The ability to process high volumes of data (big data) in real-time has become crucial for many organizations, and this is where data streaming technologies come into the picture. These technologies allow large amounts of data to be processed in real-time or near real-time as it is generated, enabling businesses to gain immediate insights and make time-sensitive data-driven decisions. By Darryn Campbell.
At the heart of these technologies is the concept of data streams, also known as event streams. Data streams are sequences produced by various sources, such as social media feeds, Internet of Things (IoT) devices, log files, scientific data sets, and more. These streams of data are then ingested and processed by data streaming technologies. The blog post then explains:
- Basic concepts of data streaming technologies
- Data streaming architecture
- Data consistency
- Tools for data streaming technologies
Data consistency is a significant concern in data streaming. Data streaming technologies use various techniques such as event ordering, exactly-once processing, and fault tolerance to ensure consistency. These techniques ensure that the data is processed in the correct order, no data is lost or processed multiple times, and the system can recover from failures without data loss. Nice one!
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