Systems | Development | Analytics | API | Testing

A data extraction tool can help improve the accuracy of data by automating the extraction process and reducing the risk of human error. This can lead to more reliable and consistent data that can be used to make better business decisions. Moreover, data extraction tools can help you increase productivity and improve the quality of your data as they automate the process of retrieving data from multiple sources.

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.

In today’s data-driven world, businesses must deal with complex challenges related to managing, integrating, and properly using massive amounts of data housed in multiple locations. Organizations that unlock the right data architectural approach empower themselves with much better decision-making and strategic insights. Two popular approaches — data mesh and data fabric — have surfaced as prominent and innovative solutions for handling data at scale.

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.

Today we’re pleased to announce a new strategic partnership and resale agreement with Imperva. Imperva customers will now be able to directly license Kong Enterprise, the fastest, most feature-rich, and secure API management solution. In addition, Kong customers will be able to use the Imperva API Security plugin within Kong Enterprise. This allows developers to comprehensively secure their APIs and protect their business applications and data against unauthorized access.

Despite its many advantages, Node.js comes with a set of potential pitfalls if you don't maintain your application properly, such as: In this post, we'll explore these pitfalls and how to avoid them. Let's dive straight in!

There are many ways to load code and access file-related constants in Ruby. We can create a clear architecture by separating and handling concerns into classes and pulling in only the classes we depend on. Many full-stack frameworks like Rails and Hanami offer a built-in method to access the classes we want, as long as we stick with a certain convention. How does this work? In this post, we will explore three different options for loading code: using load, require, and autoload.

Gaetano DiNardi shares what many companies get wrong when they analyze website performance and traffic, and steps you can take to better know and serve your visitors.

Following the improvements to Bitrise’s Breakdown Chart, here is a quick walkthrough of how to leverage those Breakdown Chart improvements, including filtering options, exporting data, and more.