In our recent paper, “Tuning In to AI,” we examine the role of DBAs and database monitoring tools in AI.
How your company systematically stores and manages its corporate data, including the vast datasets needed for complex AI systems, significantly impacts the role of anyone responsible for ensuring database integrity. Databases provide the infrastructure for storing, accessing, and managing data critical for training machine learning models and other aspects of AI.
Database professionals rarely work in isolation. In many environments, they collaborate with a range of information technology (IT) specialists, including data architects, system administrators, network engineers, and software developers. The demands on DBAs can vary significantly, from the complex tasks of implementing indexing strategies and advanced query tuning to the mundane tasks of routine data backup and recovery work.
The challenge faced by modern database professionals is the need to optimize database operations amidst ever-changing workloads, particularly due to the advent of AI. At a business level, the pressure is on to meet key business SLAs (service level agreements) and ensure that AI systems stay online.
The Role of Database Monitoring Tools
Quickly uncovering the root cause of performance problems is central to meeting key SLAs and minimizing the risk of downtime, data loss, and poor customer experiences. Due to the complex nature of large-scale IT environments, database professionals need to quickly understand the overall health of the databases under their wing. Continuous automated monitoring of database performance allows DBAs to detect anomalies and performance issues rapidly and with clarity. There are two high-level approaches to database management and monitoring:
- Proactive monitoring means anticipating costly issues before they arise.
- Reactive monitoring means your teams can quickly triage and remediate issues adversely affecting your AI systems.
A good database monitoring tool will show performance issues and any downtime they cause. It allows database professionals to rapidly find the root cause of issues while also providing remediation recommendations. But the role of database tooling isn’t just about resolving issues; it's about optimization strategies and tuning and configuring databases for very demanding needs, such as those required by AI systems.
Key points from the whitepaper:
- Companies are exploring how AI can support business growth with hopes of enhancing existing models or developing new products and services. Effective AI systems require significant amounts of high-quality data.
- Databases provide the infrastructure for storing, accessing, and managing data critical for AI applications. Data storage can be on-premises, in the cloud, or hybrid, with a trend towards cloud-based solutions.
- As AI adoption increases, the demands on database administrators are growing. They must ensure the data infrastructure supports AI applications. Data storage for AI can be on-premises, in the cloud, or hybrid, with a trend towards cloud-based solutions.
- Database management and monitoring tools are designed to enable proactive monitoring and rapid troubleshooting of multiple database instances and types.
- Good monitoring tools can be configured for different user types and are powerful enough to provide insight into multiple database instances, with both real-time and historical analysis capabilities.
[Download The Whitepaper Today]
