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Contest for control over the semantic layer for analytics begins in earnest Contest for control over the semantic layer for analytics begins in earnest
A battle for control over how data is processed by analytics applications is starting to emerge in the cloud. Providers of data warehouses such... Contest for control over the semantic layer for analytics begins in earnest


A battle for control over how data is processed by analytics applications is starting to emerge in the cloud. Providers of data warehouses such as Snowflake, Amazon Web Services (AWS), and Microsoft are aggregating massive amounts of data. Naturally, providers of analytics and business intelligence (BI) applications are treating data warehouses as another source from which to pull data.

Snowflake, however, is making a case for processing analytics on its data warehouse. For example, in addition to processing data locally within its in-memory server, Alteryx is now allowing end users to process data directly on the Snowflake cloud.

At the same time, however, startups that enable end users to process data using a semantic layer that spans multiple clouds are emerging. A case in point is Kyligence, a provider of an analytics platform for Big Data based on open source Apache Kylin software.

Given the total cost of data warehouse platforms in the cloud, providers of these services are anxious to surface value that goes beyond merely being a repository for data, said Mike Leone, an industry analyst with Enterprise Strategy Group (ESG). “They want the data warehouse to be the entry point for other services,” said Leone. “Otherwise, the data warehouse is too expensive.”

That effort is drawing support from vendors, such as Alteryx, as the amount of data in a Snowflake repository increases.

However, Alteryx remains committed to a hybrid cloud strategy, said Sharmila Mulligan, the company’s chief marketing officer. Most organizations will have data that resides both in multiple clouds and on-premises for years to come. The idea that all of an organization’s data will reside in a single data warehouse in the cloud is fanciful, she said. “Data is always going to exist in multiple platforms,” said Mulligan. “Most organizations are going to wind up with multiple data warehouses.”

Similarly, Kyligence is trying to provide an analytics platform that spans multiple platforms. It pulls data from multiple platforms to create an online analytical processing (OLAP) database that provides end users with a familiar construct for analyzing data, said Li Kang, head of North America for Kyligence.

The company thus far has raised $48 million in pursuit of that goal. Redpoint Ventures, Cisco, China Broadband Capital, Shunwei Capital, Eight Roads Ventures, and Coatue Management are all investors. Kyligence counts UBS, Costa, Appzen, McDonald’s, YUM, L’OREAL, Porsche, Xactly, China Merchants Bank, and China Construction Bank among its customers.

Yesterday, Kyligence announced it’s making an enterprise edition of Apache Kylin ,dubbed Kyligence Cloud 4, available on the AWS and Microsoft Azure clouds where it can pull data from not just Snowflake, but also object-based storage repositories. Previously, the enterprise edition of the platform was available only for on-premises platforms.

That capability is critical, because over time the primary platform organizations will opt to store data on will change, noted Kang. “The center of data gravity tends to shift,” said Kang.

Kyligence Cloud, in effect, is an example of the additional semantic layer for processing and analyzing that is starting to emerge in hybrid cloud computing environments, said Kevin Petrie, vice president of research for the Eckerson Group. “By analyzing SQL queries and automatically building OLAP indices to pre-compute results, they can help enterprises offload a lot of the queries that would otherwise hit the data warehouse,” he said.

Regardless of the path forward, it’s not clear that data warehouses will emerge as data processing powerhouses. In many cases, they are just the latest incarnation of a data lake. The challenge IT organizations face now is making sure this latest iteration of a data lake doesn’t turn into yet another swamp.

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