Big Data Glossary
What is Stream Processing?
Definition of Stream Processing
What is Stream Processing?
Stream processing is a method of processing data in real-time as it is generated, rather than in batches. In this way, stream processing allows for real-time analysis of data as it is generated, rather than having to wait for the data to be collected and stored before it can be analyzed. A stream processing system typically ingests data from one or more sources, such as sensors, social media feeds, or financial transaction systems, and processes the data in real-time as it is received. This processing can include filtering, aggregating, and analyzing the data to detect patterns, identify trends, and trigger actions or alerts.
Stream processing systems can be used for a wide range of applications, such as monitoring social media activity, detecting fraud in financial transactions, and controlling industrial processes. They are commonly used in IoT, Cybersecurity, and Financial industries.t
Introducing Crosser
The All-in-One Platform for Modern Integration
Crosser is a hybrid-first platform that in one Low-code platform has all the capabilities that you traditionally would need several systems for.
In one easy-to-use platform:
- Event Processing
- Data Ingestion & Integration
- Streaming ETL
- Batch ETL/ELT
- Reverse ETL - bidirectional
- Stream Analytics
- Functions & custom code (python, C#, JavaScript)
- Inference of AI/ML models
- Automation Workflows
Platform Overview
Crosser Solution for Data Mining
Explore the key features of the platform here →
Want to learn more about how Crosser could help you and your team to:
- Build and deploy data pipelines faster
- Save cloud cost
- Reduce use of critical resources
- Simplify your data stack
Sign up for a free account for testing your ideas. Connect to your data sources and test with real data.
Join our weekly live platform demo and have a chat with our experts. Choose between Industrial IoT focus or Cloud Data Integration sessions.