What is the Difference Between Data Observability and Data Monitoring?

Conventional wisdom says that data monitoring provides 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. 

With the shift towards distributed, cloud-centric data infrastructures, data quality monitoring tools have rapidly become outdated.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 observability vs. data monitoring

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.

How does data observability deliver better data insights than data monitoring?

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:

  1. Access to more data: With data observability, it's possible to collect and analyze data from a wide range of sources, such as metrics, traces, and logs. This gives a more complete picture of the system and its data, making it easier to identify patterns and trends that may not be visible with just a subset of data.
  2. Real-time insights: Data observability allows for real-time access and querying of the data, which means that issues and inconsistencies can be identified and addressed more quickly. This can be especially important in cases where data quality or accuracy is critical.
  3. Root cause analysis: Data observability makes it possible to track data as it flows through the system and identify the source of any issues or inconsistencies. This enables more accurate root cause analysis and helps to prevent similar issues from happening in the future.
  4. Correlation of events: Data observability allows for correlation of different events happening in the system, which can help identify patterns, uncover hidden relationships, and reveal the impact of changes on the system.

How data observability optimizes cloud-based data stacks

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:

  • Identifying and addressing bottlenecks: Data observability allows you to track and analyze data as it flows through the cloud-based data stack, which can help identify bottlenecks or other issues that may be impacting performance. This information can be used to make adjustments and optimize the stack to improve efficiency.
  • Optimizing resource usage: Data observability can provide insights into how different resources are being used in the cloud-based data stack, such as CPU, memory, and storage. This can help identify areas where resources are being wasted or over-allocated, and make adjustments to optimize usage and reduce costs.
  • Identifying and addressing data quality issues: Data observability allows you to track and analyze the data in your cloud-based data stack, which can help identify any issues or inconsistencies that may be impacting data quality. This can be especially useful in cases where data accuracy is critical.
  • Understanding and optimizing query performance: Data observability can provide detailed information about query performance and usage, which can help identify and address any issues that may be impacting efficiency. This can include identifying slow-performing queries, optimizing indexing, or adjusting resource allocation to improve performance.
  • Troubleshooting: In cloud-based data stack, it is more complex to trace the problem. Data observability can help to trace the problem by providing the end-to-end visibility of the data flow and can identify the root cause of the problem easily.

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.

To learn more about how enterprises can use data observability to improve data reliability and quality, check out the Acceldata Data Observability Cloud.

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