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Comparing IBM DataStage with Rivery and Weld

Carolina Russ
Carolina Russ6 min read
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What is IBM DataStage

IBM DataStage (part of IBM InfoSphere Information Server) is a high-performance ETL and data integration platform that supports parallel processing and massive data volumes. It provides a visual design interface (DataStage Designer) to build data flows, along with features for metadata management, data lineage, and enterprise governance. DataStage can run on-premise or on cloud (via IBM Cloud Pak for Data) and integrates with IBM’s data quality and master data management solutions.

Pros

  • Parallel processing engine for high-throughput ETL, optimized for large data volumes.
  • Robust metadata management, data lineage, and governance via InfoSphere platform integration.
  • Supports on-premise, virtualized, and containerized (Cloud Pak) deployments for flexibility.
  • Extensive transformation library (data cleansing, lookups, joins) and connectivity (files, databases, mainframes, Hadoop).

Cons

  • High total cost of ownership: perpetual licensing and specialized administration needed.
  • User interface and development experience feel dated compared to modern cloud ETL tools.
  • Steep learning curve for job optimization (partitioning, parallel directives) and advanced features.

IBM DataStage Overview:

What I like about IBM DataStage

DataStage excels at processing huge data volumes with parallelism and pushdown optimization. The metadata-driven approach makes lineage tracking and governance straightforward.

What I dislike about IBM DataStage

Licensing and maintenance costs are high, and the UI feels dated. Complex jobs require specialized knowledge to optimize performance.
Read full review

What is Rivery

Rivery provides a SaaS ELT-based Data Integration platform that enables the seamless loading of data from diverse sources and custom API-based platforms into your preferred data warehouse. While River lacks on-the-fly transformations, it offers Post-load Transformations as compensation.

Pros

  • Supports custom integrations though native GUI
  • Has reverse ETL option
  • Supports Python
  • Has data transformation capabilities
  • Great customer support

Cons

  • Lack of advanced error handling features
  • Cannot transform data on the fly (ETL)
  • Complex pricing model
  • UI is lacking when working with larger complex pipelines
  • Product documentation is lacking

As a user on G2 puts it::

What I like about Rivery

As a data analyst, I find the tool really easy to use; it's intuitive how you connect to the different data sources and create your data pipelines.

What I dislike about Rivery

Read full review

What is Weld

Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.

Pros

  • Premium quality connectors and reliability
  • User-friendly and easy to set up
  • AI assistant
  • Very competitive and easy-to-understand pricing model
  • Reverse ETL option
  • Lineage, orchestration, and workflow features
  • Advanced transformation and SQL modeling capabilities
  • Ability to handle large datasets and near real-time data sync
  • Combines data from a wide range of sources for a single source of truth

Cons

  • Requires some technical knowledge around data warehousing and SQL
  • Limited features for advanced data teams

A reviewer on G2 said:

What I like about Weld

First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.

What I dislike about Weld

Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.
Read full review

IBM DataStage vs Rivery: Ease of Use and User Interface

IBM DataStage

DataStage Designer provides a visual canvas to build ETL jobs, but the interface is relatively old-school. Job parameters, parallelism, and performance tuning require specialized training. Monitoring and debugging use InfoSphere consoles.

Rivery

Rivery is known for its ease of use, especially for data analysts who need to connect different data sources and create pipelines quickly. Its intuitive GUI makes setup straightforward.

IBM DataStage vs Rivery: Pricing Transparency and Affordability

IBM DataStage

DataStage has high licensing costs (perpetual + support) and often requires dedicated hardware. Best suited for large enterprises with extensive ETL needs; cost-prohibitive for small/medium businesses.

Rivery

Rivery's pricing is complex and based on credits, which may not be straightforward for all users. Costs can rise significantly with increased data usage.

IBM DataStage vs Rivery: Comprehensive Feature Set

IBM DataStage

Features include: visual job design, parallel processing (MPP), pushdown optimization (offloading to DB/Hadoop), data quality integration, metadata-driven development, and enterprise governance. Also supports REST and mainframe data sources.

Rivery

The platform supports custom integrations, Python scripting, and reverse ETL, making it versatile for various data integration needs, but lacks on-the-fly transformation capabilities.

IBM DataStage vs Rivery: Flexibility and Customization

IBM DataStage

Custom logic can be written via routines (BASIC, Java, or Python) and embedded in jobs. DataStage can integrate with external schedulers (Control M) and monitoring tools. However, it’s not open-source, so feature evolution is tied to IBM’s roadmap.

Rivery

Rivery offers flexibility in custom integrations and supports post-load transformations, but its user interface may lack robustness for managing larger, more complex pipelines.

Summary of IBM DataStage vs Rivery vs Weld

WeldIBM DataStageRivery
Connectors200++200+200+
Price€99 / Unlimited usageEnterprise licensing (custom quotes, usually six-figure annual)€0.75 per credit *100MB of data replication
Free tierNoNoYes
LocationEUArmonk, NY, USA (IBM HQ)US
Extract data (ETL)YesYesYes
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL)YesNoYes
TransformationsYesYesYes
AI AssistantYesNoNo
On-PremiseNoYesNo
OrchestrationYesYesNo
LineageYesYesNo
Version controlYesYesNo
Load data to and from ExcelYesYesNo
Load data to and from Google SheetsYesNoNo
Two-Way SyncYesNoNo
dbt Core IntegrationYesNoNo
dbt Cloud IntegrationYesNoNo
OpenAPI / Developer APIYesNoYes
G2 Rating4.84.24.7

Conclusion

You’re comparing IBM DataStage, Rivery, Weld. Each of these tools has its own strengths:

  • IBM DataStagefeatures include: visual job design, parallel processing (mpp), pushdown optimization (offloading to db/hadoop), data quality integration, metadata-driven development, and enterprise governance. also supports rest and mainframe data sources. datastage has high licensing costs (perpetual + support) and often requires dedicated hardware. best suited for large enterprises with extensive etl needs; cost-prohibitive for small/medium businesses. .
  • Riverythe platform supports custom integrations, python scripting, and reverse etl, making it versatile for various data integration needs, but lacks on-the-fly transformation capabilities.rivery's pricing is complex and based on credits, which may not be straightforward for all users. costs can rise significantly with increased data usage..
  • Weldweld integrates elt, data transformations, and reverse etl all within one platform. it also provides advanced features such as data lineage, orchestration, workflow management, and an ai assistant, which helps in automating repetitive tasks and optimizing workflows.weld offers a straightforward and competitive pricing model, starting at €99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
Review the detailed sections above—connectors, pricing, feature set, and integrations—and choose the one that best matches your technical expertise, budget, and use cases.

Want to try a better alternative? Try Weld for free today.