Explore our expert-made templates & start with the right one for you.
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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:
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.
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.
Explore our expert-made templates & start with the right one for you.