Operations | Monitoring | ITSM | DevOps | Cloud

We are thrilled that Elastic has been recognized as a Leader and a Fast Mover in the 2021 GigaOm Radar Report for Cloud Observability. GigaOm Radar reports offer a forward-looking view of the market and are designed to help IT decision makers evaluate solutions with an eye to the future. Their analysts consider not just where the solutions are today, but also how the market is evolving and where the solutions are headed relative to that trend.

As your Elastic usage increases and your use cases expand, it's important to know the benefits and cost savings that you can achieve by running Elasticsearch as a service. But since every Elasticsearch implementation can vary by use case and deployment model, it can be complicated to tackle on your own. So with that in mind, we are excited to share the Elastic Cloud Value Calculator.

The Elastic Common Schema (ECS) provides an open, consistent model for structuring your data in the Elastic Stack. By normalizing data to a single common model, you can uniformly examine your data using interactive search, visualizations, and automated analysis. Elastic provides hundreds of integrations that are ECS-compliant out of the box, but ECS also allows you to normalize custom data sources. Normalizing a custom source can be an iterative and sometimes time-intensive process.

Indices are an important part of Elasticsearch. Each index keeps your data sets separated and organized, giving you the flexibility to treat each set differently, as well as make it simple to manage data through its lifecycle. And Elastic makes it easy to take full advantage of indices by offering ingest methods and management tools to simplify the process.

Cloud API logs are a significant blind spot for many organizations and often factor into large-scale, publicly announced data breaches. They pose several challenges to security teams: For all of these reasons, cloud API logs are resistant to conventional threat detection and hunting techniques.

Cache is king for speedy data retrieval. So if you’re interested in how Elasticsearch leverages various caches to ensure you are retrieving data as fast as possible, buckle up for the next 15 minutes and read through this post. This blog will shed some light on various caching features of Elasticsearch that help you to retrieve data faster after initial data accesses.

Your logs, metrics, security, and trace data are all invaluable to you. They are mission critical for your observability and security needs. As your IT infrastructure grows and keeps generating more and more data, your data volumes and data storage needs go up accordingly. It can quickly become cost-prohibitive to indefinitely store all of it on your hottest machines.

Today, I’m happy to share more about our partnership and commitment to our users that they will have the best possible experience of both Elasticsearch and Grafana, across the full breadth of Elasticsearch functionality, with dedicated engineering from both Grafana Labs and Elastic. Through joint development of the official Grafana Elasticsearch plugin users can combine the benefits of Grafana’s visualization platform with the full capabilities of Elasticsearch.

For the 7.10 release of Elastic Workplace Search, we highlighted some of the new analytics logging capabilities and took you through a short walkthrough of some sample analysis scenarios. With the 7.11 release we introduced analytics fields, which open up new possibilities for exploring query and click data with helpful dashboards and visualizations.

We are excited to bring you a number of updates for using Elastic Cloud (Elasticsearch managed service) in the AWS Marketplace.