflow Development
In DataStage, workflows are developed using a visual interface called the DataStage Designer. The following steps outline the process:
Define Sources and Targets: Identify the data sources and targets for your ETL task.
Create Stages: Create stages to read from sources, perform transformations, and write to targets.
Connect Stages: Connect the stages using paths, specifying how data moves between stages.
Configure Transformations: Apply necessary transformations to the data flowing through each stage.
Test and Deploy: Test the workflow in the DataStage Designer and deploy it to the appropriate environment for execution.
Code Sample
// Define a source stage to read from an Oracle database
Source ora_source
format DBMS_DBI.DBMS_DATABASE_SOURCE_FORMAT
database "mydb"
server "myserver"
port 1521
service "xe";
// Define a transformation stage to sort data
Transform transformer
Sort;
Conclusion
DataStage is a powerful tool for ETL tasks, offering high performance and flexibility in data integration. With its visual development interface and robust set of features, DataStage simplifies the process of designing and executing complex data workflows.