Side by side with the popularity of hyperscale public cloud providers such as AWS and Microsoft Azure, many organizations maintain on-premises data centers. Organizations may keep them for mission-critical legacy systems with performance demands the cloud cannot meet, or simply be in transition and have not yet migrated their infrastructure to the cloud.

I find it amazing how much opportunity and flexibility cloud environments are creating for organizations of all sizes. I’m seeing more and more companies experimenting with open-source software (OSS) relational database systems, which years ago would’ve been too complicated for the customer to set up. With Azure, you can spin up OSS systems like MySQL or PostgreSQL quickly to determine if the engine fits their needs. If it does, you can continue development on it.

Transformation is key to being at the forefront of the tech industry, and over the past two years, I’ve been excited to lead an outstanding team of developers and engineers as we’ve embarked on evolving our monitoring tools toward observability. With this in mind, we’re excited to announce two significant product releases today. The first is a completely new product offering and subscription service we call SolarWinds® Observability.

Everyone in the information technology industry understands “change” is guaranteed. People are creative and constantly striving to find more efficient ways to solve problems and more innovative ways to deliver services to consumers. But keeping up with the constant cloud and internet technology shifts and taking advantage of all the new capabilities is a harrowing task for digital organizations.

In the recent past, container-based deployment architectures have played a significant role in improving applications on multiple fronts, including: Containers are all-inclusive packages containing lightweight services which are easy to spawn and terminate. However, container-based deployments can comprise hundreds of individual services and their replicas spinning up and down at any moment.

As organizations adopt cloud technologies and modernize their applications, the data they generate and ingest often grows exponentially, leaving them with difficult choices for storing and using this data. Customers are beginning to explore moving away from traditional relational database management systems (RDBMS) because of the data volume to be ingested, as these RDBMS often cannot handle workloads.

Traditionally, when monitoring or troubleshooting active incidents, engineers access logs directly on the source system. However, modern IT environments are now too complex, and engineers can no longer manage and analyze logs effectively this way. With the adoption of microservices and the use of cloud-native infrastructure, it’s no longer feasible.

Technical debt is a term that comes up frequently in IT organizations—whether to characterize software development, tech stack, or infrastructure. For instance, does your organization have operating systems that you keep around because they have legacy code or support an application critical to your operations, but whose vendor is long out of business? Maintaining legacy systems can be considered a form of technical debt.

One of the biggest mistakes I’ve had to repeatedly help companies fix has been poor partitioning design. I’ve seen many database architectures designed in an attempt to make queries faster. While faster queries can be a product of implementing partitioning correctly for a given design, I’ve often seen query response times get much slower from implementing partitioning incorrectly for the database design.