Data Science and Databases

What do you do when you come across a dataset that’s simply too big to allow fast queries? Google BigQuery could be part of the solution, though it’s no silver bullet.

In this article, Toptal Java Developer Maksym Goroshkevych explains how BigQuery can drastically improve query performance, and when you should and should not use it.

The increasing accuracy of machine learning systems has resulted in a flood of applications using them. As machine learning models matured and improved, so did ways of attacking them.

In this article, Toptal Python Developer Pau Labarta Bajo examines the world of adversarial machine learning, explains how ML models can be attacked, and what you can do to safeguard them against attack.

Building a mobile app isn’t cheap—and the harsh reality is that most apps fail. Find out how to leverage fundamental analytics metrics to make sure your app actually turns a profit.

Static websites are simple and fast, but it’s also possible to automate updates to them to keep them fresh and relevant. Find out how to generate daily-updated HTML pages that track the latest releases of popular GitHub repositories.

You can just write sequentially executed code in JavaScript, but should you?

In this article, Toptal Freelance JavaScript Developer Omar Waleed tests the widespread Node.js belief that synchronous code degrades performance and is, in a sense, just plain evil. Is this really true?

Automatically scaling container deployments in a microservices-based app architecture is downright luxurious…once it’s set up. But what’s the best way to tune an app’s orchestration parameters?

Data warehouses aren’t exactly a new concept, but industry demand for data science services, coupled with the rise of AI and machine learning, is making them more relevant than ever.

In this post, Toptal Data Warehouse Developer Chamitha Wanaguru outlines three basic principles you need to keep in mind when developing a new data warehouse.