Upsolver provides a no‑code data lakehouse platform that lets data engineers and analysts build, run, and monitor ELT pipelines directly on cloud object storage. Using a visual builder or SQL, the system auto‑generates optimized Spark or Flink jobs, adds cataloging, schema enforcement, and governance features, and delivers ready‑to‑query tables for downstream BI and analytics tools, eliminating the need for extensive coding and data movement.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
3OFounders
Product
Problem
Organizations often struggle to ingest, transform, and govern large volumes of raw data across cloud storage and data lake environments, requiring complex engineering effort and specialized tooling. This complexity slows time‑to‑insight and hampers the ability to build scalable, self‑service analytics pipelines.
Solution
Upsolver offers a no‑code data lakehouse platform that enables data engineers and analysts to design, execute, and monitor ELT pipelines directly on cloud object storage. Users define transformations through a visual interface or SQL, while the engine automatically generates optimized Spark or Flink jobs that run in the customer’s cloud account. Built‑in data cataloging, schema enforcement, and governance features ensure data quality and compliance without manual scripting. The platform integrates with downstream BI and analytics tools, delivering ready‑to‑query tables in a lakehouse architecture and reducing reliance on traditional data warehouses.
Target Audience
Primary customers are data engineering and analytics teams in mid‑size to large enterprises that need to build and maintain cloud‑native data lakehouse pipelines without extensive coding.
Features
- Visual pipeline builder with drag‑and‑drop components and native SQL editor
- Automatic code generation for Spark, Flink, and serverless execution engines
- Seamless ingestion from streaming sources (Kafka, Kinesis) and batch files (CSV, JSON, Parquet)
- Integrated data catalog, schema evolution, and data quality rules
- Fine‑grained access control and audit logging for governance compliance
- Direct connectors to major BI platforms and data science notebooks
- Scalable execution that leverages the customer’s cloud resources, eliminating data movement