The goal of data monitoring is to provide insights to assure high levels of data quality. As a result, the market is flush with data monitoring solutions which do, in fact, provide some degree of insights, but are incapable of operating at the scale required by today’s modern data stacks.
As data teams increasingly focus on building cloud-centric data infrastructures, data quality monitoring tools have rapidly become outdated. They were designed for an earlier generation of application environments, and are unable to scale, they are too labor-intensive to manage, too slow at diagnosing and fixing the root causes of data quality problems, and hopeless at preventing future ones.
With real-time data insight expectations, alerts are too late, and slow is the new down. This era’s solution is data observability, which takes a whole new proactive approach to solving data quality that goes far beyond simple data monitoring and alerts, reducing the complexity and cost of ensuring data reliability.
Data monitoring refers to the process of continuously monitoring the data flow and performance of a system to ensure that it meets the desired specifications and SLAs. It typically involves setting thresholds and alerts to notify the team of any issues, such as a bottleneck or a data loss.
Data observability, on the other hand, is the ability to understand the internal state of a system by collecting and analyzing data from various sources. This includes metrics, traces, and logs, as well as the ability to access and query this data in real-time.
In addition to providing comprehensive monitoring, an enterprise data observability platform makes sure to monitor data, data systems, and data quality from every potential angle, rather than giving short shrift to any key facet. Moreover, data observability assumes data is in motion, not static. So it continuously discovers and profiles your data wherever it resides or through whichever data pipeline it is traveling, preventing data silos and detecting early signals of degrading data quality. Finally, data observability platforms use machine learning to combine and analyze all of these sources of historical and current metadata around your data quality.
With data observability, it's possible to track data as it flows through data pipelines and identify any issues or inconsistencies that may be affecting data quality. This makes it easier to pinpoint the source of any problems and take appropriate action to fix them, whereas data monitoring only gives threshold-based alerts which might be too late for recovery.
Data observability provides a more comprehensive view of data systems and can scale to address diversity such as hybrid and multi-cloud environments. This allows for more detailed analysis and understanding of the data and how it is being used as enterprises seek high-quality data to build essential data products.
The key to developing these data products is to have data that’s available and actionable, and that can only occur when the data can be trusted for accuracy and quality. Data observability provides this through a variety of features, including:
Data observability is critical for optimizing cloud-based data stacks, as it can help identify and address issues that may be impacting performance and efficiency. Despite the rapid adoption of cloud-native data stacks and data platforms, many vendors skipped adding observability capabilities. Some specific ways that data observability can be better than data monitoring in this context include:
Data observability provides a more comprehensive view of the cloud-based data stack, which can help identify and address issues that may be impacting performance and efficiency in ways that traditional data monitoring alone can't.