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Upsolver enables you to use familiar SQL syntax to quickly build and deploy data pipelines, powered by a stream processing engine designed for cloud data lakes.
Upsolver excels at enabling data engineers, data warehouse administrators, and data scientists to quickly:
- Deliver real-time analytics in the data lake and data warehouse
- Implement self-service data engineering for your autonomous teams
- Generate data as a reliable, accurate, and simple-to-use product for your data mesh
- Replicate operational databases into the data lake and data warehouse (using CDC)
- Improve query performance for data in the data lake
- Reduce the cost and complexity of your production data pipelines
Here's how to get started with Upsolver:
Use built-in connectors to move data between popular data sources and destinations, such as cloud object stores, relational databases, search indexes, and data warehouses.
Data is represented as tables, including streams, search indexes, and files. Easily perform operations such as joins and aggregations using familiar SQL syntax.
You can efficiently inspect event streams, profile them for completeness, and replay them from any point in time. The schema automatically evolves to ensure consistent behavior for BI applications and reports.
Reliably extract data from a wide range of sources and move it to your data lake and data warehouse with a single command.
Easily insert, update, and delete data in your data lake and data warehouse. Transform, enrich, and join real-time and batch data using SQL.
Develop and inspect pipelines interactively. Integrate with existing tools and processes to test, deploy, and manage your data pipelines across environments.
Monitor performance and failures, and manage the SLAs of your business-critical data pipelines from a single pane of glass.
Easily analyze and understand how your data is processed by querying our task executions table.