With Elastic 7.9, the Elastic Agent and Fleet were released, along with a new way to structure indices and data streams in Elasticsearch for time series data. In this blog post, we'll give an overview of the Elastic data stream naming scheme and how it works. This is the first in a series of blog posts around the Elastic data stream naming scheme.

Many users need their Elasticsearch clusters to always be available. And a lot of these same users also want to upgrade their Elasticsearch environment when a new version is released, so they can take advantage of all the new features and functionality. The result is that admins end up upgrading the Elasticsearch engine while it is operating at full capacity in production. Sound too good to be true?

The telecommunications world is in the middle of its fourth industrial revolution. Organisations are trying to bring out as many new services as possible to monetise their infrastructure, but despite their modern approach, they still own and maintain legacy — and most importantly — multi-vendor infrastructures. Due to complex organisational structures and decentralised management systems, most responsibilities are divided between multiple departments.

Signed search keys in Elastic App Search give you more control over a user's search experience. You can tailor the experience to show results you know are more relevant to the specific user while also controlling what data the user can see and search over.

It is with great excitement that we announce our first-ever supervised ML and security integration! Today, we are releasing a supervised ML solution package to detect domain generation algorithm (DGA) activity in your network data. In addition to a fully trained detection model, our release contains ingest pipeline configurations, anomaly detection jobs, and detection rules that will make your journey from setup to DGA detection smooth and easy.

We’re excited to share that the official Elastic Cloud Terraform provider is now available in beta. Operations and SRE teams often rely on Terraform to safely manage production-related infrastructure using methodologies such as infrastructure as code, which allows you to apply peer-reviewed infrastructure changes in an automated and controlled fashion. The provider works with Elasticsearch Service on Elastic Cloud, Elastic Cloud Enterprise, and Elasticsearch Service Private environments.

We are delighted to announce that Elastic Cloud on Kubernetes (ECK), the official Elastic Operator, is now a Red Hat OpenShift Certified Operator. The operator helps make it easier to deploy and automate Elasticsearch, Kibana, APM Server, Beats, and Enterprise Search in your OpenShift environment.

Teams around the world are going through changes. With offices closed from Hong Kong to San Francisco, Zoom meetings are the new norm, and online platforms are the standard for collaborating and keeping businesses running as usual. We’ve written about distributed work and how doing distributed well requires the right tools. When a traditional office environment isn’t available, information naturally becomes fractured across multiple single-purpose platforms.

In this blog post you’ll learn how to create visualizations for Kibana dashboards from Jupyter Notebooks. The good news for you as a Python developer: You won’t have to spend time writing cumbersome plain JSON documents or even JavaScript code. Under the hood, we’re going to make use of Kibana’s Vega plugin which became generally available with Elastic Stack 7.10.

On December 13, SolarWinds released a security advisory regarding a successful supply-chain attack on the Orion management platform. The attack affects Orion versions 2019.4 HF 5 through 2020.2.1, software products released between March and June of 2020. Likewise, on December 13, FireEye released information about a global campaign involving SolarWinds supply-chain compromise that affected some versions of Orion software.

When approached by stakeholders in their organization, few security teams can confidently demonstrate that logging and alerting capabilities are working as expected. Organizations have become more distributed and reliant on cloud offerings for use cases such as identity and access management, user productivity, and file storage. Meanwhile, adversaries have extended their operational capabilities in cloud environments.

The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users. Elastic Cloud on Microsoft Azure gives you access to Elastic Observability, allowing you to monitor your infrastructure and see how every signal interrelates by utilizing a wide variety of resources that can be deployed in minutes.

Recently, the JetBrains .NET advocacy team published a deep-dive post powered by data we retrieved from the official NuGet APIs with the goal of better understanding our community's OSS past and trying to predict trends into the future. This resulted in a giant dataset. Given our experience with Elasticsearch, we knew that the best tool to process millions of records was what we're calling the NECK stack: .NET, Elasticsearch, CSV, and Kibana.

Elastic Maps added several exciting features with the release of Kibana 7.10 that let you do even more with your location data. From making it easier to upload files with latitude and longitude fields to being able to trigger an alert when something moves across a boundary, there are a host of jaw droppingly cool new things to check out. I’ll be providing a good overview in this blog, but to see the real magic, I’d suggest: Now onto the good stuff!

Elastic on Amazon Web Services (AWS) gives you the power of Elastic Enterprise Search, Elastic Observability, Elastic Security as well as the Elastic Stack. You can quickly and easily search your environment for information, analyze data to observe insights, and protect your technology investment. Elastic Cloud lets you deploy your way, whether as a managed service or with orchestration tools you manage in the cloud.

Python introduced support for type hints in Python 3.5 via PEP 484, allowing tools like Mypy and Pyright to check your Python code for type conflicts before execution. This also helps tools that provide code auto-complete — like IDE, IPython, and Jupyter Notebooks — by providing a complete function signature, even for functions that are generated on import time like the Elasticsearch Python client.