Whaly's self-service BI platform is an alternative to Metabase. Here's what we've gathered around the pros and cons of both, to help you evaluate the BI tools on the market.
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.
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.
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.
|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|
|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.|
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!