DataBloom AI, a leading provider of virtual data lakehouse solutions announced the beta release of Blossom Sky for Google Cloud. The beta release is part of Databloom AI’s cooperation with Google for Startups Cloud Program.
Blossom Sky is the first virtual data lakehouse (VDL) on the market. It enables organizations to store, process, and analyze data within multiple data lakes and data warehouses via a single, unified platform. Federated data processing enables customers and users to manage and process their data directly at the source through data pipelines, including low-level data operations like map, join, filter, or map reduce and high-level machine learning operations such as KMeans, SGD or PageRank.
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This provides organizations with a number of benefits, including:
- Increased Data Scalability and Flexibility: Blossom Sky enables organizations to leverage their existing data infrastructure without centralizing data. This provides organizations with the ability to scale up or down as required.
- Reduced Data Duplication and Complexity: Blossom Sky removes the need for expensive Extract, Transform, Load (ETL) and data transfer costs, reducing data duplication and making data management easier for organizations.
- Better Data Security and Governance: Blossom Sky brings federated data processing to every organization as a Software as a Service (SaaS) solution.
- Time to insights (T2I): Blossom Sky brings together data silos in a single data management platform, making it easier for organizations to discover, analyze, and act on data, resulting in faster T2I.
“We are excited to partner with Google Cloud to bring Blossom Sky to market,” said Alexander Alten, CEO of Databloom AI. “Google Cloud’s infrastructure and services provide the perfect foundation for our platform, and we believe that this partnership will help us to accelerate our growth and reach more organizations around the world.”
Databloom AI, headquartered in Miami, FL, is one of the world’s leading virtual data lakehouse providers. Databloom’s cloud-based data lakehouse platform allows organizations to consolidate, manage, and analyze data across multiple data lakes or data warehouses in one, single platform.
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What is federated data analytics?
Federated data analytics is a method of processing data that is stored in multiple locations without having to move the data. This is done by creating a virtual data lakehouse that connects to the different data sources and executes data related tasks directly at the source. The metadata is then processed in the virtual data lakehouse, and the results are returned to the user.
Federated data processing is a powerful tool that can help organizations save time and money. It can also help improve data security and compliance.
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