Data Pipeline Orchestration | Intelligent Data Pipelines

Ditch the Pipeline Status Quo

How much of your day is spent combing through overly complex code, deciphering debug logs, and trial-and-error parameter tuning, all just to keep your pipelines running? Pipelines are critical, but they require far too much code, brittle configurations, and a disproportionate amount of time spent on maintenance. What’s worse is it only gets more challenging the more pipelines you build.

At a time when your data teams, their needs, and your data itself are all rapidly expanding, you need a data platform that is as dynamic as you. It’s time to ditch traditional pipeline development and start embracing Dataflows.

LESS CODE NEEDED WITH DATAFLOWS

Static
Pipelines

Imperative (Step-by-Step) Tasks

Intertwined code to manage logic, job orchestration, and infrastructure

Keeps running... just don't change anything

Dynamic
Dataflows

Declarative (End-State) Descriptions

Separation of logic and control flow, with automated management of jobs and infrastructure

Continuously running, adapting, and optimizing

Evolve Beyond Pipelines

Backed by the Dataflow Control Plane, Ascend Dataflows are smarter and more reliable than traditional pipelines. Build sophisticated flows with just a few lines of code, and productionize them in record time.

Declarative Configuration
Dataflow Control Plane
Elastic Data Fabric

Define Your End-State

Build based on your desired end-state. Period. Ascend makes it easy to create and visualize pipelines using SQL and Python. Use them interchangeably to build modular Dataflows with full lineage and dependency management. All development is operationalized by Ascend’s Dataflow Control Plane, with no need to recode logic or manually schedule runs.

The Powerhouse of Ascend

The Dataflow Control Plane takes your declarative configuration and makes it happen. Ascend combines declarative configurations (the “data blueprint”) with a continuous feedback loop to understand current state, determine new actions, and keep all pipelines running and optimized across the entire data lifecycle.

AUTO-SCALE TO YOUR NEEDS

The Ascend Dataflow Control Plane manages a multi-cloud, auto-scaled microservices architecture built on Kubernetes and Apache Spark for on-demand processing and auto-orchestration to handle near-limitless scale. As data or logic change, it scales with precision to optimize performance and costs.

Declarative Configuration: Define Your End-State
Build based on your desired end-state. Period. Ascend makes it easy to create and visualize pipelines using SQL and Python. Use them interchangeably to build modular Dataflows with full lineage and dependency management. All development is operationalized by Ascend’s Dataflow Control Plane, with no need to recode logic or manually schedule runs.
Dataflow Control Plane: The Powerhouse of Ascend
The Dataflow Control Plane takes your declarative configuration and makes it happen. Ascend combines declarative configurations (the “data blueprint”) with a continuous feedback loop to understand current state, determine new actions, and keep all pipelines running and optimized across the entire data lifecycle.
Elastic Data Fabric: AUTO-SCALE TO YOUR NEEDS
The Ascend Dataflow Control Plane manages a multi-cloud, auto-scaled microservices architecture built on Kubernetes and Apache Spark for on-demand processing and auto-orchestration to handle near-limitless scale. As data or logic change, it scales with precision to optimize performance and costs.

Designed for Seamless Integration

Ascend fits easily into your existing data ecosystem, whether you are just starting your pipeline journey, or have thousands already running. Quickly connect to any system to start building. Live Dataflows directly fuel downstream applications, analytics, and machine learning models.