[Interview] “We Have to Innovate, but We Also Have to Do It Economically”: EDB Targets the Market With “WarehousePG”
IT DAILY ·
✦ AI Summary
EDB has launched a push into the market with “WarehousePG” in response to the growing adoption of Postgres SQL and changes in the DB market.
Vice President Frank C. Sidi said the spread of AI adoption and demand for cost savings are driving demand for open source-based DBs.
EDB has proposed specialized engines by workload and an integrated data platform, and “WarehousePG 19” is targeting an official release in the first half of next year.
EDB has launched a push into the market with “WarehousePG.” IT Daily interviewed Frank C. Sidi, EDB vice president of global analytics & AI sales engineering, on site at the “EDB Postgres AI Summit Seoul 2026,” held on the 3rd at Sofitel Ambassador Seoul in Jamsil, Seoul. The photo shows Frank C. Sidi, EDB vice president of global analytics & AI sales engineering.
The interview was conducted in article format, and the topics focused on the background behind companies’ growing adoption of Postgres SQL, changes in the DB market, and EDB’s data and AI strategy. Sidi noted that companies need to drive innovation, but must do so while keeping economics in mind.
He said the expansion in companies adopting Postgres SQL is being driven by both the spread of AI adoption and the demand for cost savings. He explained that as AI adoption accelerates, demand for cost reduction is also rising, expanding companies’ need to adopt open source-based DBs.
Against this backdrop, Postgres SQL was presented as a representative open source relational DB. EDB’s response to these market changes and rising demand is to go after the market with “WarehousePG,” which was highlighted as the key point of the headline.
He pointed to a common customer requirement: pursuing innovation while securing economics, saying customers want innovation in a way they can afford rather than innovation for its own sake. He added that demand for open source adoption is very strong in an environment where pressure to cut costs is high.
As considerations when reviewing open source-based DB adoption, he cited technical support and incident response. Sidi said EDB’s support system does not stop at responding to customer requests within a set time, but is structured to take responsibility through problem resolution. He also said EDB promises to fix problems when they occur, with the goal of resolving them within a few hours.
He emphasized the importance of interoperability between DBs. The reason is that if compatibility with existing systems is insufficient, the scope of code changes during migration can expand and performance can decline. He explained that in migrations without compatibility, code-level changes are required, one line of code can grow into hundreds of lines, and performance degradation can also occur.
Sidi said that in the past, the practice of building separate systems for each use case had continued. He said that systems were kept separate for transactions, data warehousing, and vector search, and that using ETL connections between individual systems led to more data silos and greater operational complexity.
To reduce these problems, EDB proposed a strategy of using specialized engines for each workload and operating under an integrated data platform. The approach aims to address data fragmentation in the AI adoption process and the rising costs associated with AI adoption.
Within this strategy, WarehousePG was presented as the core product responsible for large-scale analytics workloads. WarehousePG is a product with an open MPP data warehouse character at the petabyte (PB) scale, targeting demand to replace existing data warehouses such as Snowflake and Greenplum. It also includes in-DB machine learning support and high-speed analytics capabilities.
The next version is called “WarehousePG 19,” and the underlying upgrade moves from Postgres 12 to 19. “WarehousePG 19” is the next version that improves ease of applying extension features, as well as flexibility and integration, by moving its foundation to Postgres 19. It also simplifies the application of data encryption technology, adds support for property graphs and Iceberg streaming, and is scheduled for official release in the first half of next year.
The speaker explained that using “WarehousePG 19” makes it possible to take advantage of stable data warehouse functionality and also use features added as Postgres evolves. He added that analytics can be performed on a single robust platform, and machine learning can be performed on the same platform as well.
Source: IT DAILY · Yang Seung-gap
Original: https://www.itdaily.kr/news/articleView.html?idxno=241406
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Source: IT DAILY
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