FinTech companies disrupt the marketplace and become successful by making it faster, more convenient, and more rewarding for users to save, borrow, or invest money with their applications and services. The financial technology space is fast-paced and competitive, but inherently high-scale - so when a FinTech company achieves strong product-market fit, there’s tremendous potential for it to quickly expand its user base and start experiencing exponential growth.

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.

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.

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.

As public cloud and multi-cloud adoption skyrockets, many organizations are looking to implement compatible services. These services increase the utility of cloud infrastructure by tapping into the underlying building blocks (otherwise known as primitives) of the cloud. That’s where the idea of a “supercloud” comes into play.