Legacy data architectures primarily focus on past events, leaving autonomous applications vulnerable to acting on outdated context. The Streamhouse framework solves this by combining connectors, durable event backbones, stateful processing, and shared semantic models into an integrated setup. Instead of forcing data into a single physical location, it delivers fresh operational state across various formats such as streams, APIs, and analytical tables. Notably, technologies like Kafka Connect, Apache Kafka, or Apache Pulsar are utilized to support these continuous feeds and event replaying.
Key points
Relying on legacy data systems designed for past events can cause autonomous systems to e…: Relying on legacy data systems designed for past events can cause autonomous systems to execute incorrect actions.
Operational and analytical systems can reuse consistent business states across formats wi…: Operational and analytical systems can reuse consistent business states across formats without building isolated, independent data pipelines.
Data driving production artificial intelligence decisions must satisfy strict standards f…: Data driving production artificial intelligence decisions must satisfy strict standards for availability, security, correctness, and end-to-end traceability.
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Our own Jay Kreps argued that every company is becoming software. He was right. Businesses no longer simply use software to help people do their jobs. Software has become the way the business actually runs.
AI is now doing to software what software did to the business. At first, generative AI helped us do our jobs faster and better. It has since moved from drafting and summarizing to writing code and carrying out complex tasks. Now we are telling AI agents what we want done and letting it figure out how, including making decisions that once required a person in the loop. We still set the direction and limits, but the decisions themselves can happen at machine speed.
Making decisions faster does not mean having the data and context necessary to get them right. Much of our enterprise data architecture was built to describe the past, whether minutes, hours, or months ago. Now we are asking that same architecture to help run the present.
Most of us have found ourselves asking AI some version of “Did you check the latest…
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