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.

Recently, I published an article on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

As technology advances and digitization takes over, there is an expectation that our lives will be more simple. ‘Self-service’ capabilities like Self-Service BI are the manifestation of this expectation within many technologies. For most, ease of use is no longer enough. Now tools must be simple to use, and flexible enough to cater to a wide range of skills and intricacy of analysis.

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.

The value of embedded analytics is unmistakable. Application teams that embed dashboards and reports drive revenue, reduce customer churn, and differentiate their software from the competition. While embedded dashboards create real value, they can also come with real costs. These costs are not always visible when companies plan for their analytics offering but can significantly impact production, scale, and the speed of bringing analytics to market.

BigQuery’s serverless architecture features storage and query optimizations that deliver transformational data analytics performance.

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.

The Stitch team has released many new capabilities the last few months, and we're excited to share all the new features and improvements with you. In this blog post, we'll take a closer look at what's new in this latest release and how it can benefit you. Here's a quick overview of what's new in Winter '23 for Stitch: Let's dive into each of these new capabilities in more detail.

ServiceNow, Inc. offers a well-known SaaS application, with companies in multiple industries using it to help manage digital workloads for a variety of departments and operations. What if it was as easy as just a few clicks to get ServiceNow data directly into your Snowflake account so you could combine it with other data sources, including ERPs, HRs, and CRMs? Well, now it is.

In this blog, the team at Yellowfin Japan demonstrates how to take advantage of Yellowfin's data visualization capability in the form of Z Charts. So, what is a Z Chart, and what does it look like?