Fugue

Fugue announces the availability of its cloud-native solution for fully automating the deployment, operations, and enforcement of cloud infrastructure at scale. We're also introducing our leadership team! Check out our latest blogs, our new website, and learn about Fugue at the AWS Summit in New York on Thursday, August 11. Come by Booth 254, talk with our representatives, and get a firsthand look at how we can help you radically simplify cloud infrastructure complexity.

The elastic compute systems of any given enterprise are now distributed across tens, hundreds, thousands or more physical nodes running an ever-growing array of cloud services, but there is no central coordinating function to act as a nexus for control and trust. In the midst of this unwieldy reality is an even more compelling reality—that the cloud is not, in fact, merely a collection of infrastructure. It’s the world’s first global computer. And, just as we abstracted the hardware of individual computers decades ago, we can abstract the distributed hardware of the cloud and radically simplify operations complexity.

Our team is showcasing Fugue's cloud operating system at the Amazon Web Services (AWS) Summit in Santa Clara, CA, today, July 13. Come by booth 420 to talk with our representatives and get a firsthand look at how we can help you radically simplify cloud infrastructure complexity. Last month at the D.C. Summit, Fugue CEO Josh Stella spoke on immutable infrastructure to a packed house—standing room only. Check out that video here!

Washington, DC – June 20, 2016 – Fugue Inc., a venture-backed software startup developing a system for continuously and automatically building, optimizing, and enforcing cloud infrastructure, has announced that CEO Josh Stella will be speaking at the AWS Public Sector Summit in Washington, D.C., on June 20-21, 2016. The team will be demonstrating Fugue's cloud operating system at booth 1008.

Implementation of cryptographic hashes is theoretically not that hard, given a good cryptographic library. However, things get trickier when we want to allow dependency cycles. At that point, we get a bit of a chicken-and-egg problem. We need resource A’s hash to compute resource B’s hash, but that in turn depends on B’s hash again! Let's study the problem and solve it.

For some time, we’ve been using AWS Lambda with SNS topics and votebots, but we hadn’t tried anything big with it. Until now. The Serverless framework gave us the push we needed. Our goal? To power an application useful to a business function via an API built with AWS Lambda and API Gateway, harming no EC2 instances in the process.

This year, 2016, is a year of expansion. Here you see an aggregate mapping of a wide swath of Earth’s cloud “hardware”—installed by Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM/Softlayer. All of it, arguably, can be and eventually will be manipulated as parts of the same machine.

As our company grew, the volume of discourse on Slack became unwieldy. Channels propagated and we were faced with Slack bloat. But the problem was not just one of noise, it was one of uncertainty.

Our system is made up of a collection of services engaging in sophisticated interactions. It became obvious early on that testing manually would only get us so far. Here are some insights that have proven useful as we transitioned from a mostly manual work mode to a mostly automated one in QA testing.

In Python, mocking is accomplished through the unittest.mock module. The module contains a number of useful classes and functions, the most important of which are the patch function (as decorator and context manager) and the MagicMock class. Mocking in Python is largely accomplished through the use of these two powerful components.