Support all use cases; real-time, near real-time or batch.
Ingest data at scale, from any data source.
Use models trained and refreshed in batch workflows to make predictions on real-time data pipelines.
Compatible and integrated with leading big data technologies and platforms.
Hadoop distributions: MapR, Hortonworks, and Cloudera.
Cloud platforms: Amazon Web Services, Microsoft Azure
Key 3rd party partnerships: Attunity, DataStax, Couchbase, ElasticSearch, Kyvos Insights, Marketo, SalesForce, and more.
Ensure future readiness, avoid vendor lock-in.
Work with the power and flexibility of best-of-breed open source technologies integrated into a high-performance, scalable, and reliable enterprise-grade platform.
Operationalize machine learning at scale, on real-time data.
Use a comprehensive set of advanced analytics and machine learning operators, like Spark MLlib, ML, PMML, H2O, Tensorflow and more.
Model training and scoring, across real-time and batch workflows
A/B testing and model swap using champion challenger
Use pre-integrated drag-and-drop operators in a visual UI.
Introduce custom logic where needed, in the language of your choice (Java, Scala, and Python).
A single, consistent user experience for application development over multiple Big Data engines, including Storm, Spark, Tensorflow, and Flink.
Build and interconnect application sub-systems that each leverage the best-suited engine. For example: event processing using Storm or Flink and machine learning using Spark.
Keep full control and the flexibility to add new functionality and interfaces as the technology ecosystem evolves.
A one-stop-shop for your complete data processing journey: ingest, data quality, data blending, transformation, analytics, action triggers, storage and visualization.
Build on-premise or cloud applications that connect to infrastructure components and services no matter where they are.
Use pre-built templates
Easily access support and tutorials at every step.