You’re comparing Databox vs IBM DataStage vs Weld. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Weld | Databox | IBM DataStage | |
|---|---|---|---|
| Connectors | 200+ | 100+ | 200+ |
| Price | $99 / 5M Active Rows | $47 / month - 3 sources, 5 users | Enterprise licensing (custom, usually six-figure annual) |
| Free tier | |||
| Location | EU | US | Armonk, NY, USA (IBM HQ) |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | Yes (ODBC/flat files) | ||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.5 | 4 |
Overview
Databox is a cloud-based data visualization and business intelligence platform that helps businesses of all sizes track, analyze, and share their key performance indicators (KPIs). It offers a wide range of features, including the ability to connect to multiple data sources, create custom dashboards, and share insights with others.
Easy to use
Powerful features
Great customer support
Comprehensive data visualization
Real-time data updates
Expensive
Limited customization
Lack of advanced features
Limited drill-down capabilities
A reviewer on Capterra:
“Databox is always looking for ways to improve its interface. It's smooth - data updates quickly and it's easy to use. The customer service is super responsive, and always willing to step in and help out with the Databoards (dashboards) I'm working on. I would say it is my favorite tool to use as an analyst - ever!”
“Still missing some more obscure, less popular, integrations.”
Overview
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.
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).
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.
G2 Reviews:
“Best data integration tool on the market with a wide range of connectors and advanced data integration and quality features.”
“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).”
Overview
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.
Lineage, orchestration, and workflow features
Ability to handle large datasets and near real-time data sync
ETL + reverse ETL in one
User-friendly and easy to set up
Flat monthly pricing model
200+ connectors (Shopify, HubSpot, etc.)
AI assistant
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
A reviewer on G2 said:
“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”
Side-by-side
Databox is easy to use with a smooth interface and real-time data updates, making it a favorite among analysts for data visualization and reporting.
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.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side
Databox is easy to use with a smooth interface and real-time data updates, making it a favorite among analysts for data visualization and reporting.
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.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side
Databox is on the pricier side, which might deter smaller businesses or startups with limited budgets, despite its robust features and customer support.
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 offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side
Databox is on the pricier side, which might deter smaller businesses or startups with limited budgets, despite its robust features and customer support.
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 offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side
The platform offers powerful data visualization tools and comprehensive dashboards, but lacks advanced features and customization options, which could be limiting for some users.
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.
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.
Side-by-side
The platform offers powerful data visualization tools and comprehensive dashboards, but lacks advanced features and customization options, which could be limiting for some users.
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.
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.
Side-by-side
Databox provides a range of data visualization tools, but customization is limited, particularly for more complex reporting and analysis needs.
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.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
Side-by-side
Databox provides a range of data visualization tools, but customization is limited, particularly for more complex reporting and analysis needs.
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.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
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