ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts. Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed.

This blog post was written by Pedro Pereira as a guest author for Cloudera. Right now, someone somewhere is writing the next fake news story or editing a deepfake video. An authoritarian regime is manipulating an artificial intelligence (AI) system to spy on technology users. No matter how good the intentions behind the development of a technology, someone is bound to corrupt and manipulate it. Big data and AI amplify the problem. “If you have good intentions, you can make it very good.

The accelerated exploitation of new technologies and data drive successful digital transformation and is key to gaining competitive business advantage. Hence, executives place a premium on innovation, flexibility and acceleration.

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.