By 2025, Gartner predicts data stories will be the most widespread way of consuming analytics. It’s far from a fad - it’s a part of modern analytics you can’t afford to overlook. However, are you still unsure of what data storytelling is, or why there's a sudden buzz?

Have you ever looked at clouds with a friend and seen patterns, shapes, and animals that they just can’t make out? The images appear so obviously to you but no matter how hard you try to show them, your friend sees an entirely different shape, or perhaps nothing at all. Building product features can be a similar exercise in frustration. As designers and builders, sometimes we see our own “cloud shapes” in the product.

Optimizing server performance is important in supporting end-user requirements. Using server optimization, actively monitor: Web server monitoring and optimization helps you to troubleshoot bottlenecks as they emerge and optimize server performance. In this post, we will discuss how to optimize performance and why it is important.

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically.

When the world went remote, studios needed to limit the number of people on set. To support the shift to remote work, teams needed to learn and implement new software tools. Game technology — like Unreal Engine and Unity — were powerful enough to meet the demands for this rapidly changing industry. But what else does a virtual production setup need?

In previous posts, we covered the basics of a C++ Microservices deployment including: With those basics in place, this blog will focus on optimization of the container in a C++ Microservices deployment. We'll examine how to structure the Dockerfile and the resulting Docker image to reduce the number of layers and disk space used.

Like any online service, your API users expect high availability and good performance. This also means one customer should not be able to starve another customer’s access to your API. Adding rate limiting is a defensive measure which can protect your API from being overwhelmed with requests and improve general availability. Similarly, adding quota management also ensures customers stay within their contract terms and obligations ensuring you’re able to monetize your API.

At this point, we can pretty confidently project that the cloud isn’t going anywhere. In the last few years, we’ve seen critical production workloads move to the cloud en masse—and that’s only going to continue. Now that the shift has occurred, other companies will need to get on board as well. The question is no longer if a company should adopt cloud but when and how. Ajeet Singh, ThoughtSpot’s Co-founder and Executive Chairman, is no stranger to embracing change.

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