Businesses have been managing data for decades. Tasks like dealing with data silos, keeping data secure and preparing data to be analyzed are nothing new. What is new, however, is the scale and complexity of data management. The total volume of data that businesses manage is exponentially increasing.

Armed with a Ph.D. from UC San Diego, our guest started off with internships at Google and Microsoft before gaining valuable experience as a VP and a highly sought-after consultant for startups and SMBs. Now he’s one of the world’s foremost experts on wrangling vast data sets and maximizing efficiency.

The only thing better than one awesome (and extremely useful!) tech tool is an integration with another similarly great tool. That’s why we’ve been working on building up our collection of new integrations because our main goal, always, is to make developers’ lives easier. And we do that by allowing you to immediately get all the live data you need from Rookout straight to your favorite tool. And the next step in doing so?

Logs are one of the three key “pillars” of observability, and cloud environments are no exception. You can’t know what’s happening in your cloud without analyzing cloud service logs, which allow you to audit and monitor workflows within your cloud. That said, cloud logging is a unique beast in certain respects.

ChaosSearch has been named to the 2022 DBTA (Database Trends and Applications) 100 list of “Companies that Matter Most in Data.” The DBTA 100 showcases forward-looking companies that are improving and expanding upon existing technologies and processes to help their customers use data more effectively. As data volumes grow and digital transformation initiatives take flight, many organizations are examining the right data architectures for them.

As a software engineer here at Grafana Labs, I’ve learned there are two questions that commonly come up when someone begins setting up a new Loki installation: “How many logs can I ingest into my cluster?” followed by, “How fast can I query these logs?” There are two ways to find out the answers.

Whether it’s for capability or speed, your legacy systems will need to be replaced at some point. But how you handle that transition will determine your overall success. If you’re not careful, you might end up dealing with two separate systems.

As big data continues to grow exponentially, enterprises are discovering that legacy data environments (e.g. data warehouse or data mart) were never designed to efficiently process and extract insights from the vast volumes of data they generate today. In turn, enterprises are shifting investments away from legacy data environments and searching for future-proof alternatives (e.g., data lakes, data lakehouse, data fabric, or data mesh) to support data-driven, new-generation initiatives.