The UI popup that presents options for drilling through the data. To see how it works, check out Create charts with explorable data.
The act of summarizing data with a mathematical function, such as a summing up the values in a column, or counting the number of rows in a table. The resulting number might be called a metric, which is distinct from metrics in Metabase.
An application programming interface is an app’s defined way that other programs can communicate with it. Typically the interface is web-based (like Metabase’s API), using internet addresses like
/data/id=123 called routes to specify different actions for the application to carry out. Check out Working with the Metabase API.
Grouping aggregated results by a dimension, e.g the count of users broken out by country.
Sometimes called a “calculated” column. You can use custom expressions to add columns to the results of your query in the notebook editor. For example, adding a column that calculates the difference between the total and subtotal.
On dashboards, you can customize the click behavior of a question card to determine what happens when people click on the card’s table or chart. You can set up a chart to send people to another question, dashboard, or external URL. You can also use values from the chart to parameterize the URL, for instance, by inserting an ID into the URL string. See our Learn article on Custom destinations.
Similar to formulas in spreadsheet software, you can use custom expressions in the notebook editor to create custom columns, or to create more advanced filters and aggregations. For an in-depth guide, check out Custom expressions in the notebook editor.
A page in Metabase that arranges questions and text cards in a grid. You can add filter widgets, and wire up them up to individual cards on the dashboard by selecting the field to filter. See our docs on dashboards.
The structure of your information, including how that information relates to itself and the real world.
/reference/databases or by clicking on the book icon throughout Metabase, the data reference section lets you add and look up metadata about your databases and their tables and fields. See Exploring data with Metabase’s data browser.
An Enterprise Edition feature, data sandboxes are a set of boundaries that define access to data down to both columns and rows. You can coordinate data sandboxes with your SSO setup. To see a big-picture view of how to use data sandboxes, check out Embed Metabase in your app to deliver multi-tenant, self-service analytics.
A database used for analytics. The database could be a relational database, or a database designed specifically for analytical queries. See Which data warehouse should you use?.
An attribute of an entity or object by which a measurement or metric can by grouped and filtered. For example, the category of an ordered product is a dimension of an order, and the total number of orderes (the measure) could be grouped or filtered by that dimension.
Using an iFrame (an inline frame) to place either a single question or a dashboard in another app. The Enterprise Edition offers full app embedding, which lets you embed the whole Metabase app inside your own app.
Sometimes called a column, a field is an attribute of a database table. You can think of it like the “heading” of a column. For example, in the Sample Dataset, the
Category field contains the values “Doohickey,” “Gadget,” “Gizmo,” and “Widget.”
Field filters are special variables you can use in SQL queries that let you create “smart” filter widgets, and connect your SQL-built charts to dashboard filters. If you write queries in SQL, you should definitely learn how these work, as you can do things like create pre-populated dropdowns in your filter widgets. Check out Field filters: create smart filter widgets for SQL questions.
The equation that powers a custom expression, like the formula you’d enter in the cell of a spreadsheet. For example,
= [Subtotal] - [Discount].
A predicate expression (a question that resolves to either true or false) that you apply to a field to limit the results returned by a query. For example, you could limit the results of orders by only including orders where the value of the
Total field is over 100. In this case the predicate expression would be “
Total > 100?”. In SQL, queries are filtered using a
WHERE clause, like
WHERE Total > 100. See also filter widgets.
A UI element on a dashboard or SQL query where you can input a value or select one from a menu to filter results, like selecting
CA to limit results to the state of California.
Joining tables refers to combining results from multiple tables. To join two tables, say table A and table B, you’ll generally want to connect the foreign key in table A to the entity key in table B, so the database knows how to organize the results. Check out Joins in Metabase.
A numeric evaluation of an attribute belonging to an object of interest, like the count, the height, the average age, the sum total, etc. Measures are often broken out by one or more dimensions.
The friendly resident robot of Metabase who can integrate with Slack to help out with finding questions.
Information that describes data. You can add descriptions and assign data types to tables and fields in the data reference and data model sections of your Metabase to help people understand the nature and value of your data. See also The Data Model page: editing metadata.
In general in analytics, metric is synonymous with a measure. In Metabase, a capital-M Metrics is a saved aggregation with or without filters based on a single table. Admins can create Metrics to help keep your analytics organized by standardizing the way everyone calculates things like revenue, or lifetime value, or other key aggregations your organization relies on. See our docs on metrics.
A query written in the database’s query language, usually SQL. The native query editor includes a sidebar with three tabs: data reference, SQL variables, and SQL snippets.
The GUI interface for custom questions. You can also start with a simple question, and open up the notebook editor at any time. The notebook editor allows you to join tables and create custom column. Like questions composed with the query-builder, questions composed with the notebook editor will benefit from the action menu.
The root collection in a Metabase instance.
Your semi-private collection in Metabase (Admins can see the contents of all personal collections). Your personal collection is a great place to draft questions and dashboards, which you can move to a public collection when they’re ready to go.
A pivot table is a table that groups rows and columns, and includes summarial rows with aggregate values for those groupings. These aggregate values are usually referred to as subtotals and grand totals, though these aggregates could also be other values, such as averages.
The reason they’re called pivot tables is because you can rotate (“pivot”) a column 90 degrees so that the values in that column become column headings themselves. Pivoting values into column headings can be really helpful when trying to analyze data across multiple attributes, like time, location, and category. You can pivot multiple columns, or not pivot any at all.
Pulses are a set of questions, along with a schedule and email or Slack recipients, which will be run and sent out on that schedule. Note: Pulses will be deprecated in favor of Dashboard Subscriptons sometime in the near future.
See entity key.
In Metabase, a question is a query and its visualization. There are three types of questions: simple, custom, and native queries. Questions can be added to dashboards and collections. You can also set up an alert on a question.
A specific subset of a larger group of items, like a certain grouping of your customers. In Metabase, admins can define a segment, which is a named filter or set of filters, that can be applied to a GUI question to standardize how your organization defines a certain subset. See our docs on segments, or for a higher-level view, check out Keeping your analytics organized.
A small database that ships with Metabase so you can start asking questions and creating dashboards before you connect to your database. The Sample Dataset contains four tables:
Reviews. It’s an H2 database, and you’ll see the Sample Dataset used in examples all over Learn.
Stands for Structured Query Language. Developed starting in the 1970s, SQL is the reigning champion for talking to relational databases. See our Best practices for writing SQL queries
A snippet is a named bit of SQL code that you can insert in a SQL query. For an in-depth guide, see SQL snippets: reuse and share SQL code, or check out our docs on snippets. The Enterprise Edition lets you organize snippets into folders – check out our docs on SQL snippet folders and permissions.
Stands for Single Sign-on. An authentication system (often called auth) that lets people log in to access multiple, independent applications. See Authenticating with SAML and JWT-based authentication. For a high-level view on how SSO works with data sandboxing, check outEmbed Metabase in your app to deliver multi-tenant, self-service analytics.
Data’s natural habitat. In a database, a table is a series of fields, with the values of those fields arranged in rows, each row with a value corresponding to a field. A table is also a type of visualization, or chart, that resembles a spreadsheet, with columns corresponding to fields (or aggregations in the case of custom columns). Check out Everything you can do with the table visualization.
Typically a line, bar, or area chart where the x-axis is time, and each point on the line is a value at a fixed interval (once an hour, or once a day, etc.). See our Learn article on time series comparisons.