When implementing data observability tools, organizations face a critical decision: should they opt for standalone tools or embedded solutions? Both approaches offer unique benefits and challenges, impacting everything from implementation speed to long-term scalability.

Standalone tools are dedicated solutions specifically designed for data observability. They operate independently of other systems and often provide a robust set of features tailored for monitoring, analyzing, and managing data systems. For example, Bigeye is a standalone data observability tool.

On the other hand, embedded tools are integrated within existing data platforms or analytics tools. They offer observability features as part of a broader data management or analytics suite, enhancing the capabilities of platforms like Snowflake or Databricks. These tools are designed to provide observability within the existing infrastructure, reducing complexity and setup time.

Standalone Tools for Data Observability

Standalone tools are dedicated solutions specifically designed for data observability. These tools offer robust features tailored to monitor, analyze, and manage data systems independently of other platforms.

Here are some of the main pros and cons to consider:

                       PROS                                                         CONS

Embedded Tools for Data Observability

Embedded tools are integrated within existing data platforms or analytics tools. These solutions offer observability features as part of a broader data management or analytics suite.

                 PROS                                                           CONS

Future Trends

We anticipate that in the future, most data tools will incorporate embedded observability features. Standalone tools will evolve to read and aggregate information from these embedded solutions. Currently, standalone tools must independently gather observability data, which requires significant engineering effort. However, as platforms like Snowflake and Databricks develop their own observability features, standalone tools will benefit by consuming this readily available information. Over time, standalone tools will become aggregators, providing a holistic view of your data at a glance.

This shift in the market is likely to take several more years. In the meantime, your choice should balance immediate needs with long-term strategic goals, ensuring that your data observability strategy supports your organization's growth and operational efficiency.

Conclusion

Both standalone and embedded tools for data observability have their place in the modern data landscape. By carefully evaluating the pros and cons of each approach, you can select the solution that best aligns with your organizational needs and resources. Whether you prioritize specialized features and scalability or ease of integration and cost-effectiveness, the right tool will enhance your data observability efforts, ensuring reliable, high-quality data for your business operations.

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ABOUT THE AUTHOR

Kyle Kirwan is the co-founder and CEO of Bigeye, a provider of data observability tools.  In his career, Kirwan was one of the first analysts at Uber. There, he launched the company's data catalog, Databook, as well as other tooling used by thousands of their internal data users. He then went on to co-found Bigeye, a Sequoia-backed startup that works on data observability. You can reach Kyle on Twitter at @kylejameskirwan or on LinkedIn.