Operations | Monitoring | ITSM | DevOps | Cloud

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!

So you’re using InfluxDB Cloud, and you’re writing millions of metrics to your account. You’re also running a variety of downsampling and data transformation tasks. Whether you’re building an IoT application on top of InfluxDB or monitoring your production environment with InfluxDB, your time series operations are finally running smoothly. You want to keep it that way.

We are excited to partner with AWS and announce the availability of InfluxDB on the new Amazon Elastic Container Registry Public announced this week at AWS re:Invent. With this new registry, developers can now find their favorite open source products from within the AWS developer experience. At InfluxData, we believe it is important to bring our product — InfluxDB — to the platforms and ecosystems where our developers are building. And of course, many of our developers are building on AWS.

The growing popularity of Docker has led many enterprises to containerize applications. By 2022, more than 75% of global organizations will be running containerized applications in production, Gartner predicts, up from less than 30% today. Yet the shift to containers has posed new challenges to performing effective monitoring. As more applications move to the cloud and become containerized, the demand for dynamic container monitoring has become more urgent.

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 analysing data one of the biggest questions you may often face is: what is causing this situation? In this blog, we’re going to look at how causal inference can be used to understand in more detail what the biggest influencing factors are across a dataset. Traditionally in Splunk, we talk about correlation; does metric x go up or down in accordance with metric y or is there a relationship between x and y?

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.