Stream Analytics & Integration

A modern real-time integration platform that’s nothing like you’ve seen before. Build and deploy new pipelines in minutes with minimum effort.

• Low Code - Build integration and automation pipelines faster than ever with the Flow Studio. 
• Low Complexity. Engineers and Data teams are fully empowered to build advanced use cases without developers.
• Low TCO - Crosser Control Center gives you smooth mass deployment and monitoring of pipelines. 
• Low Footprint. 500 MB RAM is all that’s needed for the Crosser Node, the integration runtime.
• Low Latency/high performance. In-memory processing for millisecond processing latency and high throughput.

The modern enterprise is event-driven and real-time enabled. The foundation for this is the ability to connect to any data source and capture and act on changes. New data, updated data or deleted data. We have you covered for all of it.

• IoT / Machine Data. Process streams of IoT data and take actions based on smart conditions. 
• Report-by-Exception. Only trigger actions on streaming data if the data value changes.
• Webhooks gives you the ability to connect to SaaS services that push events. Could be payments or orders. 
• Pub/Sub like WebSockets and MQTT let you subscribe to topics important to you. 
• Change Data Capture (CDC) support in databases lets you keep your data in sync.
• File Watcher detects changes to files that can trigger events.
• Anomaly Detection. Apply fixed rules, custom algorithms or ML/AI models on streaming data to detect anomalies on IoT data, Video/Audio, user behaviour or transactions.

Unlike traditional ETL/ELT, iPaaS or Event Stream Analytics solutions, Crosser has a unique mix of ease-of-use and capabilities.

Add advanced real-time processing, stream analytics and logic to your integration and ETL pipelines and get ready-to-use data in minutes. Or improve your operational efficiency with smart automations.

• Connect to +800 data sources with the connector library
• Streams from IoT, machines, Video/Audio, transactions, logs and Apps.
• Batch data. Files. API’s 
• Change Data Capture (CDC)
• Transform data with standard modules or with SQL
• Process & run analytics with advanced logic with out-of-the box modules.
• Complement with your own code or ML/AI model.
• Trigger actions and smart automations
• Integrate to any destination in the Cloud, on-prem or in the edge.

All-in-One.

Use the simplicity of Flows-as-a-Service for your SaaS and Cloud services and leverage self-hosted nodes for on-premise data or self-host in your own Cloud. The only platform where you can use the same control plane to manage your Flows and deployments, everywhere.

With Crosser FlowWatch you are on-top of all your streaming, batch and API Data Flows. Wherever they run.

• Flow Monitoring & Alerts
• Data Freshness Monitoring
• Data Validation
• Streaming Data Validation
• Data Volume Validation
• AI/ML model Monitoring

Read More about the Crosser FlowWatch Features→

Crosser enables intelligent any-to-any data integration pipelines in a true hybrid manner and with low effort. It’s as easy to run pipelines in the edge as in the cloud thanks to the Crosser Control Center.

Any-to-Any integration and automation pipelines.

• Edge or Cloud Stream Analytics. Analyze and act on data streams. Anywhere.
• Edge-to-Cloud Intelligent ETL. Integrate ready-to-use data to your Cloud.
• Reversed ETL. Create triggers from the Cloud storage to your applications.
• Horizontal integration on-prem behind the firewall.
• Machine to machine closed-loop automation pipelines.

Explore how you can leverage Crosser to unlock Big Cloud savings for your Big Data. 

• Reduce Data Volume. Pre-process data in the edge, on-premise or in the cloud.
• Bypass complicated and expensive cloud services for integration, stream analytics and event processing.
• Integrate directly to Cloud storage, Snowlake, Databricks and more.
• Simplify the data stack - reduce your software spending with fewer layers. 
• Low Infrastructure cost. 90% savings compared to Kafka.
• Offload cloud by leveraging hybrid edge, on-premise and multi-cloud for the ultimate balance of data processing cost and performance. 
• Run distributed ML/AI in the edge, close to the data source when needed.