Starburst Data has added data sharing and governance tools to bolster the data-mesh capabilities of its analytics platform.
The Boston-based data and analytics provider, founded in 2017, enables customers to build a data mesh architecture, which is a decentralized approach to data management and analytics.
Data mesh removes accountability for data and analytics from an organization of a centralized data team by allowing data teams from different areas, such as human resources, finance, and sales, to manage and analyze their own data.
Its goal is to reduce the bottlenecks that often result from a centralized approach to data while leveraging the domain knowledge of data experts within an organizational domain. The theory behind the approach is that a financial data expert will work better with financial data than a data generalist.
While transferring data monitoring and analysis to domain experts, data mesh also connects different areas of an organization with data catalogs and data integration capabilities to enable sharing of data products and cross-domain analysis.
Besides Starburst, vendors specializing in data meshing include Talend, Informatica, and Denodo.
Starburst’s new features are based on Starburst Stargate, a gateway that allows Starburst Enterprise customers to perform analytics on globally distributed data without moving it and while adhering to data sovereignty regulations.
Meanwhile, the capabilities – unveiled on September 21 at the Big Data Conference in London – aim to improve the ability of Starburst customers to develop and share data products used for analysis, such as applications, models and dashboards they create using global data. sets.
In particular, two new governance tools have the potential to make it easier for users to share and analyze collected data across borders, according to Eckerson Group analyst Kevin Petrie.
Data masking and cell-level filtering ensure that only specific users and groups of users can view and use certain data products and other data assets. And exception-based policies for certain data products and data assets now allow authorized users to bypass certain policies while reducing the burden on administrators to manage data security.
“The granular security controls – data masking and cell-level filtering – should significantly improve Starburst’s customers’ ability to reduce the risk of cross-border activity,” Petrie said. “And exception-based policies for data products should make governance easier for data teams.”
Vishal SinghData Product Manager, Starburst Data
In February, Starburst added new data product features which ultimately led to the development of this latest update, according to Vishal Singh, Data Product Manager at Starburst. In particular, the vendor saw an opportunity to connect the new data product features it released in February with Stargate to help with global data sharing.
“We saw a huge opportunity, especially in the [Europe, Middle East and Africa] region,” Singh said. “Data often resides across borders and clouds, requiring organizations to adhere to a myriad of compliance regulations, which can significantly limit insights due to partial data access. These improvements meet that challenge.”
Additionally, customer response to the February release influenced the features added on September 21, he continued.
“We collected a lot of customer feedback after our February release, particularly around the user experience and the streamlining of granting or denying access to certain data products,” Singh said.
Beyond the new governance tools, Starburst’s new analytics capabilities include the following:
- cloning of data products and datasets to enable sharing of data products within the organization and to improve access to and use of those data products;
- user experience changes designed to make it easier to manage data security policies at scale; and
- consistent data governance capabilities that cover data from its raw form through the development and deployment of data products to ensure regulatory compliance in any industry and build secure data products without risking be exposed.
All are generally available, except for data masking and cell-level filtering, which are currently in private preview. And combined, the tools meet the important needs of organizations that collect and share data globally, according to Petrie.
“Starburst’s product enhancements, while incremental in scope, target a broad requirement for enterprises to secure and govern cross-border access to data,” he said. “This requirement will only become more stringent given regulatory pressures and geopolitical tensions. To be successful, data mesh implementations must meet this requirement and reduce compliance risks.”
Although Starburst unveiled its new analytics capabilities at the London conference, the vendor will host its own event, called Datanova, virtually on September 29.
Data mesh will be the main topic of the Starburst conference, which is designed to show data consumers how to take data mesh from theory within their organizations to practice.
Meanwhile, in terms of features, Starburst plans to continue improving its user experience, according to Singh. Customer feedback after the February release included requests for improved user experience related to streamlining access to certain data products.
Also, data quality and adding more metrics to data products and datasets will be areas of focus, Singh said.