April 7th, 2020 • By Jon de Andrés Frías In the first part of our series of blog posts on how we remove technical debt using Apache Kafka at Rollbar, we covered some important topics such as: In the second part of the series, we’ll give an overview of how our Kafka consumer works, how we monitor it, and which deployment and release process we followed so we could replace an old system without any downtime.

Data saturation is everywhere. We want to collect more data because we want better information from them. However, the rapid rise in our ability to collect data hasn’t been matched by our ability to get meaningful insights from the data.

Retailers are using big data to report on consumer demand, inventory availability, and supply chain performance in real time. Big data provides a convenient, easy way for retail organizations to quickly ingest petabytes of data and apply machine learning techniques for efficiently moving consumer goods. A top supermarket retailer has recently used Unravel to monitor its vast trove of customer data to stock the right product for the right customer, at the right time.

Though manual testing will always have its place, test automation is a necessary part of an organization’s testing process as it can help the business achieve larger business goals like high-quality products with less cost and reduced investment on resources. Also, there are other benefits such as quick test results, shortened release cycles among many others.

One key aspect of the Cloudera Data Platform (CDP), which is just beginning to be understood, is how much of a recombinant-evolution it represents, from an architectural standpoint, vis-à-vis Hadoop in its first decade. I’ve been having a blast showing CDP to customers over the past few months and the response has been nothing short of phenomenal…