Data Movement
for Data Developers

Deliver data generated by your prod environment to analytics users at scale, without sacrificing quality.

From structured transactional databases to semi-structured, interaction event streams, ingest prod data to warehouses and lakes.

Learn How Upsolver Works

Unlock the business value of your prod data

Your production environment constantly generates data, including user-triggered API events, sensor readings, and transactional logs, that can provide differentiated business value. But frequently-changing nested schemas and fluctuating data volumes leave this data siloed in engineering systems, or throttled by brittle analytics ingestion pipelines. Cue broken data contracts, erratic costs, ownership deflection, and ultimately, missing business deliverables.

Enter Upsolver.

Upsolver is the cloud-native data movement solution that’s designed to make it easy for data developers to deliver high volume, complex prod data to analytics users, without delay or quality blindspots. By decoupling compute instances from data and metadata storage—yes, just like Snowflake—, we guarantee highly reliable ingestion workflows that are fully auto-healing and never miss a beat.

Product overview

Three reasons developers ❤️ Upsolver

Extract and load in a single tool

High-effort, low-value data engineering work is keeping your moat data from generating value. Upsolver replaces that work with a single tool that extracts data from prod and loads it into analytics environments, without developer intervention.

Choose-your-own-code pipelines

For no-code data ingestion, simply configure the desired source, target, and in-flight pre-processing steps, then launch the job—all from the UI.

Simple Upsolver SQL is automatically generated and ready for CI/CD. Edit the SQL in an Upsolver worksheet before launching the job, or write and execute dbt models using the dbt-connector, for a low-code experience.

For the full development experience, build your pipeline in an Upsolver worksheet or using the Upsolver CLI or Python SDK.

Data quality and observability

Upsolver makes working with data easy by automatically mapping columns and data types between sources and targets, evolving the schema at pace with data even for nested data structures, and parsing and flattening arrays and JSON structs.

For everything else, there’s Upsolver Observability UI.

Set quality expectations at row and column levels and decide how you want violations to be handled in any Upsolver pipeline.
Detect data quality and pipeline issues in near real-time and set up alerting based on fully up-to-date systems tables.

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Observability superpowers in Upsolver

Proactive data quality

Proactive data quality is as simple as setting expectations on any data property for immediate on-failure alerting.

Data flow rate

Detect when source data runs dry or when a flood is coming to adapt downstream pipelines accordingly.

Data freshness

Monitor when data last arrived in each field to maintain data product SLAs.

Schema evolution

Prevent unpredictable production data payloads from breaking analytics pipelines. Upsolver automatically adds new columns to the target–unless you tell it not to.