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Gain insights into Kubernetes errors with Elastic Observability logs and OpenAI
As we’ve shown in previous blogs, Elastic® provides a way to ingest and manage telemetry from the Kubernetes cluster and the application running on it. Elastic provides out-of-the-box dashboards to help with tracking metrics, log management and analytics, APM functionality (which also supports native OpenTelemetry), and the ability to analyze everything with AIOps features and machine learning (ML).
Challenges of observing Kubernetes: Understanding a complex and dynamic system
As technology evolves in the enterprise, oftentimes the processes and tools used to manage it must also evolve. The increased adoption of Kubernetes has become a major inflection point for those of us in the monitoring and management side of the IT operations world. What has worked for decades (traditional infrastructure monitoring) has to be adjusted to the complexity and ephemeral nature of modern distributed systems where Kubernetes has a prime role.
Trace your Azure Function application with Elastic Observability
Adoption of Azure Functions in cloud-native applications on Microsoft Azure has been increasing exponentially over the last few years. Serverless functions, such as the Azure Functions, provide a high level of abstraction from the underlying infrastructure and orchestration, given these tasks are managed by the cloud provider. Software development teams can then focus on the implementation of business and application logic.
The impact of NWDAF on telco service providers: Embracing vendor agnostic data analytics
Network Data Analytics Function (NWDAF) is a key component in 5G networks, designed to collect, analyze, and deliver valuable insights to service providers. NWDAF provides an unbiased, vendor-vendor agnostic view of the network, expanding telco visibility beyond traditional use cases. As network complexities grow, service providers require unbiased and accurate data to make informed decisions, driving the demand for vendor agnostic data analytics.
The Met Office gains valuable data insights to make informed decisions with Elastic
The Met Office, the UK's national weather service, is tasked with predicting the unpredictable - the ever-changing weather patterns that can have a huge impact on people's lives. Having been in the business for over 150 years, they require a reliable and powerful monitoring and insights capability to ensure their systems and processes run optimally.
Using AIOps effectively with Elastic Observability
Over the past several years, one topic that has become of increasing importance for DevOps and site reliability engineering (SRE) teams is AIOps. Artificial intelligence for IT Operations (AIOps) is the application of artificial intelligence (AI), machine learning (ML), and analytics to improve the day-to-day operational work for IT operations teams.
How to use Elasticsearch and Time Series Data Streams for observability metrics
Elasticsearch is used for a wide variety of data types — one of these is metrics. With the introduction of Metricbeat many years ago and later our APM Agents, the metric use case has become more popular. Over the years, Elasticsearch has made many improvements on how to handle things like metrics aggregations and sparse documents. At the same time, TSVB visualizations were introduced to make visualizing metrics easier.
RCA Series: Accelerate security investigations w/ machine learning and Elastic (3/4)
Comprehensive security requires multiple layers of threat protection. Sophisticated threats exploit idiosyncrasies in your environment. Unsupervised machine learning identifies patterns of normal activity from your data, and therefore can catch attacks that standard approaches to threat hunting, such as pre-defined rules, are likely to miss. This video explains how machine learning adds a layer to your threat protection, and how interactive tools offered in the Elastic Security solution accelerate the investigation of security incidents.
RCA Series: Root Cause Analysis in Manufacturing, Electric Grids & Connected Devices (4/4)
With digitization adopted in many industries, real-time data from manufacturing and operational equipment can be used to monitor and optimize operation - by applying data-driven modeling including machine learning. Learn how you can ingest sensor data from industrial processes and operational equipment into Elastic, build monitoring dashboards and set up automated alerts in Kibana, and apply predictive modeling to optimize your operations (OT).