Systems | Development | Analytics | API | Testing

For argument’s sake, even if you think your QA strategy is picture-perfect, you’re likely deficient in one area. Don’t panic… you’re not alone. Most companies face major QA issues that hinder their ability to deliver top-notch software. Identifying and addressing QA gaps is pivotal, whether it’s a shortage of skilled testers, limited device and O/S coverage, or automation.

Salesforce is a popular customer relationship management (CRM) platform that extends advanced data analytics capabilities to its users. However, to experience many of Salesforce's greatest data benefits, you must enlist the help of third-party data management and pipeline integrations. In this guide, we'll walk you through the benefits of building pipelines for your Salesforce data and cover how Integrate.io can help you achieve your data integration goals.

CI/CD, a software development strategy, combines the methodologies of Continuous Integration and Continuous Delivery/Continuous Deployment to safely and reliably deliver new versions of code in iterative short cycles. This practice bridges the gap between developers and operations team by streamlining the building, testing, and deployment of the code by automating the series of steps involved in this otherwise complex process.

DreamFactory can effortlessly create secure internal REST APIs in a matter of minutes, and offers a comprehensive database testing solution. With DreamFactory, you can validate your databases with ease, ensuring they meet your application’s requirements and deliver impeccable performance. In this article we’ll cover how DreamFactory simplifies the database testing process. Here’s the key things to know about simplifying database tests with DreamFactory.

Product-led growth (PLG) is a business model that emerged in the last decade with the enormous success of vendors like Slack and Datadog. Unlike traditional sales-led models, PLG models cut out the middlemen (sales reps, for example) and let customers just download and use the product without third-party onboarding. The relative novelty of the pricing model and its demonstrably successful application in growing these companies attracted a lot of attention.

In data engineering and analytics, "Data Mart" and "Data Warehouse" are often used interchangeably. However, they serve distinct purposes and have unique characteristics. Understanding these differences is very important as businesses rely heavily on data-driven insights. This article explores the complexities of Data Marts, Data Warehouses, and the emerging concept of data lakes, showing their functionalities, benefits, and how they fit into the broader data ecosystem.

An overwhelming amount of data is generated daily (we're talking quintillions of bytes). For businesses, the amount of raw data coming in each day makes uncovering insights a challenge. Luckily, data mining gives your organization the ability to dig past what's raw to uncover patterns in your data sets. These patterns can result in business insights that help you make more informed decisions. Data mining tools simplify this process.

In part one of this series, we looked at some basic usages of Action Policy. Now we'll leverage Action Policy for more advanced authorization use cases. First up, let's explore applying pre-checks.

In the first part of this series, we built the asynchronous components of a much larger solution. Today, we will build the API interface that can be consumed in real-time.

With Kong Insomnia 8.3 (available today) we’ve brought back 100% local storage with no cloud synchronization for your projects, while at the same time keeping the same workflow for users who want to collaborate in the cloud. To start using Local Vault, get started for free with Insomnia.