What Comes Next: A Five Part Checklist for Enterprise DB Success
How to De-Risk Licensing, Get AI-ready, and Slash Operational Drag in the Months Ahead
The Long & Short
In today’s world of growing database diversity, organizations will have to take careful steps to avoid runaway costs and operational inefficiencies while remaining agile and AI-ready for the uncertain road ahead. Lean on truly open, AI-ready solutions and adopt a unified, platformized approach to database management to position your organization for sustained success.
The reality for most enterprises in 2025 is that no single database can service all workloads. AI initiatives, customer-facing applications, transactional data, and more all have different needs and place different demands on data infrastructure. This has inherently led to a world in which the average organization now utilizes multiple databases based on purpose, performance, and architecture.
While this approach provides flexibility and performance gains, it obviously invites quite a bit of additional complexity as well—especially when managing a diversity of databases and management tools at scale.
To help stay ahead, organizations should consider the following best practices designed to maximize value and minimize complexity of diverse OS database environments:
Embrace Platform Engineering for Open Source Databases: Treat databases as part of a broader internal developer platform (IDP) to improve scalability, reduce friction, and ensure consistency across environments.
- Adopt or develop a control plane that supports provisioning, scaling, backups, and access control across open source and source available databases (e.g. PostgreSQL, MySQL).
- Invest in self-service UIs and APIs for developers and data teams to request and manage database instances (using open source tools like Percona Everest).
- Standardize database deployment pipelines (e.g., with Terraform) to reduce variability and human error.
- Integrate database lifecycle operations (spin-up, patching, decommissioning) into your existing DevOps workflows.
Invest in the Right Tools for the (AI-Driven) Road Ahead: Build for a world in which AI workloads are abundant and open source is the gold standard for handling data of all types—from structured, transactional data to vector data and beyond.
- Evaluate and adopt open source databases well-suited to AI workloads, including vector search capabilities (e.g., PostgreSQL + pgvector).
- Ensure compatibility with popular data science ecosystems (e.g., Python, Jupyter, TensorFlow, PyTorch).
- Look for open source solutions that promote extensibility and integrations to ensure database capabilities can grow and expand organically with the evolving technological landscape.
Centralize Observability and Performance Monitoring: The wider your database architecture becomes the greater the potential for blind spots and inefficiencies to flourish. Implement systems and services that enable monitoring and management to ensure you are getting the most out of your data infrastructure and spotting issues before they become incidents.
- Deploy a unified observability stack (e.g., Percona Monitoring and Management, Grafana, etc.) across all database services.
- Monitor key health indicators: query latency, connection saturation, disk I/O, replication lag, and memory usage.
- Set up automated alerting for anomalies or usage spikes.
- Implement auditing and query analytics to detect inefficient usage patterns or unoptimized queries.
Eliminate Licensing Challenges with Community-Led, Open Source Solutions: Prioritize community-backed, open source databases with permissive, OSI-approved licenses to reduce vendor lock-in and legal complexity. Focus on licensing literacy to avoid falling victim to open washing.
- Favor databases with permissive licenses (e.g., Apache 2.0, MIT, BSD) over those with restrictive or ambiguous terms.
- Conduct a license audit before adopting or expanding use of any open source DBMS.
- Stay informed about license changes and community forks (e.g., OpenSearch vs. Elasticsearch).
- Engage with active community ecosystems to benefit from innovation, support, and long-term viability.
Leverage Automation and Self-Service: Utilize AI-driven tools and other forms of automation to enable internal users (data scientists, analysts, devs) to request, deploy, and interact with data and databases in a way that is streamlined and accessible.
- Automate database provisioning, schema deployment, and backup configuration via CI/CD workflows.
- Implement role-based access control (RBAC) and policy-as-code to safely enable self-service access.
- Use AI-assisted tooling to help non-DBAs diagnose query issues, recommend indexes, or provision optimized storage.
- Allow internal users to search, browse, and request access to data assets through a centralized portal or data catalog.
By treating the database layer as a composable platform—rather than a patchwork of disconnected tools—organizations can: accelerate time-to-insight for internal teams; reduce operational burden through automation and observability; support modern workloads, from AI/ML to real-time analytics, without compromise; and maintain governance, scalability, and flexibility as they grow.
Ultimately, success with open source databases in 2025 isn’t about choosing the perfect DBMS—it’s about building a system where many databases can thrive together under a common, unified strategy. And to best prepare for the road ahead, leaders should build those strategies around services and solutions that are AI-ready, cloud-native, and most importantly, free and open.
Kubernetes isn’t just about scalability, it also provides a kind of continuous resiliency that simply isn’t possible without it. If you have issues that need to be resolved, patching that needs to be done, or a new deployment on deck, you can get things done with little-to-no disruption. Processes that used to require an hour of downtime now require just a few minutes, if at all. That’s why Kubernetes has become indispensable for our operations.
Diego M. Infiesta
IT Infrastructure Manager
Ryanair