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As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Following the innovation of microservices, serverless computing is the next step in the evolution of how applications are built in the cloud. Azure Functions in a nutshell. Dynatrace news.
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Following the innovation of microservices, serverless computing is the next step in the evolution of how applications are built in the cloud. Azure Functions in a nutshell. Dynatrace news.
Cloud platform metrics (AWS, Azure, Kubernetes, etc.). The number of signal fluctuations and the sliding evaluation window for alerting allow you to further fine-tune alerting sensitivity. The post Dynatrace innovates again with the release of topology-driven auto-adaptive metric baselines appeared first on Dynatrace blog.
Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Manual troubleshooting is painful, hurts the business, and slows down innovation.
The innovative technology powering these recent and future use cases comes from Keptn , our recognized CNCF open-source project. If you want more information on Keptn, then I suggest joining the Keptn Open Source community and help us drive innovation that benefits both the open-source project and Dynatrace Cloud Automation Solution.
Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes. Log analysis can reveal potential bottlenecks and inefficient configurations so teams can fine-tune system performance. Accelerated innovation. billion in 2020 to $4.1
The rapidly evolving digital landscape is one important factor in the acceleration of such transformations – microservices architectures, service mesh, Kubernetes, Functions as a Service (FaaS), and other technologies now enable teams to innovate much faster. So please stay tuned for updates. Waterfall visualization of all requests.
SQL Server has always provided the ability to capture actual queries in an easily-consumable rowset format – first with legacy SQL Server Profiler, later via Extended Events, and now with a combination of those two concepts in Azure SQL Database. Unfortunately, my excitement was short lived for a couple of reasons.
Let’s take a look at two key indicators from our 2018 Dynatrace ACM Survey: MTTI (Mean Time to Innovate): How long does it take to push a new feature that is fully tested, ready to go to production, until end-users receive it? You’ll discover how to integrate Dynatrace into a configuration change (e.g.
Containers are the key technical enablers for tremendously accelerated deployment and innovation cycles. For a deeper look into how to gain end-to-end observability into Kubernetes environments, tune into the on-demand webinar Harness the Power of Kubernetes Observability. But first, some background. Why containers? Watch webinar now!
During Perform Barcelona 2019, which, by the way, was a blast, many of you approached us in the Innovation Center asking about new locations for Dynatrace Synthetic. New Azure region available! Stay tuned for other awesome additions we’re working on for you! What’s new? appeared first on Dynatrace blog.
Regional disks Regional disks are available at Azure and Google Cloud but not yet at AWS. Conclusion In this blog post, we discussed the basics of storage configuration and saw how to fine-tune various storage parameters. . > apiVersion: storage.k8s.io/v1 v1 metadata: name: regionalpd-storageclass provisioner: pd.csi.storage.gke.io
And they can do useful work, particularly if fine-tuned for a specific application domain. Amazon Web Services, Microsoft Azure, Google Cloud, and many smaller competitors offer hosting for AI applications. When AI becomes a commodity, it decouples real innovation from capital. Invencions Off Kilter.
Key Takeaways Multi-cloud strategies have become increasingly popular due to the need for flexibility, innovation, and the avoidance of vendor lock-in. Yet it reveals a migration trajectory favoring multi-cloud models as companies wake up to advantages such as heightened innovation potential tied with these varied service structures.
Early years: Fueled by innovation and community-mindedness Initially, the lines weren’t so blurred, and certainly, they weren’t muddied. ” In those early years, the company reflected not only an innovative spirit but also a spirit of community-mindedness. Is MongoDB free for commercial use?
Microsoft engineering is actually sending quite a few folks over the Atlantic to come talk about SQL Server 2017, SQL Server on Linux, GDPR, Performance, Security, Azure Data Lake, Azure SQL Database, Azure SQL Data Warehouse, and Azure CosmosDB. SELECT * FROM Azure Cosmos DB – Andrew Liu. Saturday Sessions.
Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking. We can’t tally and tabulate all the responses, but it’s clear that there’s no shortage of creativity and innovation.
Google Cloud and Microsoft Azure released Scope 3 data in 2021. The last number I saw was “over 20GW”, and Amazon has much better global PPA coverage, including India and China, than Google Cloud and Microsoft Azure, who have very few PPAs in Asia. AWS speaker: T.
Tens of petabytes of data stored in our servers and other object stores such as GCS, S3 and Azure Blobstore. Version5: files metadata in MySQL, files stored in EOS/GCS/S3/Azure and served via HTTP, search in Lucene. Version6: files metadata in MySQL, files stored in EOS/GCS/S3/Azure served via HTTP, search in Elasticsearch.
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