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.

We're excited to announce that indices created in Elasticsearch 7.10 will be smaller. Bigger isn't always better, and our internal benchmarks reported space reductions up to 10%. This may not seem like much for small use cases, but it's huge for teams handling (and paying for cloud storage of) petabytes of data.

Managing hundreds or thousands of containers has quickly become the standard for many organizations. With infrastructures growing more complex, we want every user to find value with Elastic (regardless of where or how they operate). We created Elastic Cloud on Kubernetes (ECK) — the official Operator — to simplify setup, upgrades, scaling, and more for running Elasticsearch and Kibana on Kubernetes.