Cognite Data Fusion Release: April 2022

Data governance is key to ensuring high quality throughout your data's lifecycle, and Cognite Data Fusion (CDF) has the tools and features to secure that the data meet your expectations. This release optimizes data management by introducing more granular access to data transformations using data sets.

Read more about this and the other improvements for the CDF April 2022 release below. We cover the main features in the five-minute video above. 

Scope transformation access to data sets

Protect access to CDF SQL transformations from ungoverned actions and visibility by associating a transformation to a data set. Users will only be able to see and run the transformations associated with the data sets they have access to.

- As a project admin granting users access to transformations, select the data sets a user can see and run transformations for under Scope in the Transformations capability dialog:

- As a user creating a transformation, select the data set you want to associate to a transformation in the Create new transformation dialog. Only users with transformation access to this data set can see and run the transformation.

This feature is also available in the CDF API, Python SDK, and Transformations CLI. Read more about transformations

Ingest data into sequence rows

You’ll now find Sequence Rows as an option in the Destination type field on the Transformations page in the CDF user interface and no longer need to use other developer tools like APIs and SDKs to ingest sequence rows into the Sequence resource type in CDF.

Develop applications using Java

Already, you can build solutions using Cognite's SDKs. Now, we introduce the Cognite Java SDK to interact with the CDF v1 APIs. The Java SDK is optimized for data publishing and allows you to write data efficiently with the upsert function. The Java SDK covers core CDF functionality, such as contextualization and data extraction. You’ll find all the info you need in the cdf-sdk-java repo, and we have updated the Cognite API documentation  with code samples on how to perform tasks with the Java SDK.

Change isStep setting for ingesting time series to CDF

When you stream time series data points into the CDF data model, the interpolation between the data points is controlled by the isStep property on the time series object. You can now change the isStep property value without recreating and re-ingesting the entire time series.