Enhancing data management with Apache Iceberg data lake integration

The data management landscape is evolving rapidly, with organizations seeking more efficient ways to handle vast amounts of information through innovations such as Apache Iceberg data lake integration.

As businesses generate and utilize increasing volumes of data, the need for flexible, scalable and integrated solutions becomes even more important. Innovations such as Apache Iceberg provide new opportunities for organizations to streamline data operations and maximize the value derived from datasets, according to Ron Ortloff (pictured), senior product manager, Iceberg and data lake, at Snowflake Inc.

Snowflake’s Ron Ortloff talks to theCUBE about Apache Iceberg data lake integration.

“At the core of Apache Iceberg is compute engine interoperability. So we’ve got a growing community of vendors that are contributing to the project,” Ortloff said. “We have now a wide variety of compute engines that support Apache Iceberg and this is where customers now have more choice. You have some shops that use Spark, some shops that use Snowflake. Now they can have a single copy of data in an open table format and be able to interoperate on top of that data.”

Ortlof spoke with theCUBE’s Dave Vellante and George Gilbert at Data Cloud Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the general availability and importance of Apache Iceberg and the future of data interoperability and integration across different compute engines. (* Disclosure below.)

Maximizing value through Apache Iceberg data lake integration

The choice between managed and unmanaged Iceberg tables depends on an organization’s specific needs and existing infrastructure. Managed tables offer a more integrated experience, simplifying data management by handling tasks such as compaction and clustering internally within Snowflake, according to Ortloff.

“So we do have a lot of customers that are in a situation where they’re using an external catalog, the DIY approach, where they’ve spun up their own maintenance jobs, but they’re getting tired of that,” he said. “They’re growing too big, it’s getting too complex. You can take those externally managed Iceberg tables in Snowflake and run a table conversion command, which then transfers control to the Snowflake catalog. They become a fully managed table in place.”

This flexibility extends to interoperability with other platforms, such as Spark and Databricks, Ortloff concluded.

“At the end of the day, Apache Iceberg is an open-source project. The great thing about the Apache Software Foundation is it has a very transparent,” he said. “Things are very, very transparent and visible. So there is the power of that sort of open-source and the long track record that the Apache Software Foundation, doing things like Apache Parquet, Apache Spark, these things are fundamental capabilities in our industry now.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of Data Cloud Summit:

(* Disclosure: Snowflake Inc. sponsored this segment of theCUBE. Neither Snowflake nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE