InfoQ Homepage Metrics Content on InfoQ
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Q&A on Cloud Discovery Tool for Multi-Cloud Environments
Cloud Discovery is an open-source tool from Twistlock that connects to cloud providers and gets an inventory of all the various infrastructure resources deployed. Cloud Discovery gathers and reports resources metadata in an aggregated way. Furthermore, application security holes can be identified when there’s more visibility across environments, such as which resources are missing a firewall rule.
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Three Pillars with Zero Answers: Rethinking Observability with Ben Sigelman
At KubeCon NA, held in Seattle, USA, in December 2018, Ben Sigelman presented “Three Pillars, Zero Answers: We Need to Rethink Observability” and argued that many organisations may need to rethink their approach to metrics, logging and distributed tracing.
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Making Machine Learning Adoptable for Clinicians
Dr. Alexander Scarlat explains the core tenants of machine learning in his 12-part series "Machine Learning Primer for Clinicians." Scarlat covers defining aspects of machine learning, followed by examples that communicate aspects of measuring the performance of machine learning models. The series uses animated charts in place of the math to help readers understand the machine learning concepts.
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Grafana Adds Log Data Correlation to Time Series Metrics
The Grafana team announced an alpha version of Loki, their logging platform that ties in with other Grafana features like metrics query and visualization. Loki adds a new client agent promtail and serverside components for log metadata indexing and storage.
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Uber Open Sources Its Large Scale Metrics Platform M3
Uber’s engineering team released its metrics platform M3 as open source which it has been using internally for some years. The platform was built to replace its Graphite based system, and provides cluster management, aggregation, collection, storage management, a distributed time series database (TSDB) and a query engine with its own query language M3QL.
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Enabling Continuous Delivery with a Dedicated Team
Robin Weston describes how an external enablement team was able to introduce continuous delivery practices in an organization with high resistance to change and siloed teams. Rather than just bringing in new technology and tools, the team focused on sharing and educating teams. Practices ranged from continuous integration, to following the test pyramid, or reducing cycle time by identifying waste.
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Building Observable Distributed Systems
Today's systems are more and more complex; microservices distributed over the network and scaling dynamically, resulting in many more ways of failure, ways we can't always predict. Investing in observability gives us the ability to ask questions to systems, things we never thought about before. Some of the tools that can be used for this are metrics, tracing, structured and correlated logging.
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Thanos - a Scalable Prometheus with Unlimited Storage
The Improbable engineering team open sourced Thanos, a set of components that adds high availability to Prometheus installations by cross-cluster federation, unlimited storage and global querying across clusters.
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Metrics Collection from Large Scale IoT Deployments at Vivint
Vivint's engineering team built their own metrics collection platform to collect and analyze metrics from their deployed devices. The key motivation behind writing their own system was to be able to store only aggregated data and focus on its analysis, which they achieve by their Rothko project.
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How to Measure Continuous Delivery
Stability and throughput are the things that you can measure when adopting continuous delivery practices. These metrics can help you reduce uncertainty, make better decisions about which practices to amplify or dampen, and steer your continuous delivery adoption process in the right direction.
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Metrics Collection and Monitoring at Robinhood Engineering
The Robinhood server operations team published a series of articles talking about their metrics collection, monitoring and alerting infrastructure. OpenTSDB, Grafana, Kafka and Riemann form the core of the stack, with Kafka acting as a proxy layer from which the data is pushed into Riemann for stream processing of the metrics and into OpenTSDB for storage.
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Monitoring Metrics for Docker Containers
Stefan Thies, DevOps Evangelist at Sematext, in a recent post discusses ten important container monitoring metrics and their implications on operating Docker containers, specifically when running many containers per host. Combined in a single correlated view these metrics provide a starting point for monitoring Docker-based environments.
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Eight Dragons of Agile Measurement
Larry Maccherone, Director of Analytics and Research at AgileCraft and frequent speaker at agile conferences like QCon, gave a webinar in which he discusses the major risks and challenges when introducing metrics in an agile environment. Risks are referred as "dragons" and the techniques to get rid of them as "slayers".
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Defining Devops as CALMSS
Forrester has come-up with a new definition of DevOps. Forrester has added an additional “S” for sourcing in the CALMS definition of DevOps. They believe that DevOps must be supported by a solid sourcing strategy to extend the ecosystem. This then brings them to the acronym of CALMSS.