Articles by Philipp Kahr
Philipp is a consultant on the services EMEA team working with different customers that spawn from small to large multinational companies. Prior to Elastic, he was a infrastructure and cloud architect at T-System. In his spare time, he likes to find curious ways to analyse and visualise any form of data.
Identify slow queries in generative AI search experiences
Uncover the power of Elasticsearch tracing and optimize your APM with insights into query times, bulk indexing, and machine learning impacts. Master semantic search and enhance performance for data-driven decisions.
How to troubleshoot slow Elasticsearch queries for better user experience
Master the art of troubleshooting slow Elasticsearch queries for better user experience, and learn how to optimize query performance by using APM insights and Lens charts.
How to activate APM in Kibana and Elasticsearch to gain next-level alerting insights
Kibana alerting has been around for a while, but there's more in the works to provide better views into what each alert is doing and where it’s spending its time. Learn how we worked to improve the insights you can gather with APM in Kibana.
Monitoring service performance: An overview of SLA calculation for Elastic Observability
Elastic Stack provides many valuable insights for different users, such as reports on service performance and if the service level agreement (SLA) is met. In this post, we’ll provide an overview of calculating an SLA for Elastic Observability.
Measuring the impact of YouTube chess tutorials on the use of popular openings
This is the third blog post in a series of ones to follow. We will take a look at how the use of popular chess openings is influenced by YouTubers and streamers.
Insights into chess game trends: A detailed look at Lichess data
This is the second blog post in a series of ones to follow. We will take a look at chess game trends using Lichess data.
Importing 4 billion chess games with speed and scale using Elasticsearch and Universal Profiling
This is the first blog post in a series of ones to follow. We will use Elastic APM and Universal Profiling to solve performance problems that can occur when importing chess game data using a custom Python application.