Databricks on Tuesday announced an industry-specific data lakehouse for the manufacturing sector, in an effort to surpass its data lake and data warehouse rivals.
A data lakehouse is a data architecture that offers storage and analytics capabilities, in contrast to data lakes, which store data in native format, and data warehouses, which store structured data (often in SQL format).
Dubbed Databricks Lakehouse for manufacturing, the new service offers capabilities for predictive maintenance, digital twins, supply chain optimisation, demand forecasting, real-time IoT analytics, computer vision, and AI, along with data governance and data sharing tools.
“The Lakehouse for manufacturing includes access to packaged use case accelerators that are designed to jumpstart the analytics process and offer a blueprint to help organisations tackle critical, high-value industry challenges,” the company said in a statement.
Databricks is providing partner-supported services and tools such as database migration, data management, data intelligence, revenue growth management, financial services, and cloud data migration under the aegis of what the company calls Brickbuilder Solutions under the new lakehouse for manufacturing.
These partners include Accenture, Avanade, LT Mindtree, Wipro, Infosys, Capgemini, Deloitte, Tredence, Lovelytics, and Cognizant.
Databricks’ Lakehouse for Manufacturing has been adopted by enterprises such as DuPont, Honeywell, Rolls-Royce, Shell, and Tata Steel, the company said.
Industry-specific lakehouse to aid data managers
Databricks’ new Lakehouse for Manufacturing is expected to have a positive impact on data managers or data engineers, according to IDC Research Vice President Carl Olofon.
The lakehouse offering will make it easy for data managers to coordinate data across data lake and data warehouse environments, ensuring data consistency, timeliness, and trustworthiness, Olofson said.
Other analysts feel the offering will also help data science teams across enterprises.
“It helps data science teams skip a step by having preconfigured analytics rather than a blank slate to start from,” said Tony Baer, principal analyst at dbInsights.
Databricks is in a better position to deliver advanced data science capabilities when compared to other offerings from rivals, according to Doug Henschen, principal analyst at Constellation Research.
“That’s certainly evident in this Databricks Lakehouse for Manufacturing, which includes support for digital twins, predictive maintenance, part-level forecasting and computer vision,” Henschen said.
Lakehouse for Manufacturing aimed at accelerating adoption
The Lakehouse for Manufacturing offering from Databricks is aimed at accelerating the adoption of the company’s lakehouse offerings and increasing the stickiness of other services, according to Olofson.
“Lakehouse is still a new and somewhat amorphous concept. Databricks is trying to accelerate adoption by offering industry-specific lakehouses. These are really what you might call ‘starter kits’ since the guts of any lakehouse are specific to what data the company has and how it is to be put together,” Olofson said.
Providing such kits, or what IBM used to call, “patterns”, according to Olofson, is meant to jumpstart the customer enterprise by offering a partially complete set of functionality that the customer can finish with company-specific definitions and rules.
“This is a well-worn approach in software when seeking to sell products that are complex or multifunctional, since customers often don’t know how to get started. If Databricks can win over customers with these lakehouse offerings, they will get a measure of stickiness that should ensure that the customer will remain loyal for a while,” Olofson added.
The launch of industry-specific warehouses is a mix of the company’s internal priorities that includes factors such as considering the market that has the biggest potential for Databricks’ offerings and industry-specific demand, Constellation Research’s Henschen said.
“I suspect that the company launched a lakehouse for the manufacturing sector as the next one in line after having already introduced similar offerings for retail, financial services, healthcare and life sciences, and media and entertainment last year,” Henschen said.
The launch of the industry-specific lakehouse is aimed at lowering the barrier to lakehouse adoption by adding capabilities such as pre-built analytic patterns that would help enterprises jumpstart their journeys, Baer said.
Databricks versus Snowflake
Databricks, which competes with Snowflake, Starburst, Dremio, Google Cloud, AWS, Oracle, and HPE, has timed its industry-specific lakehouse announcements in a competitive manner with Snowflake, experts said.
“The announcements are very similar to that of Snowflake and there is an element of competitive gamesmanship in the timing of announcements as well,” Henschen said, adding that Snowflake might have a head start as it kicked off its industry cloud announcements in 2021 with media and financial services cloud offerings.
However, there seems to be a difference in the approach between Snowflake and Databricks in terms of the “speak” of their product offerings.
“Snowflake does not use the term ‘lakehouse’ in their materials although they say that data lake workloads are supported by them. Their core technology is a cloud-based data warehouse relational database management system (RDBMS), with extensions that support semi-structured and unstructured data as well as data in common storage formats such as Apache Iceberg,” Olofson said, adding that Snowflake too offers industry-specific configurations.
Analysts said it was “too early” to gauge any changes in marketshare arising from these industry-specific offerings.
“I’d say it’s still early days for combined lakehouses to be displacing incumbents. Databricks customers may be running more SQL analytic workloads on Databricks than in the past, but I don’t see it displacing incumbents in support of high-scale, mission-critical workloads,” Henschen said.
“Similarly, it’s early days for Snowflake Snowpark, and I don’t see customers choosing Snowflake as a platform for hardcore data science needs. Best-of-breed is still winning for each respective need,” Henschen added.