Databricks launches Lakehouse for the production segment

Databricks on Tuesday announced an industry-specific data lake house for the manufacturing sector in an effort to outdo its data lake and data warehouse rivals.

A data lake house is a data architecture that offers storage and analysis capabilities in contrast to data lakes, which store data in native format, and data warehouses, which store structured data (often in SQL format).

Called Databricks Lakehouse for Manufacturing, the new service offers predictive maintenance, digital twins, supply chain optimization, demand forecasting, real-time IoT analytics, computer vision and AI, as well as data management and data sharing tools.

“The Lakehouse for Manufacturing includes access to packaged use case accelerators designed to jump-start the analytics process and offer a program that helps organizations tackle critical, high-value industry challenges,” the company said in a statement.

To help new production lakehouse users, Databricks provides partner-supported services and tools such as database migration, data management, data intelligence, revenue growth management, financial services, and cloud data migration under the company’s umbrella of what it calls Brickbuilder. under Solutions:

These partners include Accenture, Avanade, L&T 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.

An industry-specific repository to help 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 Olofsson.

Lakehouse’s offering will make it easier for data managers to reconcile data across data lake and data warehouse environments, ensuring data consistency, timeliness and reliability, Olofsson said.

Other analysts believe the offering will also help data science teams across enterprises.

“It helps data science teams skip a step by having a preconfigured analysis, rather than a blank slate to start with,” said Tony Baer, ​​principal analyst at dbInsights.

According to Doug Henschen, principal analyst at Constellation Research, Databricks is in a better position to provide advanced data science capabilities compared to other competing offerings.

“That’s certainly evident in the Databricks Lakehouse for Manufacturing, which includes support for digital twins, predictive maintenance, part-level forecasting and computer vision,” Henschen said.

Lakehouse for Manufacturing aims to accelerate adoption

The Lakehouse for Manufacturing offering from Databricks is aimed at accelerating adoption of the company’s lakehouse offerings and increasing the “stickiness” of other services, according to Olofsson.

“Lakehouse is still a new and somewhat amorphous concept. Databricks is trying to accelerate adoption by offering industry-specific lake houses. “These are really what you might call ‘starter kits,’ because the guts of any lake house are specific in terms of what data the company has and how it needs to be collected,” Olofsson said.

Providing such packages, or as IBM called “patterns,” according to Olofsson, is meant to jumpstart the use of lake houses, offering businesses a partially complete set of functionality that users can complete with company-specific definitions and rules.

“This is a well-worn approach in software when you’re looking to sell complex or multi-functional products, because customers often don’t know how to get started. If Databricks can attract customers with these lakefront offerings, they’ll get some stickiness that should ensure the customer stays loyal for some time,” Olofsson added.

Launching industry-specific repositories is driven by a mix of internal company priorities that include factors such as considering which industries have the greatest potential for Databricks’ offerings and industry-specific demand, Constellation Research’s Henschen said.

“I suspect the company launched a lake house for the manufacturing sector as the next one, having already introduced similar offerings for retail, financial services, health and life sciences, media and entertainment last year,” Henschen said.

The launch of the industry-specific Lake House aims to lower the barrier to Lake House adoption by adding features such as pre-built analytics patterns to help businesses get started on their journeys quickly, Baer said.

Databricks vs. Snowflake

Databricks, which competes with Snowflake, Starburst, Dremio, Google Cloud, AWS, Oracle and HPE, has timed its industry lake house announcements to rival Snowflake with, experts say.

“The announcements are very similar to the Snowflake announcement, and there is also an element of competitive gaming in the timing of the announcements,” Henschen said, adding that Snowflake may have a head start as it begins its industry cloud announcements in 2021 with media. . and financial services cloud offerings.

However, there seems to be a difference in Snowflake’s and Databricks’ approach in terms of how they talk about their product offerings.

Snowflake doesn’t use the term “lakehouse” in their stuff, even though they say the data lake workload is powered 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 common data storage formats such as Apache Iceberg,” said Olofsson, adding that Snowflake also offers industry. special configurations.

Analysts say it’s too early to gauge any changes in market share stemming from industry-specific offerings.

“I’d say it’s still early days for the combined lake houses to displace the incumbents. Databricks customers can run more SQL analytic workloads on Databricks than before, but I don’t see that replacing incumbents to support highly scalable, mission-critical workloads,” Henschen said.

“Also, it’s early days for Snowflake Snowpark, and I don’t see customers choosing Snowflake as a platform for their hard data science needs. Best of breed still wins for every relevant need,” Henschen added.

Copyright © 2023 IDG Communications, Inc.

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