It’s easy to pitch technology buying decisions as black or white, where one camp is the promised land and the other is a dystopian wasteland where companies and profits go to die. But that doesn’t match reality. Instead, organizations need to balance technical trade-offs with their needs. So, while it’s easy to stand atop the “rip and replace” mountain and shout the virtues of your new technology, that’s not something that most organizations are willing to do.

Monitoring your ClickHouse database is a proactive measure that helps maintain its health and ensure that it continues to meet the needs of your applications and users efficiently. It allows you to address issues before they become critical, ensuring that your database environment is secure, reliable, and performing optimally. In this article, we'll detail how to use the Telegraf agent to collect performance metrics from your ClickHouse clusters, and forward them to a datasource.

Efficient connectivity for stateful workloads such as databases across multiple Kubernetes clusters is crucial for effective multi-cluster deployments. The challenge lies in providing seamless communication between services deployed across these clusters. Calico Cluster mesh enhances Kubernetes’ native service discovery, allowing it to function across multiple Kubernetes clusters.

Ever wondered how popular websites manage millions of users and interactions without crashing? The answer lies in MongoDB, a NoSQL database, document-based model. This is particularly useful for applications like social media platforms, where users can have multiple posts, comments, and interactions. MongoDB is also highly scalable, able to handle large amounts of data and traffic by distributing the workload across multiple servers.

In Part 1 of this blog, we prepared our demo container environments using Docker for the Node Express and Python Flask applications. Now, we move on to the more complex phase of our exploration, where we will dissect and explain the inner workings of our applications. This sequel is designed for those who want to improve their web development skills, offering a comprehensive guide to debugging and tracing.

In a world of too many acronyms and initialisms, we’re here to talk about one more – TDM, aka DevOps test data management.

Grafana Mimir is our open source, horizontally scalable, multi-tenant time series database, which allows us to ingest beyond 1 billion active series. Mimir ingesters use consistent hashing, a distributed hashing technique for data replication. This technique guarantees a minimal number of relocation of time series between available ingesters when some ingesters are added or removed from the system.

This is the first post in a series of two looking at debugging and tracing MySQL, which has been a foundation stone of the tech industry, utilized by applications big and small, from personal blogs to complex e-commerce platforms. MySQL has demonstrated adaptability and robustness countless times, making it a critical part of the Internet’s infrastructure. This adaptability has helped MySQL remain relevant amidst the constantly evolving technological landscapes.

Whether your data is on the moon or in your basement, Grafana has got you covered. As the go-to platform for monitoring and observability, Grafana has been your trusty sidekick for data visualization for years, in part because we’re always looking for new ways to support our users, no matter where they keep their data. That’s why we’re excited to tell you about our latest supported data source — SurrealDB.

If you’re working with AI, you’re working with data. From numerical data to videos or images, regardless of your industry or use case, every AI project depends on data in some form. The question is: how can you efficiently store that data and use it when building your models? One answer is PostgreSQL, a proven and well-loved database that, thanks to recent developments, has become a strong choice to support AI.