Operations | Monitoring | ITSM | DevOps | Cloud

Services are the backbone of our systems. Whether they’re functional microservices or logical components of a traditional application, they are the pieces that make up our businesses. We can’t do the computer thing without services. But who’s responsible for owning a service in your company or organization? The cast of characters involved in the lifecycle of a service is more than just software engineers.

In this previous article we learned to create our own Docker images to test and distribute our applications regardless of their dependencies. As we know, Docker is a wonderful tool for collaborative programming work, allowing to work in a virtually identical environment regardless of the operating system you are in.

When mission-critical applications were all implemented within your data center, capacity management seemed easy – Most organizations deployed infrastructure sets (compute, network, and storage) to meet their 95 to 99% capacity requirement. For a retailer, that might mean planning around a “Black Friday” date (Cyber-Monday wasn’t around then). For a bank or a manufacturer using ERP or financial systems, this might instead be the end of year close.

Since StatsD was introduced by Etsy in 2011, it has become a mainstay of infrastructure monitoring. But as dev and ops teams rely more and more on containerized microservices, they have pushed the limits of StatsD’s design. One major shortcoming is that StatsD has no built-in support for tagging your metrics with key-value pairs.

If you have ever used a search bar on a website, you've probably used Elasticsearch. Elasticsearch is an open-source search and analytics engine used for full-text search as well as analyzing logs and metrics. It allows websites to use autocomplete in text fields, search suggestions, location or geospatial search. Tons of companies use Elasticsearch, including Nike, SportsEngine, Autodesk, and Expedia.

There’s no one-size-fits-all migration path for moving legacy applications to the cloud. These applications typically reside on either physical servers, virtual machines or on premises. While the goal is generally to rearchitect or redesign an application to leverage cloud-native services, it’s not always the answer.

In the Data Age 2025 report, worldwide data is expected to grow 61% to 175 zettabytes by 2025. The enterprise sector, in particular, generates more than 30% each year. To be ready for a digital future, consider the scaling strategy of data infrastructure beforehand. Scale-up and scale-out are the main ways to add capacity to your infrastructure.

The seamless scaling and headache-free reliability of a serverless application architecture has become compelling to a broad community of cloud specialists. But for those who have yet to become converts, a specific issue related to service startup latency—Cold Starts—has been one of the cited key objections. Fortunately, the serverless marketplace is maturing.