Cloudera SQL Stream Builder (SSB) gives the power of a unified stream processing engine to non-technical users so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. There are many ways to distribute the results of SSB’s continuous queries to embed actionable insights into business processes.

Over the past handful of years, systems architecture has evolved from monolithic approaches to applications and platforms that leverage containers, schedulers, lambda functions, and more across heterogeneous infrastructures. Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily.

As the use of ChatGPT becomes more prevalent, I frequently encounter customers and data users citing ChatGPT’s responses in their discussions. I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics. ChatGPT is an excellent resource for gaining high-level insights and building awareness of any technology. However, caution is necessary when delving deeper into a particular technology.

In this post, I will demonstrate how to use the Cloudera Data Platform (CDP) and its streaming solutions to set up reliable data exchange in modern applications between high-scale microservices, and ensure that the internal state will stay consistent even under the highest load.

We are thrilled to announce that the new DataFlow Designer is now generally available to all CDP Public Cloud customers. Data leaders will be able to simplify and accelerate the development and deployment of data pipelines, saving time and money by enabling true self service.

We just announced the general availability of Cloudera DataFlow Designer, bringing self-service data flow development to all CDP Public Cloud customers. In our previous DataFlow Designer blog post, we introduced you to the new user interface and highlighted its key capabilities. In this blog post we will put these capabilities in context and dive deeper into how the built-in, end-to-end data flow life cycle enables self-service data pipeline development.