Big Data, Smaller BillsOptimize the cost of running your data pipelines with Ascend's Unified Data Engineering Platform
Doing More, With Less
Cost reduction has quickly become the top priority. We’re here to help. When it comes to running your data pipelines efficiently, Ascend is committed to saving you and your business both time and money.
- No upfront commits. Our monthly pay-as-you-go plan gives you full access to the Ascend Unified Data Engineering Platform without being locked into a lengthy contract.
- Flexible Pricing. Choose between serverless and cluster-based pricing. With Ascend’s advanced auto-scaling features, you’ll never have to worry about paying for unused capacity again.
- 30-day free trial. Compare side-by-side how much cheaper it is to run your data pipelines on Ascend.
- Free Data Eng Consulting. Work with our data experts and architects to quickly prototype and build with Ascend.
Let’s Talk Cost Reduction
Schedule a 30-minute demo with a data engineer to learn how Ascend can help reduce your infrastructure cost.
How Ascend Delivers Big Data Cost Optimization
Ascend’s DataAware™ intelligence and pipeline optimizer ensures your pipelines run at optimal efficiency, actively reducing your infrastructure needs. Ascend intelligently dedupes workloads, garbage collects unused data, and optimizes Spark job parameters automatically — more efficient pipelines means smaller Spark clusters, and lower infrastructure bills.
Ascend dynamically launches new Spark clusters in seconds, auto-scaling both nodes and executors, sharing resource pools where safe to do so, and reuse resources whenever possible.
Ascend runs on nearly 100% Spot instances to keep your infrastructure costs down.
Fully Optimized Apache Spark-Based Data Pipelines
Create autonomous Apache Spark-based data pipelines with 95% less code. Define your data logic in Python, PySpark, SQL, and YAML interchangeably.
Add, remove, and edit data sets and logic in minutes. Ascend tracks full data lineage, backfilling and propagating changes automatically.
Ascend dynamically manages your Spark clusters based on workload and optimizes Spark parameters for every job based on data and code profiles.