Ascend Data Orchestration Platform

Data Orchestration

Automate your fastest path to data engineering and analytics engineering success with DataAware Orchestration

Automate the Most Complex Data Engineering Challenges with
90% Less Maintenance​

Many of today’s current data orchestration frameworks are imperative by design, putting the burden on developers to design the tasks, connect them together via dependency relations, and pass them to a compute and infrastructure team/layer that manages the execution. This results in inflexible pipelines that break easily and are expensive to run and maintain.

However, Ascend’s data orchestration platform is declarative by design with a sophisticated control plane that enables the user to define the high-level blueprint of the pipeline, and the control plane does the rest. The data orchestration software via the control plane does the rest to manage the tasks that implement the blueprints meaning you can focus more on the business logic and transformations and less on the management and maintenance of the pipelines. 

Want to learn more about the declarative vs imperative model?

Multi-DAG Orchestration

  • Create a feed of cleaned, transformed data in one pipeline and simply subscribe to it in another

  • Any change made to the transformations upstream of the created feed will automatically flow through the feed to the downstream pipeline

Resource-Aware Workload Prioritization​

  • DataAware processing priority determine which components to schedule first when resources are constrained

  • Increasing the priority on a component causes all its upstream components to be prioritized higher

  • Negative priorities can be used to postpone work until excess capacity becomes available

Continuously Running Workloads​

  • Ascend’s Automation Cloud ensures that data pipelines are always running in the background

  • Eliminate the need for manual starts or a separate orchestration tool to schedule jobs ​

Mid-Pipeline Checkpointing​

  • Incremental data processing is safely stored in your private object store

  • Fast access to incremental processing stages

  • Never double process data without meaning to do so

Automated Rollback

  • Dynamically create, manage, and delete your clusters based on workload

  • Optimize resource efficiency while enforcing appropriate security boundaries

  • Optimize Spark parameters for every job, based on data and code profiles

Automated Backfill

  • Automate the process of backfilling data after deploying code fixes with DataAware intelligence

  • Detect code changes, trace the lineage of the previous version, and update the pipeline—automatically

Want to learn more about the data orchestration platform and how it can help you quickly and easily bring advanced automation to your data and analytics engineering efforts with the Ascend Data Automation Cloud?

From Our Customers


The New Data Scale Challenge
From struggling with data volume and infrastructures to scaling data team capacity—what is the answer to increasing bandwidth?
DataAware Podcast
With a variety of guests from all facets of data engineering and associated teams, episodes look in-depth at the role of data engineering and data teams, trends, best (and worst) practices, real world use cases, and more.
A Deep Dive Into Data Orchestration at Harry's
Learn how the Harry's data science team expedited ingesting, transforming, and delivering retail data feeds into a new, robust shared data model.