Mercari transformed marketing and sales with Google BigQuery and Flywheel Software by applying Machine Learning to user growth and sales growth.

Developers often build high-performing, scalable applications using GraphQL and PostgreSQL to define data structure and achieve reliability, scalability, and high performance. First, however, selecting the appropriate framework to simplify and streamline the development process is crucial while building a GraphQL API with PostgreSQL. This blog will explore the top tools for building GraphQL APIs with PostgreSQL, including Hasura, Postgraphile, Prisma, and GraphQL Nexus.

Good data is the foundation of any successful business. Forecasting, planning, or tracking a company’s long-term performance is difficult without good data. Real-time information provided by data supports decision-making and helps save time and money. So, it becomes essential to test the data better to draw accurate insights.

Data engineers who work with huge amounts of data know that “big data” is not just an overhyped term. When the volumes of data get into petabytes the best data engineering tools start to break down. This is when you need devoted big data technologies that are fault-tolerant, scalable, and offer high performance even when amounts of data test the limits of your data platform. This article won’t be just another listicle. Instead, we’ll showcase the best big data tools by use case.

In the article “From Data to Insights: An Introduction to Product Analytics”, we walk you through the basics of product analytics, providing you with a high-level approach to get started with it. This time, we’d like to dig in deeper to dissect every step of the product analytics process, assuming that the main goal is to improve user engagement and retention.

Read this guide to learn how site reliability helps development teams accelerate reliable software delivery.

Do you understand JavaScript's concurrency model? This article is here to help! Learn how JavaScript executes non-blocking functions and dive into event loops, callbacks, async/await, and promises.

Imagine you’re planning a road trip across the country. You’ve got your car, a map, and plenty of snacks for the journey. But there’s one crucial thing missing – a functional engine that will get you from point A to point B without any issues. That’s exactly why API functional testing is so important. APIs are the engines that power our software applications. They allow different systems to communicate with each other and transfer data seamlessly.

Flaky tests are one of the most common and frustrating issues in software testing. These are tests that pass or fail sporadically, even if the code being tested has not changed. This can cause confusion and wasted time for developers who are trying to fix issues that may not even exist.

If you asked anyone at your organization, there’s no question they could name at least one business process that needs streamlining—probably they could name several. That’s the hard truth for leaders looking to gain digital transformation speed in pursuit of critical business goals, like faster customer onboarding and faster time to market.