Ask Sendai

Earlier this month I met with an enterprise-scale AWS customer. They told me that they are planning to go all-in on AWS, and want to benefit from all that we have learned about setting up and running AWS at scale. In addition to setting up a Cloud Center of Excellence, they want to set up a secure environment for teams to provision development and production accounts in alignment with our recommendations and best practices.

GitLab 12.0 marks a key step in our journey to create an inclusive approach to DevSecOps, empowering ‘everyone to contribute’. For the past year, we’ve been on an amazing journey, collaborating and creating a solution that brings teams together. There have been thousands of community contributions making GitLab more lovable. We believe everyone can contribute, and weâve enabled cross-team collaboration, faster delivery of great code, and bringing together Dev, Ops, and Security.

I’m a developer, or at least that’s what I tell myself while coming to terms with being a manager. I’m definitely not an infosec expert. I’ve been paged more than once in my career because something I wrote or configured caused a security concern. When systems enable frequent deploys and remove gatekeepers for experimentation, sometimes a non-compliant resource is going to sneak by. That’s why I love tools like AWS Security Hub, a service that enables automated compliance checks and aggregated insights from a variety of services.

Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities What happens when artificial intelligence (AI) technology arrives on wireless channels? For a start, AI promises to address the design complexity of radio frequency (RF) systems by employing powerful machine learning algorithms and significantly improving RF parameters such as channel bandwidth, antenna sensitivity and spectrum monitoring.

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. However, the key insight here is that these caches are partially shared among the CPUs, which means that perfect performance isolation of co-hosted containers is not possible. If the container running on the core next to your container suddenly decides to fetch a lot of data from the RAM, it will inevitably result in more cache misses for you (and hence a potential performance degradation).

A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe. Through data, we can learn the movement of cars and pedestrians in a city, and train our self-driving vehicles how to drive. We map pedestrian movement in cities with LiDAR-equipped cars, search video collected from the roads for interesting, real-life situations that can be used in model training, build and report on simulations, and test on both a closed track and real roads to reinforce our training.

Maxim Fedorov demonstrates an example of a live Erlang cluster being scaled from just a few nodes to 10,000 machines with no service interruption. Source: infoq.com

Anca Zaharia and Jason Maude focus on the successes and pitfalls Starling Bank encountered in building Open Banking. They cover topics such as the OAuth security flow, the permissions-controlled API, obtaining permissions for third party actions, and verifying payees between banks. Source: infoq.com

Progressive delivery is a term used to describe incremental rollout of changes to your system that optimizes for reducing risk and limiting “blast radius” of any negative outcomes of your changes. When we make a change to our system (code change or configuration change) we first expose this change to the smallest subset of users and analyze the impact of this change. If we find that the change has negligible or positive impact, we can continue to increase the subset of users that see this change.

Litmus is an open source chaos engineering framework for Kubernetes environments running stateful applications. Created by MayaData, Litmus enables users to run test suites, capture logs, generate rep The book Mastering Collaboration by Gretchen Anderson provides techniques and exercises that can be used to improve collaboration in teams and between teams and their environment. It explores topics like enlisting people, teamworking, trust, and respect, generating ideas collectively, decision making, and transparent communication.