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.
When building a full-text search experience such as an FAQ search or Wiki search, there are a number of ways to tackle the challenge using the Elasticsearch Query DSL. For full-text search there’s a relatively long list of possible query types to use, ranging from the simplest match query up to the powerful intervals query.
Kubernetes is a popular container orchestration system at the heart of the Cloud Native Computing Foundation projects. It automates the deployment, lifecycle, and operations of containers, containerized applications, and "pods," which are groups of one or more containers. The platform itself, along with each of these workloads, may generate event data. There are different kinds of data associated with these processes.
Let’s start off with some good news: since 7.9.0, your Elastic Workplace Search deployment has been collecting and logging product usage data for you and your team. Usage data like, what your users are searching for, what links they're actually clicking on, and which searches are falling short. And better yet, in a future release we’ll be putting a prebuilt Workplace Search analytics dashboard at your fingertips in Kibana, one of the most powerful visualization tools available.
