How can I get the full context of this data?
Last updated
Last updated
Select Star brings together a breadth of contextual information about your datasets in one place.
You can see from one location how a dataset is being used, which other datasets are commonly used in with the data, and who uses the data.
There are a number of different ways to explore the data context, including:
From a table page, you can immediately see columns that are frequently used to join to other columns, identified by the icon.
You can identify columns used to join to other tables from the database page as well, and even filter to show those columns.
The Queries & Joins tab can be especially helpful for new Analysts joining the organization who want to understand how a dataset is used.
Popular Queries shows the queries run most frequently on the database, sorted by popularity.
The Popular Joins tab shows the tables that are most frequently joined to the current table.
Recent Queries shows the 50 most recent queries run on that database since the last metadata sync. This can give a broader view of how different people in your organization use this dataset.
On each of these tabs, you can click the Query Name or Join Condition to see a sample query in more detail.
The right sidebar contains helpful information, such as the different tables used in the query and join conditions.
The Related tab on a database table shows other relevant data objects: Related and Similar tables.
Related Tables are those that are frequently used together in SELECT queries.
Similar Tables are those which share a similar or identical structure.
The Top Users tab can give a high-level overview of who is using a table or its related dashboards.
Top Table Users are people or accounts who run queries on the table the most.
The Top Dashboard Users are the people who most frequently run the Downstream Dashboards connected with this table.
Dashboard pages also have a Top Users tab, which shows the people who run that specific dashboard most often.
There are two types of tags: Category Tags, and Status Tags.
Category tags are used to group information relevant to a department or project. You can have a Sales or Marketing tag to group information relevant to those departments.
Status tags are used to indicate data quality. Mark any datasets or fields as Certified, Sensitive, Deprecated, or any other status you'd use to define the state of the data.
Category tags are especially helpful for understanding the context of the data. You can click on a tag and see which other datasets have been determined by data experts in your organization to be in a similar business line.
Status tags can help understand which datasets are old and unused, as well as which datasets are actively used.
Learn more about how tags work and how to manage tags from the links below.
The popularity score is a metric that indicates how popular your data assets are amongst your team in terms of usage and activity. This is measured across all your data sources taking as a reference assets of the same type. These types are Tables, Columns, Dashboards, Fields and Queries.
You can use the popularity score to filter and sort assets in table views across our Platform. When looking at popularity, bear in mind that service accounts are not considered for the popularity score. Make sure you Mark Service Accounts in settings do remove them from our calculations.
With Field Usage labels in Select Star, now you can identify how the column is used in the downstream object, either table, view or dashboard.
When showing you column-level lineage for your data assets, the following labels are visible:
As Is - This label is used when a Field has been replicated.
Aggr - This label is used when a Field is aggregated. Ex. if used in count, avg, etc.
Transformed - This label is used when a Field has been transformed. Ex. if used in to_varchar, etc.
Filter - This label is used when a Field has been used as a filter. Ex. if used in where clause.
How does it work?
For a given table, if a column is used in a downstream table or a dashboard, Select Star identifies the lineage of the column, and applies the appropriate label.
For the example shown below, CUSTOMERS table is one of the tables used to create the view CITY_FIELDS, and column total_transactions is created by applying a count on the customer_id column.
Thus when viewing the column-level lineage, for that view, Select Star shows the label Aggr.
