In the past several years, Kubernetes has become the de-facto standard for orchestrating containerized, stateless applications. Tools such as StatefulSets and Persistent Volumes have helped developers build stateful applications on Kubernetes, but this can quickly become difficult to manage as an application scales. Tasks such as machine learning, distributed AI, and big data analytics often

Over the past few years, the conventional wisdom around the value proposition of Big Data has begun to shift. While the prevailing attitude towards Big Data may once have been “bigger is better,” many organizations today recognize that broad-scale data collection comes with its own set of risks. Data privacy is becoming a hotly debated

Serverless computing refers to an architectural pattern where server-side code is run on-demand by cloud providers, who also handle server resource allocation and operations. Of course, there is a server involved on the provider’s side, but administrative functions to manage that server such as capacity planning, configuration, or management of containers are handled behind-the-scenes, allowing

Blockchain technology has a wide variety of potential applications. Fields such as finance, supply chain management, and even voting have seen innovations driven by the development of distributed applications built on blockchains, called DApps. However, developing a DApp on a blockchain often requires low-level knowledge about cryptographic protocols or particular networks. Since no one blockchain

The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems

A data-driven organization collects a wide variety of data to help in strategic decision-making. The cost of storing large amounts and variety of data has dropped dramatically in the last two decades, but too much unstructured data may not improve decision-making, and can even lead to “analysis paralysis.” Organizations react by extracting the most important,

Open source software is software distributed along with its source code, using a permissive license that allows anyone to view, use, or modify it. The term “open source” also refers more broadly to a philosophy of technology development which prioritizes transparency and community development of a project. Typically, development is managed by a governing body,

Video calling over the internet has experienced explosive growth in the last decade. In 2010, surveys estimated that around 1 in 5 Americans had tried online video calling for any reason. By May of 2020, that number had nearly tripled. A significant factor in the growth of video calling has been an open-source project called

Cilium is open-source software built to provide improved networking and security controls for Linux systems operating in containerized environments along with technologies like Kubernetes. In a containerized environment, traditional Layer 3 and Layer 4 networking and security controls based on IP addresses and ports, like firewalls, can be difficult to operate at scale because of

In a distributed application, observability is key to handling incidents and building better, more stable software. Legacy monitoring methods were built to respond to predictable failure modes, and to aggregate high-level data like access speed, connectivity, and downtime. Observability, on the other hand, is a measure of how well you can infer the internal state