Understand the two dimensions of scaling for database query and ingest workloads, and how sharding can make scaling elastic — or not. Scaling throughput and performance are critical design topics for all distributed databases, and sharding is usually a part of the solution. However, a design that increases throughput does not always help with performance and vice versa. Even when a design supports both, scaling them up and down at the same time is not always easy.

Like every business sector, Financial Services has been on a rollercoaster ride over the past couple of years. The pandemic forced a change in the way businesses work, and the way products and services are delivered to customers. Deloitte summed it up beautifully in the introduction to its ‘Finance 2025 Revisited report’1: “COVID-19 has sped up business innovation and stress-tested the concept of 100% remote work.”

Relational database or non-relational database: which should you use for your projects? It’s a common question. When choosing the database type that’s right for your requirements, it’s important to understand the differences between the two. Both database types are practical in different situations and use cases and have commonalities.

Every enterprise is now facing the same challenge: to do more with less in a demanding economic environment. Releasing value to customers sooner while at the same time controlling the costs of their infrastructures, retaining skilled staff, and reducing risk. With technology now central to the way many enterprises offer, package and deliver their products and services, it has a big part to play here. Microsoft CEO Satya Nadella summed it up perfectly in the keynote to Microsoft Inspire 2022 when he said.

In this article, we are going to look at how to monitor Redis performance using Prometheus. This will allow Redis Administrators to centrally manage all of their Redis clusters without setting up any additional infrastructure for monitoring. To follow the steps in this blog, sign up for the MetricFire free trial, where you can use Graphite and Grafana directly on our platform.

Monitoring distributed systems like MongoDB is very important to ensure optimal performance and constant health. But even the best monitoring tool will not be efficient without fully understanding the metrics it gathers and presents, what they represent, how to interpret them, and what they affect. That’s why it is crucial not only to collect the metrics but also to understand them.

Why do some organizations excel in streamlining their database operations and applications development while others find it immensely challenging? Why can some database teams embrace agility while others take months of cycles to deploy even a single line of code? What secret sauce can allow some database teams to work smarter (not harder), streamline database development lifecycles better, get to deployment faster, and create an overall stronger alignment across departments?

In the last few years, the usage of databases that charge by request, query, or insert—rather than by provisioned compute infrastructure (e.g., CPU, RAM, etc.)—has grown significantly. They’re popular for a lot of the same reasons that serverless compute functions are, as the cost will scale with your usage. No one is using your site? No problem: you’re not charged.

Unlike SQL, which was developed in the 1970s to limit data duplication, NoSQL is a relatively new type of database. NoSQL came about in response to increasing amounts of data, and it uses a distributed system to help organize large amounts of structured and unstructured data. NoSQL is popular in business tech and other industries, with large organizations such as Amazon, Google, and LinkedIn using NoSQL databases.

I was recently joined for a live webinar by Tony Maddonna (Microsoft Platform Lead, Enterprise Architect & Operations Manager at BMW) and Hamish Watson (DevOps Alchemist at Morph iT) to discuss their experiences with digital transformation and the impact it had on them, their teams and the wider organization.