Authoring wizard for data tables

  • Across the data warehouse, Ursa Studio’s Object Workshop eliminates code, from integrating raw data sources to executing complex transformations.
  • The platform is powerful enough for data scientists yet intuitive enough for non-programmer analysts.
screenshot-2-1-1 1

Pre-built, no-code objects

You'll find the following no-code objects in Ursa Studio, ready for you to deploy.

IMPORT Import objects are used to upload flat files to the database. All types of flat files are supported (e.g., CSV, fixed width, tab-delimited). These files are kept in the database in exactly the format in which they arrive. Data is transformed using downstream objects, starting with semantic mapping.
You can register any pre-existing table in the database. This allows the table to be included in the Ursa Studio ELT process, although the table itself will not be affected by an ELT.
Semantic mapping objects conform the data to one or more profiled templates for downstream use. This can be as simple as mapping a column name or casting from string to a date, but the full power of the patterns of Ursa Studio's Object Workshop are also at your disposal for transforming the data.
The single stack is a general-purpose tool to link multiple tables into a single table, using any of the restrictions and transforms of Object Workshop. Single stacks can be used in any layer of the data journey.
Integrator objects are used to generate the union of records from different "input" objects or stacks. You define a single set of "output" fields to which all "input" fields will be mapped, and provide rules for how duplicate records, if encountered, should be resolved.
The simple timeline takes a single concept ⁠— either an event or an interval ⁠— and constructs an object whose instances, representing periods of time along an entity's timeline, are guaranteed to be non-overlapping.
The complex timeline is a flexible pattern that allows you to combine multiple intervals — including those you define based on events — into a single timeline. The resulting timeline is guaranteed to have no overlapping periods for the same patient (or whatever entity is designated).
Attribution timelines are used to identify a relationship between an attributed entity (e.g., a patient) and a single attributee entity (e.g., the primary care provider) from among many potential candidates (e.g., other physicians the patient has seen recently). The result is a set of records representing non-overlapping time periods when the attributed entity on the record is attributed to the attributee on the record.
Simple episode objects take events or intervals for the same patient (or other entity) and assign them an identifier representing a continuous episode during which those events or intervals continued to occur.
The medication therapy episode object represents a series of fills for the same type of medication with little or no gaps in time between the end of the last fill's supply and the next fill.
Bespoke model objects perform complex processing tasks (e.g., making predictions based on machine learning or deep learning models, applying modern natural language processing tools) using rich, flexible languages such as R and Python rather than SQL. Models are defined in the Advanced Analytics zone of Ursa Studio and invoked here, where they are used to generate an object.
Ursa standard objects represent objects with built-in business logic. Unlike the more flexible objects that can be created in Object Workshop, Ursa standard objects are designed to represent a specific type of object and only require light customization to fit into the data model.
Ursa reference objects load Ursa-curated reference data sets into the environment. These objects are typically managed by Ursa Health because they must link with Ursa Health Standard Reference files, which are kept in an Ursa-owned storage container.

Want to learn more about Ursa Studio?

Want to talk?

We’d love to hear about your ideas for innovating in your organization and see if we can help ease you past your pain points.