High-Scale* Data Ingestion
for Snowflake and AWS Data Lakes

Upsolver delivers production data (CDC, event streams) to your analytics users.

* Upsolver moves 25 petabytes of data every single month. That’s 35 gigabytes every second!

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A different breed of data ingestion tool

There are (too) many data movement tools out there. Upsolver is the go-to solution to extract, transform, and load large volumes of production data in cloud-native environments. For other problems there are other solutions.

We compete on scale, not connectors.

✅ Use Upsolver for:

→ Moving data into Snowflake or your AWS Lakehouse (Athena + Glue)

→ Replicating production databases (Postgres, MySQL, MongoDB)

→ Ingesting Kafka / Kinesis streams

❌ Upsolver isn't for:

→ Moving data between clouds or onto on-prem / legacy data warehouses

→ Extracting data from third party applications (e.g., Facebook Ads)

→ Running small batch transformations

Extract and load at any scale, without DIY complexity

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.

Integrations Get Started

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.

Documentation Get Started

Built-in 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.

Everything you need to keep data flowing

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.

Cardinality, density, and uniqueness

Assess data consistency with field level statistics that are always up-to-date in the Upsolver Observability UI.

Summary statistics

Find everything you need to know at a glance for numeric columns: mean, max, min, percentiles, or custom cumulatives.

Product Overview Get Started

Engineers ❤️ Upsolver

Read case studies

Designed for simplicity.
Priced for scale.

We don’t charge based on monthly active rows. Upsolver pricing is composed of a fixed software fee and a data volume rate that’s 1/10th the cost of other solutions.

See Pricing