Sherpa vs Metabase

As a part of the competitive analysis series, we prepared a series of articles comparing Sherpa to other products on the market that could solve a similar problem. To be clear, Sherpa has no direct competition, no other tool on the market offers what Sherpa does. However, part of its functionality could be implemented with other products like Tableau, PowerBI, Excel etc in combination with custom-built data extraction and processing scripts typically built in-house. This series of articles focuses on the main differences and competetive advantages of Sherpa to other ways companies use to solve the problem of getting complex heterogenous enterprise collaboration data analytics.

One example of a tool that could be used to visualize the data is Metabase, which could be used to visualize the data with some level of interactivity. Unfortunately its feature set is relatively limited and presents the following challenges:

1. MB requires a database connection. Most enterprise collaboration tools (just like, basically, all SaaS) don’t offer that. Sherpa can tap into virtually any data source (APIs, Cloud storage, Google drive, datalakes, log files, web pages, filesystem, databases and, yes, Metabase too).

2. MB runs queries against the connected databases, which could be a problem if we’re talking about big systems and complex queries. Sherpa can process billions of records without creating the extra load on the production database.

3. MB only taps into clean data sources, something you can query with a relatively basic SQL. When the data is cleaned up, normalized and stored in a relational database, then MB could work. But, in my experience, the heterogeneous enterprise data is never like that and always requires a powerful ETL which MB doesn’t have (and Sherpa does)

4. MB uses SQL to process the data, which limits its applicability. Sherpa supports aggregation pipelines, Javascript and Python (and SQL too). So if you want to throw in, for example, a sentiment analysis for all stories posted on Mightycause, then it will require quite a bit of work with Metabase to visualize the results. It’s a lot easier to do that (or use any ML system) with Sherpa.

5. While MB has a decent number of standard visualizations, both their variety and flexibility pale in comparison with what Sherpa offers.

6. Some basic dashboards interactivity is available in MB, but it’s very limited, comparing to Sherpa

Sherpa has other features like ability to act on the data (e.g., send emails to a specific segment of users), advanced automation, processing inbound events, updating data/dashboards in realtime and more, but just the #1 in this list is actually enough to make MB unusable for complex heterogeneous enterprise data analytics in general and collaboration data specifically. Still, it’s a great tool for combining data from multiple SQL databases and we would highly recommend both their free opensource and paid enterprise versions for the data visualization tasks when the refined data is coming mostly from SQL-based databases!