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.

Monitoring your databases is essential for maintaining performance, reliability, security, and compliance of your infrastructure. It allows you to stay ahead of potential issues, optimize resource utilization, and ensure a smooth and efficient operation of your database system. Effective monitoring of Riak involves collecting, analyzing, and acting on a variety of metrics and logs.

Databases are the backbone of enterprise-level organizations, facilitating efficient data management, supporting critical business processes, and providing a foundation for secure innovation and growth. Effective database monitoring is critical for maintaining a high level of performance, security, and reliability in database systems, particularly in enterprise-level environments where large volumes of data and critical business operations are involved.

In February 2001, 17 people met at the Snowbird ski resort in Utah. They were the leading exponents of Extreme Programming, Scrum, and Adaptive Software Development, and they were seeking a set of compatible values based on trust, respect and collaboration. They wanted to make software development easier. And they found it in the form of a manifesto. Their only concern was that the term describing the manifesto came from a ‘Brit’ and they weren’t sure how to pronounce it.

In an era marked by the convergence of Big Data, hybrid cloud use, and the rise of machine learning and artificial intelligence, organisations across the Asia-Pacific (APAC) region find themselves at a critical juncture. The ability to collect, manage, and leverage data effectively is now a determining factor for competitive advantage – and will become more competitive.

I have always had a special fondness for Postgres, particularly captivated by its JSONB capabilities, which is at the core of what I love as it stands as a testament to its adaptability. This functionality exemplifies Postgres’s ability to adapt to modern apps’ rapidly changing needs to support structured and unstructured data to coexist seamlessly within the same database environment. The capabilities of PostgreSQL are not limited to its ability to manage relational and non-relational data.