Create fully functional ingestion flows within minutes
A Spark-based visual self-service data flow and analytics platform
Build applications 10x faster | Pre-built operators | Full lifecycle support | Cloud and on-premise
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Build, operationalize, and manage machine learning models at scale
Move, cleanse, and transform your data in the cloud from any source within minutes
Enable your data-flow journey with self-service data processing, machine learning, and DataOps
StreamAnalytix Overview
Ingest, blend, and process high-velocity big data streams as they arrive, run machine learning models, visualize results on real-time dashboards, and train and refresh models in real-time or in batch mode
Why StreamAnalytix?
Multi and hybrid cloud support
Build your applications and pipelines and host them on any cloud provider of your choice or on a hybrid environment
Unified batch and stream processing
Ingest and blend data at scale from any batch or streaming data source. Unified batch and stream processing
End-to-end data processing and analytics
Supports data ingestion, data preparation and processing, analytics, data science and machine learning, action triggers, data visualization, and data storage
Multi-engine support
Leverage Apache Spark and other compute engines including Apache Storm, Apache Flink, TensorFlow, and Oozie for 10x faster application development vs. hand-coding
Self-service
Take advantage of a broad set of users to explore complex data at scale, and have greater control over the end analytic output
Click + code and reuse
Use an exhaustive set of pre-integrated drag-and-drop operators in an intuitive visual interface, or introduce custom logic in the language of your choice (Java, Scala, or Python)
Application data flow lifecycle management
Build applications with end-to-end support for the entire application delivery lifecycle – design,
Open source flexibility
Take advantage of the best-of-breed open source technologies integrated into a high-performance, build, test, debug, deploy, monitor, and manage scalable, and reliable enterprise-grade platform
Workflow orchestration
Integrate multiple pipelines to work in sync and automate the execution of multiple inter-related tasks
Extensibility
Keep full control and flexibility to add new functionality and interfaces as the technology ecosystem evolves
Resources
white paper
Streaming analytics: Market research and trends
Explore the growth of the streaming analytics space, its leading players, and business drivers in this market update, published by Bloor Research.
blog Apr 18, 2022
Drag-and-drop Data Pipelining: The Next Disruptor in ML
Recent advances in ML and AI technologies are helping enterprises across industries quickly move from their use cases from the pilot stage to production and operationalization.
