Build, scale, and operate Apache Spark-based pipelines connecting to any sources and destinations of your choice. Simply define your desired inputs, transforms, and outputs in YAML, SQL, Python, or PySpark. Ascend will automate and optimize your pipeline, manage your underlying cloud infrastructure, and keep you from getting paged at 3am.
Pipelines-as-Code with the predictability and consistency of declarative data engineering. Describe pipelines with simple, compact definitions that easily integrate into your existing development and CICD tools such as Git, Gitlabs, Github, Jenkins, and CircleCI.
Combines declarative configurations and intelligent automation to manage the underlying cloud infrastructure, dynamically construct pipelines, and eliminate maintenance across the entire data lifecycle.
Fully managed, containerized Spark on Kubernetes. Simplified Spark jobs require no configuration, sizing, or tuning — simply write your data transforms, and let Ascend dynamically configure the rest.
Automatically ensures data integrity, tracks data lineage, de-duplicates data, and optimizes performance. Accessible from your favorite processing engines — Spark, Presto, or Hadoop, as well as tools like Jupyter and Zeppelin notebooks.
Deploy and run seamlessly across Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
“With Ascend, more people can build sophisticated pipelines to an extent I’d never have believed was possible.”
Grab 30 minutes with an Ascend solutions architect to see our Autonomous Dataflow Service in action!