What is Metabase?
Founded in 2014, Metabase claims to be a Business Intelligence solution built for everyone. Their goal is to be the easy, open-source way to empower anyone in the organization to work like a data analyst, even if they don’t know SQL. They pride themselves on accessibility and simplicity, offering exploration and queries that don’t require SQL expertise.
What is Whaly?
Whaly is an integrated, self-service business intelligence platform that enables business teams and data teams to work better together - streamlining the path from data to decisions. Our mission is to grow data adoption and company-wide trust in data by empowering business teams to access and analyze data, whenever and however they need it. Whaly includes data connectors, modeling, and visualization all in one.
TL;DR
Metabase can be downloaded as open-source for free, which makes it accessible with low effort. This is the big plus about Metabase, and makes it a good option if you need a quick-start BI platform to get you started on your data journey. However, as your business grow and scales to the next stage in your data journey, you’ll quickly find it has less robust features, and you’ll run into issues around data governance.
Data governance is the key issue with Metabase. Both data teams and business teams work from the same interface, which means there’s no clear line governing the ownership of data. As a result, the data is more vulnerable and potentially breakable, which impacts reliability. Data teams should be responsible for owning the data, which is what ‘protects’ it and ensures its accuracy. There should be some degree of separation from the environment in which the business teams freely play around with data, as this would reduce the risk of inaccurate or broken data.
Whaly, equally quick to implement, has 2 bespoke tools in our platform that’s tailored to data teams and business teams, respectively, and serves as the clear line of governance. Data analysts have the Workbench to work from, while business users have the Exploration layer through which they can play with the data. These two tools are integrated and are highly effective at communicating with each other. Whaly empowers data teams to do what they do best, instilling confidence in their data ownership, while giving business teams their own safe area that’s suited to them. Not only is the bespoke area for business teams an efficient way for them to self-serve data, you know it’s always going to be up to date, auditable and highly governed.
A few other points like slowness and lack of support and tutorials may also be a concern. Lastly, while the open-source option is free, the enterprise plan can suddenly give you sticker shock.
At a glance
| Feature | Metabase | Whaly |
|---|---|---|
| Open-source | Yes | No |
| Governance |
|
|
| Learning curve | Easy, though not as intuitive to navigate. | Easy |
| Implementation time | Easy to implement in a few minutes | Easy to implement in a few minutes |
| Scalability | Governance blockers will make it difficult to scale. Less robust features may not be suitable for more mature companies, as you grow. Large data sets lag. | Data governance is not an issue, which means you’ll have implemented these auditable best practices from the start. Robust features across all layers. Fast load time even for large data sets. |
| Self-service | Yes, for exploration and consumption. Configuration seems to require resources with SQL knowledge. | Self-service for modeling (data-savvy), exploration, and consumption. Configuration requires a main builder with SQL knowledge. |
| Speed | Low computing power, with reports of slow or lagging data sync and imports, particularly with large data sets or when multiple users in your are organization using it at once. Brief system crashes may occur. | Fastest speed/performance on the market built on latest technology |
| Modeling layer | They recently rolled out a modeling layer, but it is very limited to standard SQL. Not able to combine from multiple sources as it’s not integrated with data connectors or dbt. | Robust modeling layer that allows for combining data from any number of sources, with our Flow feature. There’s also a visual builder that doesn’t require SQL. |
| Exploration layer | Yes, but no easy drag & drop for asking questions. | Yes, with drag & drop interface |
| Visualizations | Basic capabilities | Comprehensive capabilities: Cohorts, funnels, bubble charts |
| Connectors |
|
|
| Embedding | Limited to public mode or on Enterprise plan only | Yes, available in every plan |
| Sharing reports | Push dashboards to Slack, Email | Push dashboards to Slack, Email, Google Sheets, Airtable and Webhooks |
| User Access Control | Limited
| Both row and column permissions supported |
| Working with CSV/Google Sheets | No | Yes |
| Pricing | Free with self-hosted on-prem open-source option. Paid with Cloud options start at $85/month (includes only 5 users), goes up significantly with Enterprise plan: starts at $15k/year. Paid viewer licenses. | Mix of usage-based and user-based. Cloud options start at $460/month, with always free Viewer licenses. Implementation is included, along with 1 training session. |
| Customer Support / Tutorials | Limited support. Lacking tutorials for non-technical people, despite claiming to be for non-technical users. | Whaly’s support team is highly reactive with SLAs in place; quick problem-solving. |
Cautions for Metabase
- Unable to adhere to data governance standards, which is a key issue:
- The line between where data teams and business teams work within Metabase is blurry, which means there’s no clear governance. On Metabase, everything is done on the same interface, accessible to all. This makes the data more vulnerable and potentially breakable.
- No support for version control, which is key to governance and compliance. As your team grows, you will need to be able to properly audit changes and evolutions of each model and metric. As the need to collaborate grows, this will become time-consuming and problematic. - There is a newly released modeling layer, but it is very limited to standard SQL.
- Not integrated with data connectors or dbt, which makes it difficult to easily combine data sources. - There’s not as much focus on the “non-technical beginner” as they claim
- It’s not as intuitive as it makes itself out to be. Some technical expertise and SQL knowledge is required to get started and to make the most out of the platform. Engineering resources are usually required at the start.
- Without SQL skills, it’s difficult to create custom dashboards, queries, and filter views.
- The visual editor is quite basic, without drag & drop.
- Lack of tutorials and documentation for non-technical business users - A decent starter solution, but is less robust
- Lacks key features like ad hoc reports, benchmarking, and trend or problem indicators. Described by users as “quick self-service analytics,” which is fine if you’re starting out - but not robust enough to scale with you as you grow. - While the basic open-source option is free, which is a big plus, the Enterprise plan is quite expensive. There’s no middle ground, which means it’s not tailored to businesses of all sizes as they grow.
- Slowness due to low computing power, which is problematic for scalability:
- Data import from other tools takes a long time. For larger data sets, dashboards and filter views tend to load very slowly and lags
- When multiple users from your organization are using Metabase simultaneously, it gets slow and buggy, which poses a problem for accessing data when you need it.
- Doesn’t work or scale very well with no-SQL databases like MongoDB
- Syncing data is also time-consuming
- Queries sometimes fail without a clear reason - Navigation can be confusing, which can leave you stuck without understanding why
- Lack of customer support
Whaly benefits
- Whaly is auditable with high governance standards
- Whaly has 2 bespoke tools within the platform - a Workbench for data analysts (a SQL version and a visual builder) to model the data, set the definitions, and have ownership over it. Separately, there’s the Exploration layer in which business users can play with the data in a safe way, and serve themselves the analytics they need, without breaking anything. The 2 tools communicate with each other effectively, but data teams and business teams have their own distinct “spaces” to do what they do best. This presents the clear line that ensures governance.
- We offer version control - Whaly has a robust modeling layer that allows you to combine as many sources as you want, with our integrated data connectors and a native dbt integration. Our modeling layer also has the Flow feature.
- Whaly was built to be business-first, and is highly accessible for non-technical users
- Whaly does require a main “Builder” with SQL knowledge to be the one to set up and own the platform.
- Our Modeling layer is accessible to anyone that’s data-savvy (intended for data teams), but does not require SQL knowledge. Data-savvy business users can also participate.
- Our Exploration layer is fully drag & drop, for no technical expertise is required. - Our platform has robust features like optimized queries, Flow, and selective sync, with our product evolving at a fast pace.
- Whaly is built on the latest technology, with the fastest speed/performance on the market
- While we don’t have an open-source or free version, we have flexible plans that are tailored to businesses of all sizes. Our viewer licenses are always free. With Metabase, while their starter plan seems cheaper, cost will add up quickly if you have more business users viewing the reporting. This means boosting data adoption across your company, which is undeniably a good thing, will become expensive.
- Highly reactive customer support that works in collaboration with you to troubleshoot and ensure you’re getting the most value out of Whaly
Summary
In summary, Metabase is a good quick-start BI that offers a free, open-source version, but you’ll likely encounter headaches as you start to scale your business. The lack of clear lines and boundaries around roles (data teams and business teams) will present data governance issues that will be increasingly difficult to tackle as your company grows. In addition, once your data needs start evolving, you’ll find that their features are limited and less robust.
To learn more about the differences or go further in depth, get in touch!