Use these criteria to choose the best ETL tool for your data integration needs.

Wondering how Universal Analytics (GA3) and Google Analytics 4 compare? Take a closer look at these four main differences.

We recently sat down to discuss the language for the next Kong Gateway Plugin Development Kit (PDK). Given the number of JavaScript developers in the world and the variety of libraries and debugging tools available, there was only one logical choice. I’m excited to share that with the Kong Gateway (OSS) 2.4 release, that functionality is now available to you all!

In HeathTech apps, it’s often the case that you’re dealing with private or health-related data. This requires compliance with regulations, such as HIPAA in the United States. These regulations force you to handle sensitive data in a well-defined manner, so only specific people can read it, and if they do, it should be logged for later auditing.

Here at Ably, we deal with hard engineering problems all the time and pushing at the edges often results in us running up against all sorts of interesting gotchas. We recently made some AWS NLBs fall over, and had a hazardous encounter with Cassandra counter columns. In our day-to-day, we use gRPC for fast and efficient data exchange with mutual client/server state synchronization.

Russell Stephens from Compass shares how his team cut down the time spent on testing from 80 hours to 4 and shortened their release cycles to one week. Read on to find out what steps they took to achieve this!

Businesses hoping to make timely, data-driven decisions know that the value of their data may degrade over time and can be perishable. This has created a growing demand to analyze and build insights from data the moment it becomes available, in real-time.

Many agencies that handle mission-critical functions still rely on outdated and legacy software applications that have evolved into complex and costly solutions over time. These legacy systems sit at the heart of daily business operations—and have a negative impact on efficiencies, stakeholder satisfaction, and overall mission outcomes. While most stakeholders agree that modernization is needed, they also know it’s a complicated undertaking.

Businesses large and small depend on access to information in order to make smarter, data-driven decisions. And much of that data is personal, sensitive, or confidential. So how can you balance this demand for big data with the need to protect the individuals whom this data describes? When it comes to personally identifiable information (PII), there are multiple very good reasons why you should keep it securely under lock and key.

What do Xplenty, Snowflake software, and dbt (data build tool) have in common? When used together, they merge the best of ETL (extract, transform, load) and ELT (extract, load, transform) into a powerful, flexible and cost-effective ETLT (extract, transform, load, transform) strategy. In this guide, we’ll show you how to create an ETLT strategy with Xplenty, Snowflake software, and dbt. But first, we’ll explain why you'd want to use this strategy to build an ETLT data transformation stack.