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Starburst Data added data sharing and governance tools to boost the data mesh capabilities of its analytics platform.
The data and analytics vendor, founded in 2017 and based in Boston, enables customers to build a data mesh architecture, which is a decentralized approach to data management and analytics.
Data mesh removes responsibility for an organization's data and analytics from a centralized data team by enabling data teams within different domains, such as human resources, finance and sales, to manage and analyze their own data.
Its purpose is to reduce the bottlenecks that often result from a centralized approach to data while also taking advantage of the domain knowledge of data experts within an organizational domain. The theory behind the approach is that an expert in finance data will be better at working with finance data than a data generalist.
While parsing out the oversight and analysis of data to domain experts, data mesh also connects an organization's various domains with data catalogs and data integration capabilities to enable the sharing of data products and cross-domain analysis.
In addition to Starburst, vendors specializing in data mesh include Talend, Informatica and Denodo.
Starburst's new features are built on Starburst Stargate, a gateway for Starburst Enterprise customers to perform analysis on data distributed across the globe both without moving it and also while meeting data sovereignty regulations.
Meanwhile, the capabilities -- unveiled on Sept. 21 at the Big Data London conference -- are aimed at enhancing Starburst customers' ability to develop and share data products used for analytics, such as applications, models and dashboards that they build using global data sets.
In particular, two new governance tools have the potential to enable users to more easily share and analyze data gathered across borders, according to Kevin Petrie, analyst at Eckerson Group.
Data masking and cell-level filtering ensure that only specific users and user groups are able to view and work with certain data products and other data assets. And exception-based policies for certain data products and data assets now enable authorized users to circumvent certain policies while reducing the onus on administrators to manage data security.
"The fine-grained security controls -- the data masking and cell-level filtering -- should significantly improve the ability of Starburst customers to reduce the risk of cross-border activities," Petrie said. "And the exception-based policies for data products should make governance easier for data teams."
Vishal SinghHead of data products, Starburst Data
In February, Starburst added new data product capabilities that ultimately led to the development of this latest update, according to Vishal Singh, Starburst's head of data products. In particular, the vendor saw an opportunity to connect the new data product capabilities it released in February with Stargate to assist with global data sharing.
"We saw a massive opportunity, particularly in the [Europe, Middle East and Africa] region," Singh said. "Data often resides across borders and clouds, forcing organizations to adhere to a myriad of compliance regulations, which can severely limit insights due to partial data access. These enhancements are in response to that challenge."
In addition, customers' response to the February release influenced the capabilities added on Sept. 21, he continued.
"We collected a lot of customer feedback after our February release, particularly around user experience and streamlining the granting or denial of access to certain data products," Singh said.
Beyond the new governance tools, Starburst's new analytics capabilities include the following:
- data product and data set cloning to enable the sharing of data products across the organization and improve access to and usage of those data products;
- changes in the user experience 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 business vertical and create secure data products without risking exposure.
All are generally available, except data masking and cell-level filtering, which are currently in private preview. And combined, the tools address significant needs for organizations that collect and share data on a global scale, according to Petrie.
"Starburst's product enhancements, while incremental in scope, target a big requirement for enterprises to secure and govern cross-border data access," he said. "This requirement will only grow more stringent given regulatory pressures and geopolitical tensions. To succeed, data mesh implementations must address this requirement and reduce compliance risk."
Although Starburst unveiled its new analytics capabilities at the London conference, the vendor will host its own event, called Datanova, virtually on Sept. 29.
Data mesh will be the primary subject of the Starburst conference, which is designed to show data consumers how to move data mesh from theory within their organizations to practice.
Meanwhile, in terms of functionality, Starburst plans to continue improving its user experience, according to Singh. Customer feedback after the February release included requests for an improved user experience related to streamlining access to certain data products.
In addition, data quality and adding more metrics for data products and data sets will be areas of focus, Singh said.