Agile database development best practices
Methodologies such as agile development and DevOps can help data professionals fashion their ideal DataOps ecosystem. This article reviews five fundamental best practices that can get you started in creating your own agile database development process.
As the technology industry shifts to agile development, traditional database development sticks out like a cumbersome bottleneck in an otherwise well-oiled production machine. While database management is indeed complex, it is not immutable. Change is an integral part of progress and can be had in any discipline.
The transition in database development doesn’t have to be wrought with dangers and complexity. The goal of the agile paradigm is to turn the development cycle into a collaborative ecosystem that promotes flexibility in every stage. In adopting an agile database development process, database professionals have the benefit of enhancing their work environments with the collaborative power of the DevOps infrastructure.
This article reviews five fundamental best practices that can get you started in creating your own agile database development process.
Tip #1: Improve Collaboration with Cross-Functional Teams
The biggest challenge in the adoption of an agile database development process is the natural resistance to change. Behind every piece of technology stands at least one human being, in charge of multiple tasks for the maintenance and management of the technology. To ensure a cohesive and smooth transition, it’s important to turn this into a collaborative effort that takes into account all the adoptees involved.
While traditional development paradigms keep group roles in functional teams, the agile development paradigm relies on cross-functional teams. Functional teams keep development, testing, database, and any stage in the production separate. Cross-functional teams incorporate all the stages of the development. Thus, in agile development, database management can no longer function as a separate entity. Rather, database management is now required to join the DevOps team.
When everyone works towards the same goal, production becomes easier and faster. While the agile paradigm relies on collaboration, it doesn’t conflict with the team’s independence. It’s possible to create an agile database development ecosystem in which the database team has the authority to govern itself. The entire DevOps team can own the entire stack of their allotted microservice, while the database roles maintain and manage the database.
Tip #2: Increase Visibility with Database Version Control
Visibility, or monitoring, is the practice of ensuring all the components of the database work properly. This responsibility is often entrusted with the data professional in charge of reliability engineering. Visibility gives teams a comprehensive view of the state of the database, from capacity planning, breaking alerts, to performance and behavior analysis.
Visibility provides the information needed to mitigate current risks and prevent future threats. As such, visibility is often viewed as the first and most critical step in ensuring the viability of the database. Any point of database failure may result in delayed releases. In worst case scenarios low database visibility might lead to data loss or theft.
Data version control can provide you with a single source of truth. While once data version control didn’t seem possible, nowadays there are software tools and solutions that can help you gain as much visibility as possible. Keep in mind that, unlike applications, databases are stateful. Database schema version control should endeavor to preserve the data by running scripts that can recognize the state of the database and implement controls that never overwrite the data.
Tip #3: Improve Throughput with Automation
According to the 2017 State of DevOps Report, organizations that automate tasks achieve faster software delivery while still maintaining a high level of quality. Automation helps organizations release and maintain almost error-free software and implement quick recovery fixes. As cloud migration picks up in popularity, automation has never been easier. While automation can be applied to all types of databases, automation tools for cloud databases often offer faster and simpler solutions.
Agile database management sees database management as a fundamental part of the development cycle, rather than a separate entity. Therefore, any automation should integrate with the existing DevOps automation pipeline. Integrating with the DevOps team creates a unified ecosystem that promotes safe and efficient orchestration of database deployments. Since database professionals are in charge of implementing the automation, they can transition from manual processes while still ensuring data quality standards and company policies are met.
Tip #4: Protect Data Integrity with Automated Integration and Performance Tests
Data integrity is a broad umbrella term that encompasses tasks that strive to ensure the reliability and accuracy of the data throughout the entire lifecycle. Whether the data is stored in a structured database, a data warehouse, or a data lake, data integrity prevents unintentional changes to information.
Automated testing helps preserve the integrity of the data by providing safe database feedback. With automated testing, data professionals can create a set of regression tests to replace specific manual tests. Automated testing tools can send alerts that provide the team with timely reports that enable quick fixes that protect the integrity of the data. Thus, automated testing frees data professionals from manual testing and shifts the focus on creating value.
Tip #5: Enhance the Feedback Process with Static Code Analysis
The focus of agile development on fast development and delivery can put DevOps teams under immense pressure. While human error is a constant factor in any situation, the effort to produce faster can lead to reduced quality. As opposed to application development, database development is far less forgiving to errors and mistakes.
To ensure certain database standards are met, data professionals can use static code analysis. Rather than relying solely on peer review, static code analysis introduces an automated feedback process that supplements the team’s feedback loop. Static code analysis is implemented in the early stages of the development, before testing, to provide initial analysis that kickstarts the feedback loop. Automating this stage of the development helps accelerate the database development, therefore reducing some of the pressure that weighs on the team.
Conclusion—Agile Database Development as a Stepping Stone to DataOps
Methodologies such as agile development and DevOps can help data professionals fashion their ideal DataOps ecosystem. Aided by automation tools, the DataOps infrastructure can turn into a fast and efficient machine that preserves, protect, and analyzes data. In a world that increasingly relies on the valuable information extracted from digital data, agile database development provides an automated infrastructure for achieving more reliable data in less time.