Today, we’re excited to share that Kong has closed a $100M Series D financing led by Tiger Global with participation from new investor Goldman Sachs as well as existing investors Index Ventures, CRV, Andreessen Horowitz and GGV Capital. This raise triples our valuation to $1.4 billion and will allow us to scale our go-to-market operations even faster.
When creating new cloud-native applications, developers need to choose the development and deployment methods that best serves their application's needs and purpose. At the same time, organizations are always looking to optimize their cloud budgets and efficiency. Two popular deployment strategies are Function as a Service (FaaS) and Container as a Service (CaaS). Perhaps you've heard about them!
In the latest version of NSolid v4.4.2 NodeSource introduced the new PagerDuty integration that allows users to configure message notifications that are automatically triggered when your Node.js application experiences critical performance, lifecycle, and/or security events in production. This ensures DevOps professionals looking after applications running in production, can be notified on time about new performance and security issues.
Are you tired of slow dashboards? It’s a problem we hear end-users of BI tools complain about time and time again. Whether you’re an end-user or on the data team that the end-users blame, slow dashboards suck! With many BI tools now offering their own connectors and lightweight data transformation/preparation layers, slow dashboards are a common pain point across all organizations.
We’re seeing an increase in demand for hybrid AI deployments. This trend can be attributed to a number of factors. First of all, many enterprises look to hybrid solutions to address data locality, in accordance with a rise in regulation and data privacy considerations. Secondly, there is a growing number of smart edge devices powering innovative new services across industries.
No, not really. You probably won’t be rich unless you work really hard… As nice as it would be, you can’t really predict a stock price based on ML solely, but now I have your attention! Continuing from my previous blog post about how awesome and easy it is to develop web-based applications backed by Cloudera Operational Database (COD), I started a small project to integrate COD with another CDP cloud experience, Cloudera Machine Learning (CML).
The digital revolution is making a deep impact on the automotive industry, offering practically unlimited possibilities for more efficient, convenient, and safe driving and travel experiences in connected vehicles. This revolution is just beginning to accelerate – in fact, according to a recent Applied Market Research study, the global connected car market was valued at $63.03 billion in 2019, and is projected to reach $225.16 billion by 2027, registering a CAGR of 17.1% from 2020 to 2027.
This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight
“Automated scripts are checking known paths for expected results. That’s not truly testing. Testers discover the unknown, and this skill is still very much so needed, whether there’s automation or not. Without human intuitive exploration, a team may be blind to their most expensive bugs.”— Angie Jones The above quote lets us understand the power of Manual Testing.
Standard Deviation is an important metric in performance testing analysis and informs us how stable the application under test is. In other words, it tells us if the requests that occur during the test are consistent or not. Standard Deviation measures how the response times are spread out around the average response time (mean). A small standard deviation means that the response time of all the requests are close to each other.
