Review: Birst brings DIY to BI

With straightforward data access, automated modeling, and easy reporting tools, cloud-based Birst Enterprise is the data warehouse for the rest of us

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Loading and modeling
Birst uses a Spaces metaphor to compartmentalize each deployment, allowing separate lines of business in a company to access and analyze shared data sets. Each Space provides a repository for your data warehouse, report and dashboard settings, permissioned user access rights, and the like. On first run, a navigation page divides development and administration tasks into four key phases: loading, modeling, processing data, and user access.

Navigation is intuitive enough, but it would be better served with a wizard-driven guide, or perhaps displayed in a side panel, so users retain quick access to links.

Loading data into the warehouse is easy using any of the available options. Flat files can be uploaded through the browser, while a Java app, called Birst Connect, allows uploading from the desktop. And Birst's cloud-based Extractors tap into cloud sources such as Salesforce and Google Analytics.

The Connect tool can be run as a Windows service, so the desktop app isn't constantly running, and both Connect and Extractors support scheduling.

Tested against a Salesforce instance, Extractor's fine-grained controls let me pull in individual tables or entire databases. For each of the import methods, I found uploads to be fast and easy to manage.

The next step depends upon the cost of your subscription and the configuration mode of your Space: automatic, discovery, or advanced. Automatic mode will scan your data set to determine the primary keys and relationships, generate best-guess models, and populate a Space with prefab analytics. Discovery mode adds dashboards and visualizations, while advanced mode adds modeling and advanced reporting, giving you complete access to the entire Birst toolset. To get the best of both worlds, you can start in auto mode, let Birst do the heavy lifting, then upgrade your Space to advanced mode for fine-tuning.

Birst does a nice job of mapping relationships in well-defined, well-structured data sources. However, the UI would benefit from better organizational tools. Larger maps become unwieldy to traverse, and though you can override the mappings in the same interface, it's clumsy. My manual attempts to join related sources were sometimes hindered by Birst's hiding of data objects it considered irrelevant. (This could result from a simple misspelling in matching column names, for example.) If you prefer, you can load the data using an entirely do-it-yourself approach and map the relationships manually.

Once your data is loaded and mapped, it's easy to set measures (like revenues or quantities) and dimensions (like time periods), and Birst makes quick work of structuring hierarchical navigation paths and grains (depths) available to the reporting engine. For example, using my loaded sales and product databases, it was a simple matter of a few clicks to drill from global sales figures down to a specific salesperson or part number by specifying the requisite paths and grains to include.

Birst data flow diagram
Birst lets you quickly map multiple object sources into the data warehouse for processing.
At a Glance
  • Cloud-based Birst really does bring advanced analytics to business users.


    • Easy setup
    • Good data source management
    • Data loading and modeling accessible to nontechnical users
    • Easy, drag-and-drop data visualizations and reporting
    • Good support


    • Limited forecasting tools
    • Browser and iPad access only; no rich client for the desktop
    • No support for NoSQL data sources
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