Upsolver Announces Iceberg Table Mirroring to Snowflake

For data engineers working with Apache Iceberg tables in Snowflake, the promise of querying externally managed data sources is exciting, but refreshing data to make it useful for reporting and deriving timely insights creates major challenges. 

While the ability to access and analyze this data offers tremendous potential, the reality of manual, ongoing maintenance has proved to be a significant hurdle to true data integration.

When inserts, updates, or deletes are applied to Iceberg tables, the metadata for the table changes and a new snapshot is committed. This process requires a manual refresh within Snowflake to ensure users have access to the most up-to-date information. This comes with a host of complexities and operational overhead.

Data engineers must write and orchestrate tasks to refresh the Snowflake table, adding to their workload and increasing the risk of errors. It is also essential for engineers to keep track of the snapshot filename and location to update the table, which can be a daunting task, especially in large-scale environments.

With data changes occurring randomly, determining the optimal time to run a refresh operation becomes a guessing game, potentially leading to inefficiency. Furthermore, running refresh operations when no data changes have occurred results in unnecessary compute costs, impacting resource utilization and budgets.

Introducing Upsolver’s Iceberg Table Mirroring to Snowflake Solution

As the go-to solution for ingesting high-scale data into the data lakehouse, Upsolver’s support for Apache Iceberg makes it perfectly positioned to extend its table management service to solve this problem.

Upsolver understands the challenges faced by data engineers in maintaining fresh data in order for users to run effective queries from Snowflake. This latest feature, the ability to mirror Upsolver-managed Iceberg tables to Snowflake, addresses these pain points head-on, offering an automated and cost-effective solution.

By leveraging Upsolver’s Iceberg table mirroring feature, data engineers will experience the following benefits:

1. Fresh data: Users will have access to the latest data available, ensuring they can work with the most current information without the need for manual refreshes.

2. Automatic updates: Data engineers don’t need to write and orchestrate tasks – Upsolver’s solution handles the entire process automatically, reducing operational overhead and minimizing errors.

3. Minimal costs: Refreshes are executed only when data changes occur, optimizing compute resources and minimizing unnecessary expenses.

With Upsolver’s Iceberg table mirroring to Snowflake feature, data engineers can focus on delivering value rather than grappling with the complexities of manual maintenance. This solution empowers teams to work more efficiently, leverage the latest data, and optimize resource utilization – all while reducing operational costs.

Mirror your Upsolver-managed Iceberg tables to Snowflake

Take Upsolver for a spin and see how it works out for you! Start with a free trial or get a guided tour from our solution architects. Existing customers can get started immediately by following the instructions in this guide on How to Query Upsolver Iceberg Tables from Snowflake.

Published in: Blog , Upsolver News
Upsolver Team
Upsolver Team

Upsolver enables any data engineer to build continuous SQL data pipelines for cloud data lake. Our team of expert solution architects is always available to chat about your next data project. Get in touch

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