Datawatch Unveils Data Intelligence Features With the Summer Release of Swarm

Datawatch has announced that Datawatch Swarm, its enterprise data intelligence platform, is offering new deployment options to fuel self-service analytics. The new release aligns with enterprises' needs for collaborative infrastructure and also provides multi-tenancy and SaaS deployment options for Datawatch partners.

Datawatch Swarm is designed to help business analysts, while also meeting the data governance needs of the enterprise's IT department. It fills the void in the corporate data stacks for them by connecting the IT and data governance tenets to the self-service and predictive analytics, said Jon Pilkington, chief product officer, Datawatch. The combination of the collaborative nature of the solution and centralized data management platform allows analysts to quickly find, prepare, analyze and visualize the right data for business intelligence and analytical purposes, he added.

Supporting scalable Linux deployments, the new release enables cluster load balancing, fault tolerance and configuration management, allowing IT to easily implement Swarm across all departments. In addition, Kubernetes containerization automates the distribution, scaling and management of the Swarm platform for all business users and analysts to ensure proper data governance and sharing of data, models and outcomes in the centralized data management platform.  

The new release also adds integrations to key enterprise technologies, such as single sign-on, to provide a friction-free, user-friendly experience for employees to encourage data sharing and collaboration. By combining a centralized data marketplace with data preparation, cataloging, and governance features, Datawatch says, Swarm transforms how business analysts interact with and perceive data. The solution amplifies existing investments in business intelligence, data lakes, data warehouses, master data management platforms, and catalogs with advanced data preparation and socialization technology to solve analytical challenges.

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