Put an end to
“garbage in, garbage out.”

Big data sources such as event streams, logs and database change events (CDC) pose a constant data quality challenge to transformation pipelines, due to technical issues and human actions that cause late arriving data, out-of-order data, duplicates and frequently-changing schemas.

These data issues create negative impacts such as:

• Serving incorrect results to data consumers
• Wasted engineering effort to fix data downstream
• Complex and fragile transformation pipelines

Upsolver uniquely solves the data quality challenge upon ingestion, by enforcing delivery of exactly-once, strongly-ordered data for downstream analytics, and it does it at any scale or data freshness requirement.

• Data availability – on-time, exactly-once, strongly ordered data
• Data evolution – drift detection, adapt schema automatically, replay
• Data cleansing – in-stream filtering and masking of sensitive data
• Data observability – inspection, profiling, monitoring, alerting

Upsolver leverages cloud infrastructure to economically scale to whatever size you need while maintaining strongly ordered, exactly-once data, real-time cleansing operations and stateful transformations for data lake ETL.

Whether you’re a data engineer or analyst, you can build ingestion pipelines in minutes using wizards for configuring source, destination, and cleansing operations. Built-in observability ensures that your data is what you expect, and alerts you when it isn’t. Since Upsolver is serverless and cloud-native, you have no infrastructure to manage.