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.
