Why StreamAnalytix for Apache Spark Streaming?
Writing stream processing operations from scratch is not easy. Developing an end-to-end streaming analytics application on Apache Spark Streaming for example requires writing a complex code in Scala or Python. StreamAnalytix makes it extremely easy for business users, data scientists and developers to build applications quickly using drag-and-drop operators with minimal coding.
Build App in Minutes with StreamAnalytix!
- 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.
- 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.
Business Intelligence & Analytics
- 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.
Predictive Analytics & Machine Learning
StreamAnalytix enables enterprises to analyze and respond to events in real-time at Big Data scale. It provides enterprises with the advantages of an Open Source based, enterprise-grade platform, for rapid and easy development of real-time streaming analytics applications for any industry vertical, any data format, and any use case.