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.

Recently, we announced enhanced multi-function analytics support in Cloudera Data Platform (CDP) with Apache Iceberg. Iceberg is a high-performance open table format for huge analytic data sets. It allows multiple data processing engines, such as Flink, NiFi, Spark, Hive, and Impala to access and analyze data in simple, familiar SQL tables.

To get the most out of any application, a graphical user interface improves your efficiency and data streaming without exception. A UI should help you through the steps of an often-complex flow as the visible layer between your problem and solution. Even the most hardcore back end enthusiasts will admit that its significance is undeniable for a complete product. It has to be well organized and easy to understand, yet be able to provide the right tools in the right place.

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