For over a decade, Amazon RDS (Relational Database Service) has been a popular managed database service companies have used to set up, operate, and scale databases for web and mobile applications. However, modern, high-scale applications can have upward of tens of thousands of clients. This means that maintaining direct connections between the database and application can quickly begin to consume more resources than the query executions themselves.

I was recently joined by Chris Yates, Senior Vice President, Managing Director of Data and Architecture at Republic Bank for our webinar, Gain the competitive edge with a monitoring tool. The session sparked an insightful conversation around estate monitoring from the perspective of a Senior VP, and I wanted to share some of the key take-aways.

Today’s developers use tools such as VMware Tanzu Application Platform and the Spring framework to build modern applications. However, to build best-of-breed, intelligent, data-centric applications, developers must utilize a unified, in-memory data layer. This enables development teams to truly scale their applications with the pace of business, especially when that business extends across the globe.

PlanetScale is a serverless, MySQL-compatible database platform powered by Vitess. PlanetScale handles database scaling while also providing you with the tools to increase your development velocity, such as branching, non-blocking schema changes, automatic backups, built-in connection pooling, as well as a helpful interface and CLI. Datadog’s new integration gives you deep visibility into your PlanetScale databases, so you can optimize your usage and costs.

At this point in my career, I’ve realized the responsibilities of database administrators (DBAs) and database reliability engineers (DBREs) can differ from company to company, from year to year, and even from quarter to quarter.

Having trouble deploying Kubernetes in a highly available mode and have a backing remote database? This blog is for you. I will explain how to deploy K3s in HA configuration with an external database Postgres. K3s is a certified Kubernetes distribution for IoT and Edge computing. I deployed it on virtual machines in an IBM Z mainframe. Instead of etcd, I choose Postgres as my storage for my K3s clusters. I deployed Postgres in non HA mode.

At Grafana Labs, we’ve seen an increasing number of customers who are scraping hundreds of millions of active time series but need a solution to reliably store and query such a huge amount of data. So in March, we announced our new open source TSDB, Grafana Mimir, the most scalable, most performant open source time series database in the world.

Today, we are excited to announce that the MySQL integration has received an important update, which includes a new pre-built MySQL logs dashboard and the Grafana Agent configuration to view and collect MySQL server logs. The integration is already available in Grafana Cloud, our platform that brings together all your metrics, logs, and traces with Grafana for full-stack observability.

MongoDB is a document-oriented and cross-platform database that maintains its documents in the binary encoded JSON format. Mongo’s replication capabilities and horizontal capability using sharding make MongoDB highly available. An effective monitoring solution can make it easier for you to identify issues with MongoDB such as resource availability, execution slowdowns, and scalability. observIQ recently built and contributed a MongoDB metric receiver to the OpenTelemetry contrib repo.

Do you love reading about how various performance-related issues are traced out and fixed? If so, here's our story of how we fixed the Redis performance issues we were facing by optimizing JedisPool. We hope this will help you strengthen your IT environment. Our Redis performance issues and efforts to find the cause

This is the third part of my DevOps 101 series, so before we get going, let’s do a quick recap of what we’ve covered previously In DevOps 101: What, who, why, and how, I talked about how the definition from Microsoft’s Donovan Brown encapsulates DevOps succinctly as: The union of people, process, and products, to enable the continuous delivery of value to the end users.

With Cloud SQL for SQL Server, you can bring your existing SQL Server on-premises workloads to Google Cloud. Cloud SQL takes care of infrastructure, maintenance, and patching so you can focus on your application and users. A great way to take better care of your application is by monitoring the SQL Server error log for issues that may be affecting your users such as deadlocks, job failures, and changes in database health.

When looking for a highly scalable time series database, there are a number of criteria to investigate and evaluate. First up, it’s always a good idea to consider open source software. It’s more likely to have gone through comprehensive troubleshooting, it’s typically more reliable as it has more timely and widespread peer-review, it better guarantees technology independence, it’s easier to find engineers who are familiar with it and it has great security.

FoundationDB is a distributed NoSQL database designed to support fully ACID transactions. FoundationDB uses an unbundled architecture that consists of an in-memory transaction management system, a distributed storage system, and a built-in distributed configuration system. This enables developers using FoundationDB to manage and configure each part of their database layer separately to ensure desired scalability, high-availability, and fault tolerance.

Wherever your move to a managed cloud database service starts, the big questions are pretty similar. Start figuring it out right here.

Aiven for PostgreSQL® Django is a fully-featured high-level Python web framework. Read on to see how to get it to store its data in an Aiven for PostgreSQL® database.

PostgreSQL is the most powerful and versatile SQL database available today. There is a problem that comes with this level of power and versatility. How do the developers of PostgreSQL fine-tune the default configuration for everyone? Well, they can't do it. The issue is that each database differs not only in design but also in needs. Some systems are used to store massive amounts of data that are rarely searched.

vmagent supports both the pushing and pulling (scrape) of metrics and here are examples of high availability setups for both cases.

Development teams are under increasing pressure to keep up with the demand for faster delivery of database changes, while ensuring code quality isn’t compromised. This pressure comes from both business requirements and industry expectations. The 2021 Accelerate State of Database DevOps report reveals that the number of teams who are elite performers is increasing year-on-year (making up 26% of those in the study) and these teams have more frequent code deployments and a lower change failure rate.

Like all Norse gods and Marvel villains, every open source project has a good origin story. The new episode of “Grafana’s Big Tent” podcast provides that behind-the-code look at building Grafana Mimir, the horizontally scalable, highly performant open source time series database that Grafana Labs debuted in March.

The desire to release features faster and deliver value to customers sooner is prompting many IT teams to adopt DevOps and Agile practices. By automating parts of the development process where possible and working in shorter cycles to release small changes more often, those teams that do well experience fewer errors and produce higher quality code. All of which is good news, until it comes to the database.