Big-data company Databricks Inc. wants to help customers simplify data management for business intelligence and machine learning operations in order to eliminate silos.

To do so, the company today is announcing a new “Databricks Ingest” platform that enables customers to load data into a single repository where it can be made available for both BI and ML workloads.

Databricks also announced a new Data Ingest Network of data integration partners, including Fivetran Inc., QlikTech International AB, Infoworks.io Inc., StreamSets Inc. and Syncsort Inc., which are offering built-in integrations with Databricks Ingest to automate data-loading tasks.

Previously, companies have been forced to split their data into traditional structured data and unstructured big data, and then use it separately in both BI and ML workloads. The method works, but it also leads to siloed data, slow processing and often ends with incomplete results, Databricks said. As a result, companies aren’t maximizing the value of their data, and that’s what Databricks Ingest is aimed at fixing.

“This is one of the many drivers behind the shift to a ‘Lakehouse paradigm,’ which aspires to combine the reliability of data warehouses with the scale of data lakes to support every kind of use case,” said Ali Ghodsi (pictured), co-founder and chief executive officer of Databricks.

Ghodsi was referring to Databricks’ new concept of a “Delta Lake,” which is an open-source project it launched in April last year that’s aimed at improving the efficiency of enterprise data lakes.

“In order for this architecture to work well, it needs to be easy for every type of data to be pulled in,” Ghodsi said. “Databricks Ingest is an important step in making that possible.”

With Databricks Ingest, customers will be able to load data from a range of commonly used sources, including applications such as Salesforce, SAP and Google Analytics, databases such as Oracle, Cassandra and MySQL, and file storage services such as Amazon S3 and Azure Data Lake Storage.

The partner network is important as it means companies can ensure their data is continuously loaded into a Delta Lake, without needing to set up and maintain any job triggers of schedules. Once that’s set up, any new data is automatically pulled into the Delta Lake as soon as it’s created.

“Enterprises need more data faster, and they need to be able to get it to where ML runs,” said Constellation Research Inc. analyst Holger Mueller. “Databricks’ partner network here may be a good differentiator in that respect. A successful combination of data and ML is a key enabler for enterprise acceleration, which every company must achieve in order to survive and thrive.”

Photo: SiliconANGLE