Accelerate Pipelines and Unlock BI With Database Observability

When a business hinges on optimal app experiences and reliable data, understanding the state of the databases and pipelines that feed them is crucial.

Without this visibility, application teams find it harder to rapidly scale and evolve at the speed of market innovation. Similarly, data teams struggle to uncover the game-changing insights they need to make optimal decisions.

Database observability provides a lens through which these teams can understand the details of their database change management workflows to continuously optimize the CI/CD pipeline and remove any roadblocks getting in the way of fast, compliant, error-free changes. 

This observability can be enabled through database change management automation, using logs from the automation platform to paint a complete picture of the “who, what, where, when, and why” for every database change.  

We’ll look at the key components of observability and then dive more specifically into its role for each team. We will also cover some of the challenges of database observability and implementing observability across your pipelines.

Key components of database observability

By analyzing logs and metrics, database observability enables continuous optimization of the database change process, turning insights into actionable advice for improving CI/CD pipeline performance and business outcomes.

Here’s what it takes to enable database observability and maximize its value.  

Source: Liquibase

Automated changes and logs

Automated database change management and, subsequently, automatically logging every detail in the process can be foundational for database observability. These logs offer a passive, structured approach to capturing data about database operations. 

This makes it easy to zero in on the granular information needed during investigation and troubleshooting. It also facilitates a deeper analysis of trends and patterns over time. 

By automating the change and logging processes, organizations can ensure that valuable workflow data is collected consistently, enabling detailed observability of database change operations.

Guardrails, governance, and security

Optimal database change management requires a balance between agility and control. Observability tools incorporating guardrails and governance mechanisms help maintain this balance by enforcing policies and compliance rules while enabling the speed and flexibility needed in modern DevOps environments. 

Observability can also play a pivotal role in improving security by helping to identify vulnerabilities, access concerns, changes in off-hours, and other risky scenarios. This reduces the risk of compromising data integrity or exposing sensitive information.

DevOps integration and collaborative change management

Whole-heartedly embracing database DevOps practices, including GitOps for database management, is essential for scaling and optimizing database operations. 

Database observability can provide insights that help teams identify inefficiencies and streamline their workflows. As part of the broader DevOps methodology and complete CI/CD pipeline, observability can accelerate best-practice adoption, the implementation of new technologies, and the overall performance of the environments and teams behind them. 

A collaborative approach to database change management ensures that updates are seamlessly integrated and aligned with broader business objectives. Observability facilitates this collaboration by offering a transparent view of database changes, their impacts, and the context in which they occur.

This enables teams across the organization to work together more effectively, aligning database management with application development, data pipeline, and other operational goals.

Insights for application pipeline acceleration

Database observability can help accelerate the application pipeline by offering an in-depth look at the inner workings of database change management and deployments. 

Application, DevOps, database, and IT team members can dissect and understand the nuances of their database release operations, identifying areas ripe for optimization and streamlining.

This insight accelerates the application development pipeline and ensures that database changes are in sync and deployed as quickly as application updates, maintaining integrity, performance, and innovation across the board.

For the application pipeline, database observability illuminates the “how” and “why” behind each database change in a manner that is both accessible and actionable. Teams can use this information to refine and accelerate their workflows. Plugged into observability platforms, these metrics, logs, and data can be aggregated to enable teams to draw correlations between database change and application outcomes. 

This visibility is crucial for diagnosing workflow bottlenecks, understanding the impact of schema changes, and ensuring that database modifications do not introduce unexpected behavior or downtime in the application.

It’s best to approach database observability with workflow-related questions and then build observability dashboards that speak to those needs.

Examples of these kinds of questions include: 

• How do different database change strategies affect deployment frequency and success rates?
  Observability can reveal patterns in how various approaches to database updates (e.g., blue/green deployments and canary releases) influence the overall stability and velocity of application rollouts.
• What is the impact of recent database schema changes on application performance?
  By correlating deployment timelines with performance metrics, teams can pinpoint which changes might have led to degradation or improvement in application responsiveness, guiding future schema modifications.
• Which parts of the database change process frequently experience bottlenecks, and what can be done to alleviate them?
  Metrics on deployment frequency, failure rates, and number of changes can help identify stages slowing down the process, enabling targeted workflow optimizations.
• How effective are current rollback strategies in minimizing downtime during failed deployments?
  Observability insights into rollback times and their impacts on application availability offer valuable feedback for refining disaster recovery processes.

Addressing these questions through the lens of database observability allows teams to adopt a more data-driven approach to optimizing their application pipelines.

The continuous feedback loop provided by observability tools ensures that database teams can iterate quickly, making informed decisions that enhance both database and application performance. 

This strategic alignment between database changes and application development cycles is crucial for organizations aiming to achieve DevOps maturity and what comes with it—agility, speed, and excellence in their software delivery processes.

Data science and business intelligence insights

Database observability gives business intelligence, data science, and analytics teams a critical foundation for transforming logs and metrics into actionable business insights. 

By enabling a deep understanding of the database’s operational health and performance, observability unlocks visibility needed by these teams to optimize data flows, ensure data quality, and accelerate the delivery of analytics and insights that drive strategic decision making.

At its core, database observability for business intelligence revolves around the ability to track, measure, and analyze the flow of data through an organization’s databases and data pipelines. This includes understanding how data is ingested, processed, stored, and accessed, as well as identifying any bottlenecks or inefficiencies that may impact data quality or availability. 

The insights gained from database observability enable data professionals to make informed decisions about how to best structure and manage their data to support business objectives.

For business intelligence, data science, and analytics teams, database observability can answer workflow optimization questions, such as:

• How are recent database schema changes impacting the accuracy and timeliness of our business intelligence reports?
  Understand the direct correlation between specific database modifications and their effects on BI outputs. Observability can track schema changes to specific report alterations, helping teams adjust their models or queries for consistent accuracy.

• Which database changes have led to improvements or degradations in data processing times for analytics pipelines?
  By analyzing change logs and performance metrics, observability tools can pinpoint which adjustments have positively or negatively influenced data throughput, enabling teams to replicate successful changes or roll back detrimental ones.

• Can we identify patterns of database change that correlate with increased data quality issues?
  Observability enables teams to detect recurring change patterns that may be contributing to data integrity problems, such as incorrect data types or loss of data during transformations, guiding more informed change management practices.

• What database changes have historically preceded policy violations or security incidents, and how can we proactively address these correlations?
  Through detailed logging and analysis, observability insights can reveal if certain database changes are more likely to lead to compliance or security issues. 
  Analysis can also surface patterns of recurring yet unintentional changes or something more threatening — a malicious actor intentionally initiating such changes. 
  This can allow teams to establish preventative measures or more stringent review processes for similar future changes.

Addressing these questions through database observability enables business intelligence, data science, and analytics teams to enhance their workflows and contribute more effectively to the organization’s data-driven decision-making process. 

By ensuring high-quality, timely, and accessible data, observability supports the development of insights that can lead to competitive advantages, operational efficiencies, and improved business value.

Database observability challenges

Unlocking database observability involves dismantling data silos, integrating tools, addressing skills gaps, and fostering a strong DevOps culture of collaboration and continuous improvement. 

A viable observability initiative must aggregate data from diverse environments, ensuring comprehensive insights. Upskilling team members and locking in the skill sets needed to leverage observability tools effectively will go far in actually being able to interpret data to drive actionable insights.

Enabling observability also comes with the challenges of implementing robust guardrails, governance, and security without stifling agility and visibility. Achieving this delicate balance requires automated policy checks and role-based access controls, ensuring secure and compliant database management. 

Additionally, fostering cross-functional collaboration among database professionals, DevOps engineers, and business intelligence analysts is crucial. Addressing legacy systems and technical debt is also vital, as they can significantly hinder the integration and effectiveness of modern observability tools.

Finally, ensuring data privacy and security as observability increases is paramount. This can be achieved through stringent protection measures and regular compliance audits. 

By tackling these challenges, organizations can fully harness the benefits of database observability, leading to more agile, secure, and efficient database deployment practices that propel a business forward.

Implementing database observability

Implementing database observability requires careful consideration of both the tools and the cultural shifts within an organization.

A database change management automation solution should generally include structured logging that aligns with the need for detailed observability in database deployment workflows. It can also feature governance and security capabilities that keep the right guardrails in place while allowing for access visibility. 

Cultivating a strong DevOps culture — all the way to data store workflows — is fundamental to success. Application, DevOps, and business intelligence teams should collaborate closely, share insights, and continuously refine their workflows based on observability data. 

Best practices include regular training sessions, cross-functional team meetings to discuss insights from observability data, and integrating observability practices into daily workflows to ensure minimal disruption. 

By adopting these strategies and the right database automation and observability tools, organizations can strive for higher operational efficiency and drive a more data-informed development culture.

Charting your course to data-driven success

In this exploration of database observability, we see how it enhances DevOps practices and bolsters business intelligence strategies. By integrating observability, organizations streamline workflows, improve security, and unlock confident data-driven optimization insights. 

As you continue to extend and refine your database DevOps workflows and CI/CD pipelines, consider your current observability capability, if any, and what you could be leaving on the table if you continue without it. 

Embracing database observability is a transformational shift that positions your organization for success in an industry of rapid innovation, continuous feedback, and data-driven business value.

Learn how adopting database DevOps fuels innovation by bridging the gap between development and operations.

Edited by Jigmee Bhutia