Scaling DevOps’ Efficiency & How It Relates To DBOps

DevOps is a collaborative methodology that bridges the gap between software development (Dev) and IT operations (Ops) to improve the speed and quality of software delivery. It emphasizes a culture of collaboration, automation, and continuous improvement, enabling organizations to produce software more efficiently and reliably. The core principles include collaboration and continuous feedback. There are many tools that we can use to build and scale DevOps culture: CI/CD, IaC, monitoring, observability, and most importantly automation.

Scaling DevOps presents unique challenges, especially in database management. Apart from organizational problems like security and compliance, we may face issues with data consistency, schema changes, or automation complexity. Unfortunately, applying modern principles to databases is not trivial. While we can build more microservices and deploy them independently, we can rarely duplicate our data sources with ease. Therefore, moving fast with databases is non-trivial.

To aid that, we need to build a new approach. We must understand how to move fast and increase velocity with databases and what new tools and processes we need to build around. This comes down to incorporating database guardrails and moving towards DBOps. Starting and scaling DevOps in the enterprise is only the beginning. DevOps automation tools helped us achieve improvements in many areas of software development, but now we need to see how to move forward with DBOps.

In this article, we’re going to explore how to build DevOps for databases called DBOps and how it can scale efficiency and drive business growth

The Bottleneck: Scaling DevOps and Database Management

In traditional software development, organizations often maintain distinct boundaries between teams responsible for different software lifecycle stages. Typically, this involves development, operations, quality assurance, and database administration. DevOps aimed to bring these teams together and we succeeded when it came to development, operations, and QA. 

Unfortunately, databases are still out of scope.

This brings multiple challenges that may not be obvious at first glance, especially when we’re used to microservices and frequent deployments. We might be willing to apply the same principles to database management which may not work. Let’s see why.

Recommended reading: Why DevOps Should Own Their Databases & How To Do It

First, we can’t simply build new databases on the side just like we can do with new microservices. We can’t duplicate databases easily because we need to keep the authoritative source of data in sync with other databases. If we want to build another database on the side, we need to decide how we are going to keep all those databases in a consistent state. There are many solutions to this but they are not simple or cheap.

Second, it’s much harder to deploy database changes. It’s simple with microservices - we go with blue-green deployments or canary deployments and we can move things around quickly. We can’t do that with databases, though. If we want to change the schema, we need to do it atomically or we need to build versioning in each of our applications. If something goes wrong, it is much harder to roll things back. Also, we can’t have multiple versions side-by-side. All these aspects are much harder to achieve and require careful planning and deployment.

Next, we can rarely automate deployments. We have many tools for deploying applications automatically, but we don’t have single-click tools for doing the same with databases. We can build those tools but that takes time and effort.

Last but not least, it’s easy to get databases out of sync. We can modify the configuration in one place and not do that in another one which will lead to mismatches. They typically go unnoticed until they cause severe performance issues.

All of that leads to much slower iteration times. We can’t reason about the database performance until we deploy to production or run load tests. However, load tests are very late in the pipeline, they are time-consuming and expensive, and if they find errors, then we need to start from scratch. Instead, we should promote the shift-left movement to have all the checks happening as early as possible. This includes performance checks, configuration analysis, and schema migration reviews. Let’s see how we can achieve that.

DBOps: Automating DevOps for Databases

DBOps aims to bring developers, databases, and operations together. Just like we reshaped our world with DevOps, we need to tackle databases and reorganize them in the same way.

DBOps tackles three areas:

• Deploying (provisioning)

• Testing and validating (issue prevention)

• Monitoring and troubleshooting

DBOps lets DevOps engineers take control of their databases and own them end-to-end. It also unleashes the power of DBAs by automating many of their tasks and letting them focus on more important work. DBOps makes your organization increase velocity, reduce MTTR, and improve across all the DORA metrics.

The first area is deployments. DBOps focuses on automating everything that is required to deploy a database. This includes provisioning new databases, migrating data, and keeping the data in sync between databases. This is typically achieved with Infrastructure as Code (IaC), tools for automated snapshot deployment, and solutions for propagating changes between databases. Depending on your needs, you may just deploy the database with some pre-filled data (for testing purposes), or you may need to wire up the database into the automated synchronization ecosystem.

The second area is tests and validation of the changes as they flow to the database. This requires assessing if code and configuration changes are safe to deploy. We typically achieve that with database guardrails that can automatically assess the code changes right in the developers’ environments. They can also review the schema migrations and find whether they’ll pose any risk of running long or taking the database down.

Recommended reading: Common Challenges in Schema Migration & How To Overcome Them

The third area focuses on troubleshooting and observability. DBOps introduces a way of connecting the dots between environments and throughout the software development life cycle to provide a comprehensive explanation of why the issues appear and how to fix them. DBOps focuses on resolving the issues automatically instead of just identifying them.

Tackling all these aspects helps us move faster. We can deploy things automatically, reduce the MTTR and communication between teams, and increase database reliability. DBAs can focus on architectural work and high-level improvements instead of monitoring the database instances. Also, DevOps engineers can own the databases end-to-end which lets them move much faster. Let’s see how Metis lets you achieve all of that.

Case Study: Scaling Efficiency with Metis Database Guardrails

Metis provides database guardrails that help you streamline the prevention and troubleshooting areas. This directly addresses two core areas of DBOps and lets the organizations move much faster.

Metis taps into the prevention area by analyzing the query performance, configurations, schemas, and schema migrations. To analyze the query performance, Metis uses OpenTelemetry to extract the SQL queries and execution plans and analyze if these queries will work fast enough when triggered in production.

Metis tracks tens of various symptoms of slow queries. It can analyze indexes, filters, s-argable queries, stored procedure calls, data set sizes, buffers, and many more. It can also cross-check queries between environments to analyze the production performance before the developers even merge their code. Metis also analyzes the schema migrations to make sure they are safe to be deployed;

These two elements can lead to as much as 70% incident reduction and improve the velocity of your organization.

Metis provides database observability for all your environments. It can analyze configurations, schemas, indexes, and extensions.

Most importantly, Metis analyzes live queries with execution plans and explains how to make them faster. It provides actionable insights and automated solutions to fix the issues without requiring developers to dive deep.

Metis helps reduce the time spent on manual database tasks and maintenance. It helps achieve database reliability by reducing the MTTR. Developers can safely proceed with their changes because the issues are identified and fixed early in the pipeline which improves velocity. This leads to better database designs, performance, and consistency.

Recommended reading: Observability vs Monitoring - Key Differences & How They Pair

Business Growth Through Scalable DevOps and DBOps

DBOps principles directly improve crucial KPIs. It improves velocity and reduces MTTR. It helps move faster and with greater accuracy. This helps businesses to grow in many ways.

First, improved velocity leads to faster time-to-market. This immediately gives market advantage and helps us develop our product faster.

Second, DBOps leads to reduced MTTR which improves application quality and stability. Developers are free to focus on product improvements instead of fixing bugs. This leads to the distribution of organizational focus and unleashes our teams to focus on core responsibilities.

Last but not least, lower MTTR leads to reductions in operational costs and risks. Customers report as much as 85% reduction in MTTR and doubling the performance which translates directly to lower costs.

In the same way how we built DevOps, now it’s the right time to move on with DBOps and turn databases into first-class citizens for developers. This lets them own the databases end-to-end, automate deployments and troubleshooting, and improve velocity. DBOps positively impacts key organizational metrics and leads to faster business growth.

Summary

DBOps takes DevOps one step further. Just like we streamlined development and operations to improve the deployment time and reduce MTTR, we now need to focus on provisioning, issue prevention, monitoring, and troubleshooting of the databases. DBOps lets DevOps engineers take control of their databases and own them end-to-end. This improves your organization's velocity, reduces MTTR, and helps all the DORA metrics.

DBOps solutions like Metis can help your business grow faster and be more efficient. Metis is one of these solutions and stays ahead of the DBOps movement. Explore further how DBOps solutions can make your organization shine and save money.