Comparing Dell Boomi with IBM DataStage and Weld
Carolina Russ6 min read
What is Dell Boomi
Dell Boomi AtomSphere is a cloud-native iPaaS that provides ETL, API management, B2B/EDI integration, and workflow automation via a visual “Atom” runtime architecture. It supports 200+ connectors (SaaS, on-prem, databases) and allows users to build, deploy, and manage integration processes (called Atoms) in a drag-and-drop interface. Boomi’s AtomSphere runs on a lightweight runtime engine that can be deployed in the cloud or on-premise for hybrid scenarios.
Pros
- 200+ connectors for SaaS, on-prem, and big data sources.
- Cloud-native or on-prem Atom runtime allows hybrid deployments.
- Visual process designer with drag-and-drop mapping, enriched by shape-specific logic (e.g., function, decision, loop).
- Built-in error handling, version control, and CI/CD integration.
Cons
- Costly licensing structure (per-connection, per-Atom), which can escalate for high throughput or many connectors.
- Complex transformations sometimes still require scripting (JavaScript/Groovy), reducing low-code benefits for advanced scenarios.
- Learning curve: mastering Atoms, Molecules, and hybrid architecture requires time, particularly for non-technical users.
Dell Boomi Documentation:
What I like about Dell Boomi
Boomi’s AtomSphere makes deploying integration processes easy—Atommachines can run anywhere (cloud or on-prem), and the visual interface is intuitive for building mappings.
What I dislike about Dell Boomi
Licensing can be expensive, especially for high-volume data. Complex integrations can require coding despite the low-code promise.
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.
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.
Dell Boomi vs IBM DataStage: Ease of Use and User Interface
Dell Boomi
Boomi’s Integration Builder uses a web-based canvas to create process flows. Connectors and maps are configured via dialogs. Error-handling, version control, and deployment controls are integrated. Some users find building very complex workflows cumbersome despite the visual design.
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.
Dell Boomi vs IBM DataStage: Pricing Transparency and Affordability
Dell Boomi
Boomi’s pricing is multi-faceted—permanent Atom licenses, per-connection pricing, and usage-based charges for transactions. SMBs may need to request custom quotes to stay within budget.
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.
Dell Boomi vs IBM DataStage: Comprehensive Feature Set
Dell Boomi
Features: ETL/ELT processes, API management, EDI/B2B integration, workflow automation, data quality, and master data management. It also offers training, community forums, and professional services.
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.
Dell Boomi vs IBM DataStage: Flexibility and Customization
Dell Boomi
Custom scripting is supported via Groovy or JavaScript for complex transforms. Atoms can be deployed virtually anywhere for hybrid use cases. However, you rely on Boomi for core engine updates; it’s not open-source.
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.
Summary of Dell Boomi vs IBM DataStage vs Weld
| Weld | Dell Boomi | IBM DataStage | |
|---|---|---|---|
| Connectors | 200++ | 200+ | 200+ |
| Price | $99 / Unlimited usage | Subscription-based (per Atom/connection; starts ~$1000/month) | Enterprise licensing (custom quotes, usually six-figure annual) |
| Free tier | No | No | No |
| Location | EU | Austin, TX, USA | Armonk, NY, USA (IBM HQ) |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | No |
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | Yes | Yes |
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | Yes |
| Version control | Yes | Yes | Yes |
| Load data to and from Excel | Yes | Yes | Yes |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | Yes | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| G2 Rating | 4.8 | 4.3 | 4.2 |
Conclusion
You’re comparing Dell Boomi, IBM DataStage, Weld. Each of these tools has its own strengths:
- Dell Boomi: features: etl/elt processes, api management, edi/b2b integration, workflow automation, data quality, and master data management. it also offers training, community forums, and professional services. . boomi’s pricing is multi-faceted—permanent atom licenses, per-connection pricing, and usage-based charges for transactions. smbs may need to request custom quotes to stay within budget. .
- 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. . 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. .
- Weld: weld 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..
