The ability to monitor your Elastic Cloud deployment is critical for helping ensure its health, performance, and security. Our Elastic Observability solution provides unified visibility across your entire ecosystem — including your Elastic Cloud deployments. Elastic Observability allows you to bring your logs, metrics, and APM traces together at scale in a single stack so you can monitor and react to events happening anywhere in your environment.

Last month we launched the Elastic Contributor Program to recognize and reward the hard work of our awesome contributors, encourage knowledge sharing within the Elastic community, and build friendly competition around contributions. But how do you start contributing? In this blog post, we’ll walk through how to log in to the Elastic Contributor Program portal and set up your profile so you can begin submitting your own contributions and validating others’ contributions!

Elastic 7.6 introduced the inference processor for performing inference on documents as they are ingested through an ingest pipeline. Ingest pipelines are incredibly powerful and flexible but they are designed to work at ingest. So what happens if your data is already ingested? Introducing the new Elasticsearch inference pipeline aggregation, which lets you apply new inference models on data that's already been indexed.

Chances are you already spent a big part of your day looking for a document, an email, or an answer that lies deep within a Google Slides presentation. Thankfully, you landed in the right place. With Workplace Search, finding the right information across all your cloud and on-premises data platforms is now easier than ever, and it’s a few clicks closer than you expect.

Google Operations suite, formerly Stackdriver, is a central repository that receives logs, metrics, and application traces from Google Cloud resources. These resources can include compute engine, app engine, dataflow, dataproc, as well as their SaaS offerings, such as BigQuery. By shipping this data to Elastic, you’ll get a unified view of the performance of resources across your entire infrastructure from cloud to on-prem.

At Siren, we build a platform used for “investigative intelligence” in Law Enforcement, Intelligence, and Financial Fraud. Investigative intelligence is a specialisation of data analytics that serves the needs of those that are typically hunting for bad actors. Such investigations are the primary focus of law enforcement and intelligence, but are also critical to uncovering financial crime activities and for threat hunting in cybersecurity.

Let’s talk visibility for a moment. Security visibility is a data-at-scale problem. Searching, analyzing, and processing across all your relevant data at speed is critical to the success of your team’s ability to stop threats at scale. Elastic Security can help you drive holistic visibility for your security team, and operationalize that visibility to solve SIEM use cases, strengthen your threat hunting practice with machine learning and automated detection, and more.

Machine learning pipelines have evolved tremendously in the past several years. With a wide variety of tools and frameworks out there to simplify building, training, and deployment, the turnaround time on machine learning model development has improved drastically. However, even with all these simplifications, there is still a steep learning curve associated with a lot of these tools. But not with Elastic.

Elastic Cloud on Kubernetes (ECK) is the official operator for provisioning Elastic Stack deployments in Kubernetes. It orchestrates not only day-one provisioning, but also has the processes and best practices for day-two management and maintenance baked in. If you want to run your own Elastic Stack deployment on Kubernetes, then look no further than ECK!

Machine learning in the Elastic Stack provides you with an intuitive way to detect anomalies in vast data sets. But even the most sophisticated anomaly detection job might not reveal the root cause of anomalous behavior. After an anomaly is detected, you may need to dive into further analysis, review multiple corresponding metrics, and investigate how they relate to the anomalous spike.