Big Data Integration

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. The Crosser Node is lightweight, fast and modular.
• 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

Keep your data in sync with event-driven pipelines and become real-time enabled. All with a low-effort experience.

• Stream continuous data to your cloud
• Transform & deliver ready-to-use data 10x faster to your business teams
• Leverage advanced stream analytics
• Integrate streaming data, batch or CDC from any data source

Build ETL, ELT, Reverse ETL and ML powered pipelines with the Flow Studio and deploy, monitor and manage everything from a single Control Center.

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 such as payments, orders or updated records. 
• 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.