Despite DevOps being the norm and best practice for software development today, many organizations still treat their data differently from code, neglecting these well-established best practices over many years. This session will challenge this outdated approach and advocate for defining better “Data as Code” strategies, emphasizing the importance of applying the same rigor and methodologies used in software development to data operations.

We will explore behind-the-scenes common tasks, formerly called DBA tasks––where although the role has been eliminated in modern engineering organizations, the maintenance requirements are still the same, and have even evolved in scale and complexity.

We’ll learn how to properly provision databases, scale them for optimal performance, and manage security which can no longer be an afterthought with the critical nature of today’s data. We’ll review strategies for optimal database sizing, maintenance, and migration, as well as managing schema changes effectively to minimize disruption and maintain data integrity.

While our infrastructure has largely migrated to “as Code” (IaC) management - databases have been left behind, and should level up to derive all the same benefits that IaC provides for infrastructure. We won’t stop there though, all of this can also be applied to evolving data management & storage, including maintaining secure data lakes to store and analyze large volumes of data. DataOps is DevOps, join us to learn how to bridge this divide for your engineering teams operations.

Key points covered will include:

• Behind-the-Scenes DBA Tasks: Learn how to provision databases, scale for optimal performance, and manage security to ensure efficient and secure database operations.
• Optimal Database Sizing, Maintenance, and Migration: Strategies for ensuring databases are properly sized, maintained, and migrated to support evolving business needs.
• Efficient Schema Changes: Techniques for managing schema changes effectively to minimize disruption and maintain data integrity.
• Data as a Software (SDLC): Implementing software development lifecycle practices in data management to enhance reliability and consistency.
• Provisioning Databases with IaC: Utilizing Infrastructure as Code (IaC) to automate and streamline database provisioning.
• Establishing Secure Data Lakes: Best practices for creating and maintaining secure data lakes to store and analyze large volumes of data.