DataStage is one of the most powerful ETL tools with the feature of graphical visualizations for data integration. It offers a means of rapidly generating operational data marts or data warehouses. It is an integrated set of tools for developing, designing, running, compiling, and managing applications that extract data from one or more data sources, accomplish multi-part conversions of the data, and load one or more target files or databases with the consequent data.
Some of the technological benefits gained by deploying DataStage are mentioned in the below table:
|Security||Security levels of private, shared, and collaborative for researchers|
|Interface||Option for annotating files and accessing them from anywhere|
|Storage||Repository stores the data permanently|
|Communication and documentation||Supported through data flows and transformations|
|Infrastructure||Common for data movement and quality|
|Processing engine||Supports parallel processing|
Some of the additional DataStage benefits are outlined below:
- Requires only a single interface to integrate heterogeneous applications
- Supports both Windows and Linux servers
- Does not require intensive training
DataStage follows the client–server architecture. Different versions of DataStage have different types of client–server architecture. Basically, the DataStage architecture comprises following components:
To start DataStage client, attach to a DataStage project first. Every complete project might comprise:
- DataStage jobs: DataStage jobs is a collection of jobs used for loading and maintaining a data warehouse.
- Built-in components: These are the predefined mechanisms used in a job.
- User-defined components: These are the modified mechanisms generated using the DataStage Manager. Every user-defined component executes a particular task in a job.
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A DataStage job consists of a sequence of specific stages, connected together to define the flow of data from a data source to another data store or data warehouse. Every stage explains a specific database or procedure. Stages are added to a job and connected together with the help of DataStage Designer.
Data properties are defined by:
- Table definitions: Table definitions are used to identify the data, i.e., the data of the table or explanation of the specific columns or anything that is required to specify the data.
- Data elements: Every data element defines one type of data that can be kept in a column. DataStage has several predefined data elements signifying usually required data types. There is also a provision to describe own data elements as well.
- Transforms: Transforms is used to change and clean the data by converting it into a format required to save and practice in the final warehouse. DataStage offers a large collection of built-in transforms.
A stage is categorized into two types, active or passive. A passive stage allows access to databases for the mining or scripting of data. Active stages define the movement of data and offer mechanisms for merging collecting data, data streams, and transforming data from one data type to another type.
DataStage is divided into three server components:
- Repository: A central store that contains all the information required to build a data mart or data warehouse.
- DataStage Server: Runs executable jobs, under the control of the DataStage Director, that extract, transform, and load data into a data warehouse.
- DataStage Package Installer: A user interface used to install packaged DataStage jobs and plug-ins.
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DataStage is divided into four client components:
- DataStage Manager- It is a graphical tool that permits us to view and manage the contents of the DataStage Repository. DataStage Manager allows us to browse, import, and edit metadata about targets, transformations and data sources.
- DataStage Designer- DataStage Designer is used to constructing jobs by creating a graphical design that models transformation of data and flows from the data source over the target warehouse.
- DataStage Director- The DataStage Director allows us to monitor, run and control jobs constructed in the DataStage Designer.
- DataStage Administrator- The DataStage Administrator allows us to group DataStage users, control the removal of the Repository, and, if NLS is mounted, install and manage locales and maps.
Why Learn DataStage?
When it comes to ETL activities, Datastage is a tool which is irreplaceable. Despite of having many other ETL tools in the market, Datastage is considered as one of the most powerful data warehousing tool. This tool is perfectly suitable for people who aspire to become data analysts and data science professionals, business intelligence experts, etc.
However in order to get it is highly imperative to get a DataStage certification training to get better jobs. Intellipaat is here to assist us in achieving our goals with world-class Online Datastage training that will help us stand out from the crowd.
Enroll in our Online Datastage Training and certification Course today!