Definition of DataStage
DataStage is an ETL tool which is used to Extract the data from different data source, Transform the data as per the business requirement and Load into the target database. The data source can be of any type like Relational databases, files, external data sources, etc. Using the DataStage ETL tool we provide quality data, which in return used for the Business Intelligence. DataStage first launched by Vmark, later it was acquired by IBM. DataStage was called earlier as ‘Data Integrator’.
Why do we need DataStage?
Before going to the query ‘Why we need DataStage’. Let us know about traditional batch processing.
Below is the process that was followed in traditional batch processing: –
1. Load data from source to Disk
2. Disk to perform transformations and then save to disk.
3. Disk to Target.
In the traditional batch processing becomes impractical with big data volumes, Very complex to manage lots of small jobs needed to achieve the requirement.
To overcome the above drawbacks we needed batch processing that can be done parallelly. For this need, we got the ETL batch processing system to deal with large volume data-parallel. Parallel processing can be done based on pipelining and partitioning.
How does DataStage works?
Datastage usually under goes below steps:
- We design jobs for extraction, transformation, and loading in a sequential job manner or Parallel manner.
- Schedule, run, and monitor the jobs.
- Create batch jobs.
Architecture of Datastage
Datastage usually has different components that would help us achieve the overall extraction, transform, and load.
- Administrator: – Manages the global settings and interacts with systems.
- Designer: – Here designer is used to create Datastage jobs, job sequences which in turn compiled into executable programs. Designer is mainly for the developers.
- Director: – This is used to monitor and manage the Datastage jobs. Used by DataStage support roles to monitor the jobs and fix job failures.
- Manager: – It is used to manage, browse, and edit the data warehouse repository.
The Terminology that we use are as below:
- Project
- Job
- Stage
- Link
Types of Jobs: – Parallel jobs, job sequences, and server jobs.
Parallel Jobs:
- Stages and links combined in a shared container.
- Reuse of instances of the shared container in various other parallel jobs. But the container can be used only within the job is defined.
Server Jobs:
- Used to represent sources, conversion stages, or targets.
- We have two stages: – active or passive stages.
Links:
- Links various stages in a job and indicate the flow of data when the job is run.
Server Architecture
Processing Stage Types
Datastage job usually consists of the stages, links, and transform. The stages are nothing but the flow of data from a data source to the target data source. The stage can have a minimum single data source as input or multiple data sources and one or more data output.
Let us discuss the various stages that we use in DataStage: In Job design various stages you can use are:
- Transform stage
- Filter stage
- Aggregator stage
- Remove duplicates stage
- Join stage
- Lookup stage
- Copy stage
- Sort stage
- Containers
Advantages and Disadvantages of Datastage
| Advantages | Disadvantages |
| Connect to multiple types of data sources | We need to either install of connecting to the server for the ETL work. |
| Large volume of data. Bulk transfer and complex transformation | No automated mechanism for error handling and recovery. |
| Refresh and synchronize data as much as needed. | We don’t have UNIX datastage client. |
| Reliable and Flexible to connect to different types of databases. | Affording the software might go expensive for small or mid-size companies. |
| Partitioning algorithms | |
| Easy integration and a single interface to integrate heterogeneous sources. | |
| Performs well in both Windows and Unix servers. |
Features of Datastage
- It supports the transformation of large volume data.
- Real time data integration which enables connectivity between data sources and application.
- Optimize hardware utilization.
- Supports collection and integration.
- Powerful, Scalable, Speed, flexible, and effective to build, deploy, update, and manage your data integration.
- Support big data and Hadoop.
Uses of DataStage in various fields or companies:
Presently the usage of the Datastage is gone worldwide. The fields or companies that use the DataStage are Cooper Companies, SAS, etc.
To know more about this, use the below link which would give a picture:
https://enlyft.com/tech/products/ibm-infosphere-datastage
Career path for DataStage :
Current scenario, ETL tool usage is on rise. And we can see that ETL is not confined to a particular industry. ETL is used in each and every industry to manage the data and make it a usable format.
We do have other tools called Informatica, Talend ETL tool which is cheaper than datastage.
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To be more specific to the career path we can learn data analytics which would be easier to handle and be a career milestone in the career path since you already have good knowledge in ETL tools.
Conclusion
Things that need to be remembered from the above session are the definition and flow of the datastage job.
DataStage is an ETL tool which is used to Extract the data form different data source, Transform the data as per the business requirement and Load into the target database. The data source can be of any type like Relational databases, files, external data sources, etc. Using the DataStage ETL tool we provide quality data, which in return used for the Business Intelligence.
Datastage usually under goes below steps:
- We design jobs for extraction, transformation, and loading in a sequential job manner or Parallel manner.
- Schedule, run, and monitor the jobs.
- Create batch jobs.
Key aspects as below: –
- Data transformation
- Jobs
- Parallel processing
DataStage has four main components,
- Administrator
- Manager
- Designer
- Director
Refresh and synchronize data as much as needed. Reliable and Flexible to connect to different types of databases. Partitioning algorithms Easy integration and a single interface to integrate heterogeneous sources.
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