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Revolutionize Your Data Processing with Blossom Sky
- API-first platform that fully supports cross-platform data processing, allowing users to connect to distributed data pools and perform data operations directly at the source.
- Data unification layer, which interfaces between user applications, data processing and analytics platforms such as Java Streams, SQL, Flink, Hadoop, S3, Columnar Data Formats or Spark to enable data processing abstraction.
- Compatible with all major big data platforms, data lakes, data warehouses, data processing, and streaming frameworks. We work on extended integration to support Tensorflow, Pandas, and PyTorch.
- A unique AI-driven cross-platform optimizer executes your applications across multiple processing platforms without having you change the code. Blossom Sky lays the foundation for data regulation-compliant machine learning, AI, and federated learning.
- Consistent developer experience when writing code, regardless of coding language or whether a lightweight or highly scalable platform may be required. Blossom Sky transforms the code into a set of platform-agnostic physical operators, which will be executed by underlying processing platforms.
- Enables users to focus on their application logic while harnessing the power of multiple data processing platforms. We achieve up to 150x faster data processing, read the newest benchmark in our blog: Benchmarking Blossom Sky.
For Developers
Blossom Sky is based on open source. Join the community and discuss with the original creators of Apache Wayang, influence the roadmap and build features together with thousands of others.
Prefer to get started quickly? Download our pre-built Blossom Development Environment (BDE) and read the documentation.
Prefer to get started quickly? Download our pre-built Blossom Development Environment (BDE) and read the documentation.
> docker pull ghcr.io/databloom-ai/bde:main _
What is cross-platform data processing?
Cross-platform data processing technology is a powerful tool to kickstart and enhance your journey towards digital transformation. It allows you to access, analyze and process data across different data processing platforms without having to re-platform your code. This technology can be used to train machine learning (ML) models or generative AI on distributed data pools across multiple devices, edges, data lakes, data warehouses or data storage systems. This results in improved data velocity and management without sacrificing speed, privacy or security. By using cross-platform data processing technology, you can harness vast amounts of data without transmitting it to a central server, which underpins generative AI and federated learning (FL).
Blossom Sky’s technology is a proven stack across multiple industries, serving and enabling a wide range of use cases:
- Personalization: With Blossom Sky, retailers create personalized models for each user using data from their device, such as sensor readings, usage patterns, and browsing history.
- Privacy-sensitive applications: Blossom Sky is used to train models on sensitive data, such as medical records, without requiring the data to be shared or stored in a centralized location.
- Mobile and IoT applications: Blossom Sky enables telco providers to train on device data, such as sensor readings, to perform tasks such as image recognition, natural language processing, and anomaly detection.
- Healthcare: Blossom Sky trains models using patient data to produce individualized treatment regimens or detect illnesses in their early stages.
- Autonomous vehicles: Federated Learning is used to train models using sensor data from several vehicles in order to enhance self-driving car performance.
- Industry 4.0: Blossom Sky is used to train models on sensor data from many machines to enhance the operation and maintenance of industrial systems. Models trained with Blossom Sky detect inconsistencies in rotation at earth drilling projects, or ship engines.
- Edge computing: Models are trained on edge devices such as gateways and routers to accomplish tasks smart building operations, based on usage patterns and air quality readings.
