The Ascend Blog

Weekly release notes for the week of April 28, 2021. Stay updated with our latest updates, new features, and performance enhancements.

Weekly release notes for the week of April 14, 2021. Stay updated with our latest updates, new features, and performance enhancements.

Weekly release notes for the week of March 31, 2021. Stay updated with our latest updates, new features, and performance enhancements.

Weekly release notes for the week of March 17, 2021. Stay updated with our latest updates, new features, and performance enhancements.

Weekly release notes for the week of March 3, 2021. Stay updated with our latest updates, new features, and performance enhancements.

Weekly release notes for the week of February 16, 2021. Stay updated with our latest updates, new features, and performance enhancements.

We are excited to announce the release of our new Python SDK! This SDK sits on top of Ascend’s public API, and is dynamically generated based upon the Protocol Buffer and gRPC (which we use extensively) definitions of all components within the Ascend platform.

This feature leverages the same intelligent persistence layer that backs Queryable Dataflows and Structured Data Lakes, and joins it (pun intended) with the SparkSQL Thrift JDBC/ODBC Server to provide the ability to directly access and query your Dataflows from your favorite environment, whether it is a BI tool like Looker,

Today we’re excited to formally announce support for Scala & Java transforms. Not only does this expand our support to two of the most popular languages amongst data engineers, but marries this capability with the advanced orchestration and optimizations provided by Ascend.

Credentials Vault is a centralized place to store and manage secrets used by your dataflows. The feature makes it even easier to collaborate with others to quickly ingest from, and write to external data systems. It also empowers site administrators with an interface to audit and control all credentials in

A recursive transform is a transform that uses the output of its previous run as an input into the next run. This pattern is often used to incrementally aggregate data. In cases where historical data is substantially larger than the aggregated data, this pattern can result in significant reduction in

We run everything on Kubernetes and manage system state in MySQL plus a large scale blob store (Google Storage/S3/Azure Blob Storage).