The issue of unsecured databases is growing. In 2019, 17 percent of all data breaches were caused by human error — twice as many as just a year before. And the IBM/Ponemon 2019 report found that the estimated probability of a company having repeated data breaches within two years grew by 31 percent between 2014 and 2019. Why is this happening?
"Upgrade" can be a four-letter word for admins, so at Elastic, we try to make the upgrade process as simple as possible. Why? Because we pack a ton of goodness into each release, but you can only take advantage of that goodness by being on the latest version of the Elastic Stack. This is also why we make the latest version available on Elastic Cloud the same day that we release.
Modern cloud storage tools such as Dropbox give teams the ability to easily share and centralize content, conveniently collaborate on projects, and sync data across devices. They’ve proven to be real productivity enhancers, especially with the expansion of work-from-home workforces. But cloud storage tools often end up being a dumping ground for lots of content and various clutter, making it clumsy at best (and next to impossible at worst) to find anything.
The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.
Welcome to our series on cost management and optimization in Elasticsearch Service. With the increased functionality in Elastic Cloud, it is now easier than ever to utilise many of the free and open features of the Elastic Stack to optimise your cloud deployment. This blog is a great resource for reviewing your existing high availability and data management strategies when it comes to cost management.
On July 14, 2020, Microsoft released a security update related to a remote code execution (RCE) and denial of service (DoS) vulnerability (CVE-2020-1350) in Windows DNS Server (2003 - 2019).
Our new Elastic for Students and Educator program provides online resources and support to help you teach and learn no matter where you are. Hear from Luis Francisco Sánchez Merchante, an educator based in Spain, as he reflects on the challenges he’s faced while teaching during a global pandemic.
In our previous post, we introduced the concept of the Elastic Workplace Search Custom Source API as a means of adding data for which a prebuilt content source integration isn’t available. We used a simple example — a CSV file of contact information — to demonstrate the process along with the use of the associated REST API. In this post, we explore ingesting data from a more complex organizational source: Trello.
With the increasing popularity of Python web frameworks supporting asynchronous I/O like FastAPI, Starlette, and soon in Django 3.1, there has been a growing demand for native async I/O support in the Python Elasticsearch client. Async I/O is exciting because your application can use system resources efficiently compared to a traditional multi-threaded application, which leads to better performance on I/O-heavy workloads, like when serving a web application.
This post is the third in our Kubernetes observability tutorial series, where we explore how you can monitor all aspects of your applications running in Kubernetes, including: We’ll discuss using Elastic Observability to perform application performance monitoring (APM) with the Elastic APM.
