In a recent blog post, we discussed how ChatGPT and Elasticsearch® can work together to help manage proprietary data more effectively. By utilizing Elasticsearch's search capabilities and ChatGPT's contextual understanding, we demonstrated how the resulting outcomes can be improved. In this post, we discuss how users’ experience can be further enhanced with the addition of facets, filtering, and additional context.
Across industries and geographies, businesses rely heavily on Systems Applications and Products (SAP) systems. These powerful and versatile systems streamline operations and manage critical data spanning areas like finance, human resources, and supply chain. However, the real-time monitoring of these systems, with an in-depth understanding of performance metrics and quick anomaly detection, is paramount for smooth operations and business continuity. It's here that our unique offering steps in.
As more organizations leverage the Amazon Web Services (AWS) cloud platform and services to drive operational efficiency and bring products to market, managing logs becomes a critical component of maintaining visibility and safeguarding multi-account AWS environments. Traditionally, logs are stored in Amazon Simple Storage Service (Amazon S3) and then shipped to an external monitoring and analysis solution for further processing.
Elastic Enterprise Search 8.8 seamlessly bundles new Elastic developed semantic search capabilities with an expanding catalog of open code database and storage connectors. Additionally this release adds rich capabilities to measure and simplify adding features to your search application. These new capabilities allow customers to: Elastic Enterprise Search 8.8 is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of the new features in this latest release.
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.
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.
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.
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.
