Rapidly build and operationalize Stream Processing and
Machine Learning Applications

StreamAnalytix is an enterprise grade visual platform for all your streaming and batch data processing, and analytics needs. 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.

Build and operationalize custom big data applications five to ten times faster using a visual drag-and-drop interface, an exhaustive set of pre-built operators, full application lifecycle support, and one click options for on-premise and cloud deployments. With support for multiple Big Data engines and functional extensibility built-in to the design, StreamAnalytix gives you full flexibility and control to work with the technology stack of your choice.

 

Big Data Ingest: Streaming + Batch

Connect to any data source, batch or streaming

Connect with any data source and data storage system for both streaming and batch use cases V on-demand, using pre-built connectors, or create your own using our APIs.

Data Processing and Preparation

Compatible and integrated with leading big data technologies and platforms.

Enables in-memory data processing to transform data as it arrives; perform data filtering, data blending and data enrichment V at scale, to prepare for analytics and machine learning jobs.

Machine Learning and Data Science

Visual machine learning on real-time data

Easily build, train, calibrate, deploy, and do post-production monitoring of machine learning models and algorithms, on both real-time and batch data

Data Visualization

Data visualization and real-time dashboards.

In-built or custom real-time dashboards display the status of metrics and key performance indicators for data in motion.

Application Lifecycle

Develop and operationalize applications rapidly.

Rapidly build and operationalize big data applications using a powerful and intuitive visual interface. Supports the entire application delivery lifecycle from design, build and test, to deploy and manage.

Monitoring and Error Handling

Monitor application performance in real-time.

Run streaming and batch pipelines constantly and consistently once they are in production.

Extensibility

Work with the technology of your choice.

Keep full control and flexibility to add new functionality and interfaces as the technology ecosystem evolves.

Use the Extensions API (write your own functionality in languages including Java, Scala, SQL, and Python)

Enables custom data processing (us various native Apache Spark and Apache Storm based operators)

Build customized machine learning algorithms (using Python and R)

Rapidly port predictive analytics and machine learning models (port models built in SAS or R via PMML)

Multi-tenancy and Resource Management

Create independent workspaces based on a set of use cases, group of users, or various business units in the organization.

Logically separate the same underlying shared infrastructure V and optionally assign resources to independently tackle data, execution and analytics.

Security

Built with enterprise grade security features.

Security features include multiple user roles, role-based access control based on DB backed security model, and integration with LDAP.

Also supports Kerberos to connect to various underlying big data technologies to connect, process and store data

Deployment

Deploy both on-premise and on cloud infrastructure

Sits on the edge and can be up and running in minutes, without worrying about integrating with complex on premise, public cloud or hybrid infrastructure.