Cloudera SQL Stream builder gives non-technical users the power of a unified stream processing engine 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. A dead letter queue (DLQ) can be used if there are deserialization errors when events are consumed from a Kafka topic.

Data has become an essential driver for new monetization initiatives in the financial services industry. With the vast amount of data collected from customers, transactions, and market movements, among other sources, this abundance offers tremendous potential for financial institutions to extract valuable insights that can inform business decisions, improve customer service, and create new revenue streams.

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg.

The evolution of healthcare has come a long way since local physicians made house calls and homespun remedies were formulated using items from the kitchen spice rack. Today’s healthcare is driven as much by the promise of emerging technologies centered on data processing and advanced analytics as by developing new and specialized drugs.

The best description of untrusted data I’ve ever heard is, “We all attend the QBR – Sales, Marketing, Finance – and present quarterly results, except the Sales reports and numbers don’t match Marketing numbers and neither match Finance reports. We argue about where the numbers came from, then after 45 minutes of digging for common ground, we chuck our shovels and abandon the call in disgust.” How would you go about fixing that situation?

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.