During the Kong Summit in September Dennis Kelly, Senior DevOps engineer, explained how Kong became a core service—and an integral part of the architecture—across brands at Zillow Group. Starting out with a single use case for Kong Community Edition, Zillow advanced to proxying production workloads at scale with Enterprise Edition, automating deployments with Terraform. Kong’s power and flexibility fueled its explosive adoption at Zillow.
Kong’s stateless architecture and lightweight footprint allow it to be deployed in a variety of environments, with few adjustments required for deployment strategies. At Kong Summit, the Kong Cloud team described their experience with deploying a provider-agnostic, globally-available, high performance Kong installation.
As a Customer Success Architect with Talend, I spend a significant amount of my time helping customers with optimizing their data integration tasks – both on the Talend Data Integration Platform and the Big Data Platform. While most of the time the developers have a robust toolkit of solutions to address different performance tuning scenarios, a common pattern I notice is that there is no well-defined strategy for addressing root causes for performance issues.
The best article I’ve read recently, Is the business intelligence dashboard dead? by Marius Moscovici. This article challenges the status quo around what a dashboard is, why it exists, and what business users want from their analytics.
Today, we’re thrilled to announce the general availability of Kong 1.0 – a scalable, fast, open source Microservice API Gateway built to manage, secure and connect hybrid and cloud-native architectures. Kong runs in front of any service and is extended through plugins including authentication, traffic control, observability and more.
We’re heading into the holiday season, which is a great opportunity to reflect on the year that's just gone. For me, this year has been dominated by the release of Yellowfin 8 and two new products in particular - Stories and Signals.
Deploying a successful technology solution, especially in data management, takes more than just installing software and writing a job (or multiple jobs… thousands in some cases), and running those jobs. If you’re taking on a new data management initiative, deploying using containers and serverless technology, migrating from traditional data sources to Hadoop, or from on-premises to the cloud, you may be sailing in unfamiliar waters.
Ever since Business Intelligence emerged as a technical and commercial practice, the promise has been that the right information, delivered at the right time, in the right format would help us, as users, to make better decisions.
In the last few years, microservices or microservice architecture has become a popular reference in IT due to its benefits and the flexibility this architectural style brings. Before we get into working with microservices and Talend, we should review the basics of microservices or a microservice architecture.
This is the first of a series of blogs on how to architect, engineer and manage performance. In it, I’d like to attempt to demystify performance by defining it clearly as well as describing methods and techniques to achieve performance requirements. I'll also cover how to make sure the requirements are potentially achievable.
