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- ETL made easy: Ingest data from many sources like Kafka, S3, Twitter, or TCP sockets, process it using complex algorithms expressed with high-level functions like map, reduce, join and window, and finally, push the processed data out to file systems, databases, and live dashboards.
- Data Science made easy: Build predictive analytical models using a web interface with features like inline model-test and visual analysis of model data.
- DevOps made easy: Web-based configuration, management and monitoring with multi-tenancy controls.
Spark Streaming Use Cases
- Streaming ETL: Continually clean and aggregate data before pushing it into data stores.
- Data enrichment: Enrich live data by combining it with static data.
Business Intelligence & Analytics
- Complex Event Processing: Combine data from multiple sources to infer events or patterns.
- Real-time Alerts: detect and respond to unusual behaviors ('trigger events') quickly in real-time.
Predictive Analytics & Machine Learning
- Fraud Detection: Analyze transactions request to identify that the request is legitimate or not.
- Recommendation Systems: Produce a list of recommendations based on user's past behavior.
Industry's Only Multi-Engine Streaming Analytics PlatformView Data sheet