Sold by: IBM Software
Deployed on AWS
IBM DataStage is a modern, cloud native, and secure data integration solution that enables you to transform, deliver, and enrich data at any scale and complexity. Deploy our best-in-breed parallel engine anywhere to power your batch ETL/ELT tasks on AWS.
4.1
Overview
IBM DataStage is a modern data integration solution to collect and deliver trusted data anywhere, at any scale and complexity, on and across multi cloud and hybrid cloud environments. Our solution allows you to save on data movement cost by enabling you the ability to deploy our best-in-breed parallel engine where your data is. With native connections to AWS hosted data stores, data lakes, and databases like Snowflake, S3, RedShift, and more, DataStage offers a seamless integration journey for your data wherever it resides across your enterprise.
IBM DataStage is built for convenient management of data offering automatic partitioning, elastic computation, and built-in quality functions. It services structured, semi-structured, and unstructured data and modern file and table formats like delta lake, iceberg, parquet, and more to future proof your investments. The result? A future-proofed platform that services all your batch integration needs.
Here are a few major highlights of our service:
- Jump between serverless compute on AWS or deploy containerized remote compute planes anywhere
- Toggle between ETL and ELT without pipeline configuration changes
- Leverage no-code, low-code, and high-code paradigms based on your preference
- Build quality pipelines for longevity with our native DevOps feature
Highlights
- Best-in-breed parallel engine and automated load balancing to process data at scale and maximize throughput for your AWS data lake and data warehouse projects.
- Extensive prebuilt connectors to move data between AWS, data warehouse, and on-premise endpoints. Increase developer productivity with hundreds of out-of-the-box, ready-to-use functions, and design and development capabilities.
- Design your data integration jobs once and deploy runtime components in your AWS environment to save development costs while eliminating data latency. Deliver data quickly and in a secure tool.
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Premium | IBM DataStage per 1 RU. Select the number of RUs to provision for your instance. | $8,700.00 |
Dimension | Description | Cost/unit |
|---|---|---|
IBM DataStage Overage | Additional overages applied as purchased resource units are consumed. | $834.00 |
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IBM DataStage on Cloud Pak for Data is a modern, cloud native, secure data integration solution that enables you to collect, transform, enrich, and deliver data at any scale and complexity. Bring IBM DataStage best in breed parallel engine to run data integration tasks in your AWS account.
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Our objectives are to help you increase the productivity of your business and IT and lever automated job design and out of the box, native integration with cloud data lakes, real time data sources, relational databases, big data, and NoSQL data stores.
Customer reviews
Poojasree M.
Unmatched Performance and Reliability for Enterprise Data Workloads
Reviewed on Dec 21, 2025
Review provided by G2
What do you like best about the product?
The most impressive aspect of DataStage is its high-performance parallel processing engine, which allows it to handle massive enterprise data volumes with ease. By utilizing "pipelining" and "partitioning," the system can process different stages of a job simultaneously across multiple CPU nodes. This means that instead of waiting for one task to finish before the next begins, data flows through the pipeline like an assembly line, ensuring that even petabyte-scale workloads are completed within tight processing windows.
Furthermore, its visual design environment offers a sophisticated balance between simplicity and power. The drag-and-drop interface allows engineers to build complex ETL logic using pre-built "Stages" for joins, lookups, and transformations without needing to write manual code. However, it remains highly extensible for developers; if a specific requirement isn't met by a standard component, you can integrate custom Python scripts or SQL, making it flexible enough for both standard reporting and complex data science pipelines.
Finally, DataStage excels in enterprise-grade reliability and governance, which is why it remains a staple in highly regulated industries like finance and healthcare. It integrates seamlessly with metadata catalogs to provide end-to-end data lineage, allowing users to track exactly how data has changed from source to target. Combined with robust error-handling and "Reject Links" that capture bad data without crashing the entire job, it provides a level of stability and auditability that many lightweight or open-source tools struggle to match.
Furthermore, its visual design environment offers a sophisticated balance between simplicity and power. The drag-and-drop interface allows engineers to build complex ETL logic using pre-built "Stages" for joins, lookups, and transformations without needing to write manual code. However, it remains highly extensible for developers; if a specific requirement isn't met by a standard component, you can integrate custom Python scripts or SQL, making it flexible enough for both standard reporting and complex data science pipelines.
Finally, DataStage excels in enterprise-grade reliability and governance, which is why it remains a staple in highly regulated industries like finance and healthcare. It integrates seamlessly with metadata catalogs to provide end-to-end data lineage, allowing users to track exactly how data has changed from source to target. Combined with robust error-handling and "Reject Links" that capture bad data without crashing the entire job, it provides a level of stability and auditability that many lightweight or open-source tools struggle to match.
What do you dislike about the product?
One of the most significant drawbacks of IBM DataStage is its prohibitive cost and complex licensing model, which often makes it inaccessible for small-to-medium businesses. Beyond the high initial purchase price, the "IBM Tax" includes ongoing maintenance and specialized infrastructure requirements that scale aggressively with data volume. Furthermore, because the tool is highly proprietary, organizations face heavy vendor lock-in; migrating logic out of DataStage to a modern, open-source-friendly stack like dbt or Airbyte is notoriously difficult and time-consuming.
From a technical standpoint, many engineers find the platform increasingly clunky and "legacy" compared to agile, cloud-native alternatives. While its parallel engine is powerful, it requires deep, specialized expertise to tune—settings like partition methods and buffer sizes are manual and unintuitive, leading to a steep learning curve for new hires. Additionally, while the newer "Next Gen" versions have improved, the ecosystem is still criticized for being batch-heavy, making it less agile for teams that require modern real-time streaming or "DataOps" automation.
From a technical standpoint, many engineers find the platform increasingly clunky and "legacy" compared to agile, cloud-native alternatives. While its parallel engine is powerful, it requires deep, specialized expertise to tune—settings like partition methods and buffer sizes are manual and unintuitive, leading to a steep learning curve for new hires. Additionally, while the newer "Next Gen" versions have improved, the ecosystem is still criticized for being batch-heavy, making it less agile for teams that require modern real-time streaming or "DataOps" automation.
What problems is the product solving and how is that benefiting you?
IBM DataStage primarily solves the challenge of data fragmentation and processing bottlenecks in massive enterprise environments. Large organizations often have data trapped in "silos" across legacy mainframes, modern cloud databases, and various third-party applications; DataStage provides a unified, high-performance bridge to extract and harmonize this information. Its parallel processing engine solves the "time problem" by breaking down petabyte-scale datasets into smaller chunks and processing them simultaneously, ensuring that critical business reports and data warehouses are updated within strict overnight windows rather than taking days to complete.
The primary benefit to you and your organization is data trust and operational efficiency. Because the platform includes built-in data quality and governance tools, it automatically cleanses and validates records as they move through the pipeline, reducing the risk of making business decisions based on "dirty" or inaccurate data. Furthermore, its "design once, run anywhere" architecture allows your team to build a data flow once and deploy it across on-premises servers or multiple cloud providers without rewriting code. This saves significant development time and future-proofs your infrastructure, allowing you to focus on gaining insights rather than troubleshooting manual data transfers.
The primary benefit to you and your organization is data trust and operational efficiency. Because the platform includes built-in data quality and governance tools, it automatically cleanses and validates records as they move through the pipeline, reducing the risk of making business decisions based on "dirty" or inaccurate data. Furthermore, its "design once, run anywhere" architecture allows your team to build a data flow once and deploy it across on-premises servers or multiple cloud providers without rewriting code. This saves significant development time and future-proofs your infrastructure, allowing you to focus on gaining insights rather than troubleshooting manual data transfers.
Ivan S.
Exceptional Performance and Connectivity with Intuitive Interface
Reviewed on Dec 03, 2025
Review provided by G2
What do you like best about the product?
Wide Connectivity, High Performance and Scalability, Intuitive Graphical Interface
What do you dislike about the product?
High Learning Curve, Infrastructure Dependency
What problems is the product solving and how is that benefiting you?
Complex data integration, Data transformation and cleaning
Max R.
Data Integration and Quality with DataStage
Reviewed on Jun 18, 2025
Review provided by G2
What do you like best about the product?
Best data integration tool on the market with a wide range of connectors and advanced data integration and quality features.
What do you dislike about the product?
I quite like the platform as a whole, but I believe it can improve regarding data lineage (it should indeed improve now with the arrival of Manta to the IBM portfolio).
What problems is the product solving and how is that benefiting you?
Help our clients work with integrated, qualified, and reliable data.
Banking
IBM Datastage for ETL
Reviewed on Mar 08, 2024
Review provided by G2
What do you like best about the product?
IBM InfoSphere DataStage is simple yet efficient tool for ETL processing.
It has the variety of stages to implement your designs and test the same at runtime.
It has got additional features compared to other ETL tools, which helps in debugging and error handling.
It has the variety of stages to implement your designs and test the same at runtime.
It has got additional features compared to other ETL tools, which helps in debugging and error handling.
What do you dislike about the product?
Datastage is UI is little at the backseat compared to other ETL tools.
Stages could be categorised based on functionalities.
Stages could be categorised based on functionalities.
What problems is the product solving and how is that benefiting you?
It is solving the data integration problems from variety of platforms and provide approciate data formats at the end user.
Like, JSON, Files, txts, DB , amd Bigdata etc
Like, JSON, Files, txts, DB , amd Bigdata etc
Information Technology and Services
Good product
Reviewed on Jan 31, 2024
Review provided by G2
What do you like best about the product?
Its speed. It is very fast and responsive. Support is good.
What do you dislike about the product?
a little hard to use and implement. hs few bugs
What problems is the product solving and how is that benefiting you?
fast data integration and processing