Manage your Data in one place. Successfully

Radicalbit Platform can easily manage all the stages of Data life cycle:
ingestion, preparation, processing, publishing and visualization including MLOps

Radicalbit DataOps Platform enables stream data integration and analytics over event stream processing platforms, giving users the ability to design and manage connectors and data processing pipelines in a visual way.


Faster POC Development

Manage & Monitor streaming data flows

Automate Operations & Deployment

Data Governance Over Streaming

Machine Learning Out-of-the-Box Integration

Operationalize Artificial Intelligence

Connect Data In Motion

Platform Logical Architecture



Radicalbit products

From Ingestion to Visualization

Radicalbit Natural Analytics provides the most advanced self-service capabilities for Data Integration, Data Governance, Data Preparation and Data Visualization over streaming based architectures. It offers a complete set of features aimed at managing every step of the Data Lifecycle.

  • Easy to use, code-less, GUI for defining source and sink connectors and data processing pipelines
  • Easy to manage visual data preparation tool
  • Built-in Business Intelligence module with dedicated management GUI
  • Embedded data and events catalog designed to improve data governance over streaming
  • True Real time processing of Data/Events (milliseconds latency)
  • Not only real time / suited also for historical analytics on heterogeneous data sources
  • Out-of-the-box Machine Learning integration
  • Rules engine Integration and rules serving layer
  • Runtime machine learning models versioning and serving
  • Direct data ingestion from sources / no need to ingest replicated data in order to start processing
  • Source and Sink connectors management and monitoring
  • Expressly designed for Stream Data Integration

Incrementally serving

NSDb is a storage solution conceived having streaming real-time analytics in mind. It fits perfectly the read side of Kappa Architectures (or for systems based on Command Query Responsibility Segregation pattern). The idea is to store metrics and to bind directly the incoming indexed data to the final users, thanks to pushing technologies like WebSocket.

  • View / Read oriented time series database for real-time streaming systems implementing the Kappa Architecture or the CQRS pattern
  • Real-time streaming of the incoming data using websocket
  • Native clustering feature
  • SQL-like support
  • JDBC connector
  • Streaming processor connectors (sink)
  • Kafka connector to ingest events / messages in Avro format
  • API available for a wide range of programming languages (Scala, Java, C++, Python, Go, Ruby, C#, Node.js, Android Java, Objective-C, PHP)
  • CLI

Market Recognition

Radicalbit has been recently featured by Research Companies – particularly by Gartner – as one of the most noticeable players of the global Event Stream Processing market.


“Adopt Stream Data Integration to Meet Your Real-Time Data Integration and Analytics Requirements”

Etisham Zaidi, J.W. Roy Schulte


Use Continuous Intelligence to Transform the Business

J.W. Roy Schulte


Market Guide for Event Stream Processing

Nick Heudecker, J.W. Roy Schulte

Zion Research

Event Stream Processing market report: Global Industry Perspective, Comprehensive Analysis, and Forecast, 2018–2025

Market Recognition

Radicalbit has been recently featured by Research Companies – particularly by Gartner – as one of the most noticeable player of the global Event Stream Processing market.


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