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

"What I just did in an hour… would have taken me weeks. This is really cool. I don’t need to worry about data coming through the pipeline anymore. New data will show up and get pushed where it needs to go."

"Ascend's declarative engine is a game-changer for our team's productivity and its flex-code approach strikes the right balance, offering the flexibility we need—ranging from our simple SQL transforms to our most complex recursive PySpark transforms."

"The Ascend Platform is a phenomenal application for data engineering in companies like Be Power, which benefits from an end-to-end platform like Ascend instead of investing in custom open source solutions that take massive amounts of time and effort to build and operate. I’ve searched for something like Ascend for a long time.”

"We no longer look at individual data sources to pull into Ascend, we look at our entire enterprise—customer-facing and internal—and decide how to pull that into Ascend and then feed it into other systems for visualizations. Ascend is the gateway that processes all our data.”

Resources

The New Data Scale Challenge

From struggling with data volume and infrastructures to scaling data team capacity—what is the answer to increasing bandwidth?

Download Now

Whitepaper

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.

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PODCAST