Comparing Databox with dlt (Data Load Tool) and Weld
Carolina Russ5 min read
What is Databox
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.
Pros
- Easy to use
- Powerful features
- Great customer support
- Comprehensive data visualization
- Real-time data updates
Cons
- Expensive
- Limited customization
- Lack of advanced features
- Limited drill-down capabilities
A reviewer on Capterra:
What I like about Databox
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!
What I dislike about Databox
Still missing some more obscure, less popular, integrations.
What is dlt (Data Load Tool)
dlt is an open-source Python library for building data pipelines with a code-first approach. It provides pre-built connectors for many common data sources and handles schema inference, incremental loading, and retry logic automatically. Developers define pipelines in Python, making it highly flexible and embeddable in any orchestration environment.
Pros
- Open-source and free to use
- High flexibility and control via Python code
- 60+ pre-built connectors with automatic schema evolution
- Built-in incremental loading and state management
- Embeddable in any orchestration (Airflow, Prefect, cron, etc.)
Cons
- No graphical UI—code-first, so not accessible to non-developers
- Requires engineering effort to deploy and schedule (no managed SaaS)
- Limited built-in transformations compared to dedicated ETL tools
- Monitoring and observability must be built around code (no native dashboard)
- Smaller community and support compared to more established tools
A reviewer on Medium:
What I like about dlt (Data Load Tool)
dlt is lightweight, customizable, and removes a lot of the boilerplate around API ingestion. With just a few lines of Python, we were able to create robust pipelines that handle schema changes and incremental loads seamlessly.
What I dislike about dlt (Data Load Tool)
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.
Databox vs dlt (Data Load Tool): Ease of Use and User Interface
Databox
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.
dlt (Data Load Tool)
dlt has no graphical interface—pipelines are defined in Python code, making it easy for developers comfortable with code but inaccessible to non-technical users.
Databox vs dlt (Data Load Tool): Pricing Transparency and Affordability
Databox
Databox is on the pricier side, which might deter smaller businesses or startups with limited budgets, despite its robust features and customer support.
dlt (Data Load Tool)
As an open-source library, dlt is free to use. Users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid SaaS solutions.
Databox vs dlt (Data Load Tool): Comprehensive Feature Set
Databox
The platform offers powerful data visualization tools and comprehensive dashboards, but lacks advanced features and customization options, which could be limiting for some users.
dlt (Data Load Tool)
dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. It supports major destinations (Snowflake, BigQuery, Redshift, PostgreSQL, Databricks) and allows in-Python transformations or dbt integration.
Databox vs dlt (Data Load Tool): Flexibility and Customization
Databox
Databox provides a range of data visualization tools, but customization is limited, particularly for more complex reporting and analysis needs.
dlt (Data Load Tool)
Because pipelines are written in Python, dlt offers unmatched customization—developers can fetch from any API, implement custom logic, and integrate with any orchestration or monitoring framework. This flexibility requires engineering investment but allows tailor-made solutions.
Summary of Databox vs dlt (Data Load Tool) vs Weld
| Weld | Databox | dlt (Data Load Tool) | |
|---|---|---|---|
| Connectors | 200+ | 100+ | 60+ |
| Price | €99 / 2 connectors | €47 / month - 3 sources, 5 users | Free (open-source) |
| Free tier | No | Yes | No |
| Location | EU | US | DE |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | Yes |
| Transformations | Yes | No | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | No | Yes |
| Orchestration | Yes | No | No |
| Lineage | Yes | No | No |
| Version control | Yes | No | Yes |
| Load data to and from Excel | Yes | No | No |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | No | Yes |
| G2 Rating | 4.8 | 4.5 |
Conclusion
You’re comparing Databox, dlt (Data Load Tool), Weld. Each of these tools has its own strengths:
- Databox: the platform offers powerful data visualization tools and comprehensive dashboards, but lacks advanced features and customization options, which could be limiting for some users.. databox is on the pricier side, which might deter smaller businesses or startups with limited budgets, despite its robust features and customer support..
- dlt (Data Load Tool): dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. it supports major destinations (snowflake, bigquery, redshift, postgresql, databricks) and allows in-python transformations or dbt integration.. as an open-source library, dlt is free to use. users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid saas solutions..
- 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..
