When the big data movement started it was mostly focused on batch processing. Distributed data storage and querying tools like MapReduce, Hive, and Pig were all designed to process data in batches ...
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Originally developed at LinkedIn, Apache Kafka is one of the most mature platforms for event streaming. Kafka is used for high-performance data pipelines, streaming analytics, data integration, and ...
One of the big questions surrounding the rise of real-time stream processing applications is consistency. When you have a distributed application involving thousands of data sources and data consumers ...
Shiny new objects are easy to find in the big data space. So when the industry’s attention shifted towards processing streams of data in real time–as opposed to batch-style processing that was popular ...
Kafka wasn’t the first open source project I was involved in at LinkedIn. We’d also built a key-value store, a workflow system, and a number of other things. The biggest difference with Kafka was that ...
AI workflows fundamentally depend on real-time data movement: ingesting training data streams, feeding live data to models for inference and distributing predictions back to applications. But strip ...
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