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.

Elastic Stack provides many valuable insights for different users. Developers are interested in low-level metrics and debugging information. SREs are interested in seeing everything at once and identifying where the root cause is. Managers want reports that tell them how good service performance is and if the service level agreement (SLA) is met. In this post, we’ll focus on the service perspective and provide an overview of calculating an SLA.

At KubeCon Europe, it was announced that Elastic Common Schema (ECS) has been accepted by OpenTelemetry (OTel) as a contribution to the project. The goal is to achieve convergence of ECS and OpenTelemetry’s Semantic Conventions (SemConv) into a single open schema that is maintained by OpenTelemetry. This FAQ details Elastic’s contribution of Elastic Common Schema to OpenTelemetry, how it will help drive the industry to a common schema, and its impact on observability and security.

ChatGPT is so hot right now, it broke the internet. As an avid user of ChatGPT and a developer of ChatGPT applications, I am incredibly excited by the possibilities of this technology. What I see happening is that there will be exponential growth of ChatGPT-based solutions, and people are going to need to monitor those solutions.