Since the initial launch of Kong Konnect Cloud, one common feature request has (unsurprisingly) been Multi-Region support. Many customers look for SaaS solutions that support a distributed service architecture. Even at its inception, our goal was to support more than a single region. Today, we’re happy to announce Multi-Region Support for Kong Konnect Cloud.

It is estimated there are over 15 billion mobile devices (phones and tablets) in the world today. And customers, businesses, and service providers are using or developing native mobile apps for everyday needs.

Many API developers and companies struggle to find ways to easily set up systems to monetize their APIs. Some are simple but not customizable, some are complex and require massive engineering effort to actually get it all running. To make things easier, Moesif created a feature a few months ago called Billing Meters which gives massive customizability but with a minimal amount of code and engineering effort.

DBTA recently hosted a roundtable webinar with four industry experts on “Unlocking the Value of Cloud Data and Analytics.” Moderated by Stephen Faig, Research Director, Unisphere Research and DBTA, the webinar featured presentations from Progress, Ahana, Reltio, and Unravel. You can see the full 1-hour webinar “Unlocking the Value of Cloud Data and Analytics” below. Here’s a quick recap of what each presentation covered.

We asked 34 businesses to shed some light on the best financial reporting practices. Here’s what they said.

Google Earth Engine (GEE) is a groundbreaking product that has been available for research and government use for more than a decade. Google Cloud recently launched GEE to General Availability for commercial use. This blog post describes a method to utilize GEE from within BigQuery’s SQL allowing SQL speakers to get access to and value from the vast troves of data available within Earth Engine.

The XML data format has been around since 1996. It was first envisioned as a lingua franca (bridging language) for data to be serialized and read into completely disparate systems (with different programming languages, operating systems, and even hardware). It has been wildly successful in that goal. In software, though, 26 years is like a lifetime — and in hardware, it's an eternity.

The data pipeline is at the heart of your company’s operations. It allows you to take control of your raw data and use it to generate revenue-driving insights. However, managing all the different types of data pipeline operations (data extractions, transformations, loading into databases, orchestration, monitoring, and more) can be a little daunting. Here, we present the 7 best data pipeline tools of 2022, with pros, cons, and who they are most suitable for. 1. Keboola 2. Stitch 3. Segment 4.

Is repetitive and menial work impeding your data scientists, analysts, and engineers from delivering their best work? Consider automating your data analytics to free their hands from routine tasks so they can dedicate their time to doing more meaningful, creative work that requires human attention. In this blog we are going to talk about: Now let’s dive in.