I’m excited to see our vision for an open source path forward for Elasticsearch and Kibana taking shape with OpenSearch! Since Elastic announced its intent to close-source Elasticsearch and Kibana, we’ve been working in full gear to have an open source path forward for these projects. This is our commitment to our users, this is our commitment to the community. We’ve collaborated with AWS and others to fork Elasticsearch and Kibana and create OpenSearch.

Artificial intelligence (AI) continues to take its place in the tech field. From virtual assistants to software capable of self-remediation, AI enhances everyday user experiences and increases the use of IT automation solutions in the workplace. Most of the tech workforce welcomes AI with open arms and understands the advances smart technology can bring and how it can improve business objectives.

Technical issues, such as fatal crashes, are one of the biggest reasons why users uninstall mobile applications, so quickly identifying and resolving issues is vital for user retention. This can be challenging, particularly in the Android market, which has a wide variety of mobile devices and versions of the Android operating system. You need visibility into every issue so you can determine which crashes impact your application the most and efficiently resolve them.

When Grafana Labs CEO and co-founder Raj Dutt announced to the team that the company would be relicensing our core open source projects from Apache 2.0 to AGPLv3, he opened the floor for discussion and encouraged anyone who had further questions to reach out. We believe in honesty and transparency, so we collected hard questions from Grafanistas, and Raj answered them for this public Q&A. The time felt right. As I’ve said publicly before, I’ve been thinking about this topic for years.

Grafana Labs was founded in 2014 to build a sustainable business around the open source Grafana project, so that revenue from our commercial offerings could be re-invested in the technology and the community. Since then, we’ve expanded further in the open source world — creating Grafana Loki and Grafana Tempo and contributing heavily to projects such as Graphite, Prometheus, and Cortex — while building the Grafana Cloud and Grafana Enterprise Stack products for customers.

Technology has accelerated changes toward information-based healthcare delivery and management. Today’s multi-disciplinary approach to delivering better healthcare outcomes coupled with advanced imaging and genetic-based customized treatment models depend on AI/ML driven information systems. At Robin.io, we believe machine learning is the life-saving technology that will transform healthcare. AI/ML challenges the traditional, reactive approach to healthcare.

Coca-Cola is one of the most recognizable brands on the planet. That’s because wherever it’s produced, the quality, product, and design are the same. When three Coca-Cola companies merged in 2016 to create Coca-Cola European Partners, operational differences became apparent. The company needed a way to standardize platforms and processes across 13 Western European countries and 50 bottling plants. We had three systems in place, three ways of working, and multiple languages.

‍Resilience in Action is a podcast about all things resilience, from SRE to software engineering, to how it affects our personal lives, and more. Resilience in Action is hosted by Kurt Andersen. Kurt is a practitioner and an active thought leader in the SRE community. He speaks at major DevOps & SRE conferences and publishes his work through O'Reilly in quintessential SRE books such as Seeking SRE, What is SRE?, and 97 Things Every SRE Should Know.

A common challenge in data engineering is to combine traditional data warehousing and BI reporting with experiment-driven machine learning projects. Many data scientists tend to work more with Python and ML frameworks rather than SQL. Therefore, their data needs are often different from those of data analysts. In this article, we’ll explore why having a data lake often provides tremendous help for data science use cases.