The cloud presents businesses with increased opportunities to make the most out of their data. In the cloud, there is more data coming in from more sources, and businesses can leverage that data to fuel innovation. Businesses need to prioritize building observability into data pipelines in order to leverage their data.

Effective observability of data pipelines ensures the processes providing valuable data are in good health and are running properly, thereby playing a role in business continuity and competitive advantage. For that reason, emphasis must be placed on whether the processes are producing quality data in a timely manner for the consumers relying on it for business decisions.

In today’s cloud-based world, businesses strive to extract maximum value from their data quickly and easily, therefore making observability critical to data-driven decision making.

Cloud changed the need for observability

Because cloud infrastructure scales based on need, robust observability is necessary. Resilient data processes must be built to track the data because this scalability also increases the likelihood that a process might fail along the way. Cloud-based businesses now need to think about building observability for failure.

Cloud computing has also introduced the ability to align data processes more closely with the actual data – a transformation largely due to the increased compute available in the cloud. As data processing within cloud data platforms increases, the need for effective observability solutions to monitor these processes becomes more pressing.

The cloud has also played a role in the evolving nature of data spaces, often leading to new challenges for data analysts and engineers. Sandbox environments, which were typically used for testing, have become commonplace. This has led to an explosion of new data processes that require additional monitoring and observability.

The creation of more production spaces has also heightened the need for strict process and access management protocols. The dynamic nature of the cloud requires robust, automated governance so that only authorized users have access to sensitive data. As data moves from pipeline to production, each step needs to be monitored and controlled to prevent errors and ensure the integrity of the data, emphasizing the need for effective observability solutions.

Observability is critical for organizations

Data observability ensures visibility into the performance and health of data processes. It’s the ability to track, monitor and understand the state of data processing workflows and pipelines in near real-time. Observability ensures that the processes running to serve business needs are operating optimally, generating the insights necessary for informed decisions. This benefits different stakeholders in an organization, from data analysts and engineers to the end consumers of data-driven insights.

  • Analysts: Observability empowers data analysts by placing the power of data quality in their hands. It allows them to build their own data pipelines and machine learning models. By monitoring their creations, they can ensure these processes are working correctly and delivering valuable insights. In this environment, they are not merely passive consumers of data, but rather active participants in the data lifecycle, creating an ecosystem where data-driven decision-making thrives.
  • Engineers: Data engineers benefit from data observability by monitoring infrastructure robustness and reliability. Observability tools provide them with real-time insights into the system, helping them quickly identify and address issues before they escalate. In the cloud, infrastructure scales based on needs, so observability is critical for engineers to build processes that are resilient.
  • Consumers: Proper observability impacts the end consumers of the data—the business decision-makers. Reliable, accurate and timely data is critical for making informed business decisions. Data observability ensures that the insights generated by data processes are trustworthy and available when needed, fostering confidence in the data and the decisions made from it.

Building observability for the cloud

Capital One Software, an enterprise B2B software business of Capital One, designed and built its own monitoring and observability solution for Capital One Slingshot, a SaaS product that helps businesses maximize their Snowflake investment. Preserving the quality and performance of Snowflake data pipelines in Slingshot required a custom-built observability solution.

Using fundamental resources provided by Snowflake, the observability solution was built based on a three-step approach: detect, notify and act. Monitoring activities measure the overall performance of the data processes running in Snowflake and detect abnormalities. Once an error is detected, all impacted stakeholders are informed immediately. The “act” piece varies based on the situation, but timely notifications are critical to rectifying a situation quickly. This anticipatory approach to data monitoring allows Capital One Software to maintain smooth operation of its data processes running in Snowflake, thus minimizing potential interruptions that could hinder downstream processing.

Organizations that want to ensure observability is in place for their own cloud-based data pipelines should focus on:

  • Define standards for monitoring and alerting: Develop baseline standards used across all teams and encourage further customization based on specific needs. Also, apply overarching monitors that apply to all teams (i.e., a common process failure or unauthorized access to the system).
  • Anticipate and prepare for failure: Given the volatile nature of the cloud, businesses need to design robust and resilient processes. Ensure that efficient alert systems will notify relevant parties across preferred channels in the event of a failure. This needs to be done in a timely manner.
  • Automate, but don’t forget the human element: Automate common remedial actions to aid with quick resolution. However, prepare for the potential situations where human intervention is still necessary. For example, depending on the process, it might be an analyst that resolves an issue as it arises or even a product support team if it serves a larger community of analysts.

As the data ecosystem evolves, the need for more robust, customizable monitoring and alerting solutions will only increase. Businesses should invest in solutions that meet their unique needs to ensure data processes are delivering reliable, timely and actionable insights. In the world of data-driven business, organizations cannot afford to ‘fly blind.’ It’s critical to detect, notify and act swiftly to ensure business continuity and decision-making efficacy in the face of ever-increasing data complexity.