Operations | Monitoring | ITSM | DevOps | Cloud

In the era of technology defined by cloud computing, features evolve at the blinding speed of continuous deployment. When large software development organizations like Microsoft deliver semi-annual releases of products, like SQL Server, the volume of new features can be so large they can be hard to grasp. Microsoft continues to push the boundaries on what’s possible, both in on-premises and cloud data platforms, and they aim to make the life of data professionals much more manageable.

Many DevOps teams struggle to achieve consistent builds and releases due to ineffective collaboration and communication strategies. Over 71% of software teams today are working remotely from global locations, according to a survey by Perforce and DevOps.com. Interestingly, this consistency challenge can be easily solved by a simple approach – database version control.

Earlier this year we introduced the world to Grafana Mimir, a highly scalable open source time series database for Prometheus. One of Mimir’s guarantees is 100% compatibility with PromQL, which it achieves by reusing the Prometheus PromQL engine. However, the execution of a query in the Prometheus PromQL engine is only performed in a single thread, so no matter how many CPU cores you throw at it, it will only ever use one core to run a single query.

In the era of technology defined by cloud computing, features evolve at the blinding speed of continuous deployment. When large software development organizations like Microsoft deliver semi-annual releases of products, like SQL Server, the volume of new features can be so large they can be hard to grasp. Microsoft continues to push the boundaries on what’s possible, both in on-premises and cloud data platforms, and they aim to make the life of data professionals much more manageable.

DevOps has been bridging the gap between the development and operations teams for more than a decade. It is eliminating the organizational barriers between the two and automates the delivery process. It's time to start treating databases the same way we treat the delivery pipeline when applying DevOps. When we have a large database, automation is crucial. When the database has too much information, changing a table can take ages and block further changes like inserts, updates, or deletes.

We are constantly working on contributing monitoring support for various sources, the latest in that line is support for Cassandra monitoring using the OpenTelemetry collector. If you are as excited as we are, take a look at the details of this support in OpenTelemetry’s repo. The best part is that this receiver works with any OpenTelemetry collector: including the OpenTelemetry Collector and observIQ’s distribution of the collector.

At incident.io we use gorm.io as the ORM library for our Postgres database, it’s a really powerful tool and one I’m very glad for after years of working with hand-rolled SQL in Go & Postgres apps. You may have seen from our other blog posts that we’re heavily invested in tracing, specifically with Google Cloud Tracing via OpenCensus libraries.