Build Data Products
Like Software
Apply all the best practices of code-based development to
your datasets on a unified data engineering platform.
A Unified Data Engineering Platform
Build, automate, observe, and optimize data workflows with an integrated suite of data engineering tools.
Build More With Less Code.
Save time with pre-existing integrations, advanced automations, and a flexible plugin architecture.
A robust library of pre-built connectors
Ascend has the connectors you need to ingest data quickly and reliably. And it works well alongside existing data loaders. Your choice!
Multi-language, multi-framework data transformation
Ascend supports pushdown SQL and Python based transformations to all major data clouds, as well as modern frameworks such as dbt-style SQL and Ibis.
Go beyond orchestration with automation
Ascend's DataAware™ automation engine takes the grunt work out of defining and managing orchestration logic. Just specify your dependencies and let the platform do the rest.
Activate your data with Reverse-ETL
Ascend has internal and external sharing and governance to ensure your finished data products are available to others wherever they're needed.
How DataAware™ Automation Works
Take control of your operations at scale with the most advanced metadata powered system available.
Flexible Build Experience
Whether you're a CLI pro or UI power user, we've got you covered.
A Data Engineering Platform with Native DataOps
With modern DataOps at its core, and with access to unprecedented volumes of metadata, Ascend takes DataOps to entirely new levels of sophistication and visibility.
Git Integration
Fully integrate your workflows with Git to manage pipelines across high and low-code contributors
CI/CD Deployments
Quickly deploy versions between Dev, Staging and Prod with CI/CD and let Ascend's automation reprocess impacted datasets
Custom Deployments
Take full control of your environments with customizations to standard container models and deploy routines
Observability
Get end-to-end observability on your pipelines and drill down to the code level on high consumption areas
Data Quality
Define quality tests which are validated as part of every pipeline release and continuously monitor active deployments for ongoing issues
