Operations | Monitoring | ITSM | DevOps | Cloud

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).

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.

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.

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.

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.

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.

With governments doubling down on logging compliance, many public sector organizations have been focusing on optimizing their log management, especially to ensure they retain logs for required periods of time. Logs — though seemingly straightforward — are the backbone of many mission-based use cases and therefore have the potential to accelerate mission success when centrally organized and leveraged strategically. In public sector, logs are instrumental in.

In the previous blog in our root cause analysis with logs series, we explored how to analyze logs in Elastic Observability with Elastic’s anomaly detection and log categorization capabilities. Elastic’s platform enables you to get started on machine learning (ML) quickly. You don’t need to have a data science team or design a system architecture. Additionally, there’s no need to move data to a third-party framework for model training.

Engines in Elastic App Search enable you to index documents and provide out-of-the-box, tunable search capabilities. By default, engines support a predefined list of languages. If your language is not on that list, this blog explains how you can add support for additional languages. We’ll do this by creating an App Search engine that has analyzers set up for that language.