Today we announced our Series F round of $100M led by Greenspring Associates, with Eleven Prime, IVP, Sapphire Ventures, Top Tier Capital Partners, Baseline Ventures, Threshold, Scale, Owl Rock, and Next Equity Partners. Thank you to our customers, community, partners, investors, and team. This latest investment allows us to invest as well; in our product, our community, and in our people. We build for the builders of the digital age: developers.

We are proud to announce that CircleCI has acquired Vamp, the first cloud-native release orchestration platform. This paves the way for CircleCI customers to have first-class release orchestration and continuous validation, all natively within the CircleCI platform.

In today’s data-centric world, metrics or numbers define all performance benchmarks. The time between when an event starts and ends shows how well a system can handle and process such events. One of such metrics is MTTR. MTTR usually stands for Mean Time To Resolution, but it has held several meanings over the years. MTTR is a metric used to measure how well a system can bounce back from errors and provide long-lasting solutions.

Fargate offers a great value proposition to AWS users: forget about virtual machines and just provision containers. Amazon will take care of the underlying hosts, so you will be able to focus on writing software instead of maintaining and upgrading a fleet of Linux instances. Fargate brings many benefits to the table, including small maintenance overhead, lower attack surface, and granular pricing. However, as any cloud asset, leaving your AWS Fargate tasks unattended can lead to nasty surprises.

Auto-instrumenting Lambda Monitoring didn’t originate through a focus group or business plan. It started as a hackathon project in which our growth team used Cloudwatch to build a prototype that could instrument Lambda functions with Sentry. We did this by using Cloudformation’s stack to automatically create resources in a customer environment while streaming CloudWatch Logs to Sentry through the Kinesis Firehose.

Congratulations, you finally consider moving your apps to Kubernetes. It is a big day! Here is a checklist to ensure you did not forget anything essential to increase your chances of success using Kubernetes. We divided those points into three sections, from the most important to the least. Let’s go.

For many years, it has been possible to scale Cortex clusters to hundreds of replicas. The relatively simple Dynamo-style replication relies on quorum consistency for reads and writes. But as such, more than a single replica failure can lead to an outage for all tenants. Shuffle sharding solves that issue by automatically picking a random “replica set” for each tenant, allowing you to isolate tenants and reduce the chance of an outage.

Observability, which originated from control theory, measures how well you can understand a system’s internal states from its external outputs. Observability uses instrumentation to provide insights that aid monitoring. In DevOps, gaining observability is achieved through a set of monitoring solutions. The shift to use one vendor platform to do so, versus multiple solutions, make sense as.

I’ll come clean and admit it – this part of the series will be a bit interesting given the fact that I know very little about Elasticsearch. So really, this is an honest test of the question – “can I still build something good with Dashboard Server even if I only have nominal knowledge of the tool where the data is sourced from?”

Speedscale was built primarily to provide engineering teams with better insight into their applications over time, replaying single transactions for root cause analysis that give developers and SREs confidence that tomorrow’s application code will work just as well in production as it did yesterday.