Standalone vs. Embedded Tools for Data Observability: Choosing the Right Approach

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. 

This blog post explores both of these options, helping you understand their strengths and weaknesses so you can make an informed choice for your organization. 

Understanding Data Observability

What is data observability?

Data observability refers to the ability of an organization to see and understand the state of their data at all times. By “state” we mean things like: where is it coming from and going within our pipelines, is it moving on time and with the volume we expect, is the quality high enough for our use cases, and is it behaving normally or did it change recently?

• Here are some questions you could answer with data observability:
• Is the customer’s table getting fresh data on time or is it delayed?
• Do we have any duplicated shopping cart transactions and how many?
• Was the huge decrease in average purchase size just a data problem or a real thing?
• Will I be impacting anyone if I delete this table from our data warehouse?

Observability platforms aim to give a continuous and comprehensive view into the state of data moving through data pipelines, so questions like these can be easily answered.

Common data observability activities include: monitoring the operational health of the data to ensure it’s fresh and complete, detecting and surfacing anomalies that could indicate data accuracy issues, mapping data lineage to upstream tables to quickly identify the root causes of problems, and mapping lineage downstream to analytics and machine learning applications to understand the impacts of problems.

Once data teams unlock these activities, they can systematically understand when, where, and why data quality problems occur in their pipelines. They can then prevent those problems from impacting the business, and work to prevent them occurring in the future!

Data observability unlocks these basic activities, so it’s the first stepping stone toward every organization’s ultimate data wishlist: healthier pipelines, data teams with more free time, more accurate information, and happier customers.  

Why is data observability important?

Organizations push relentlessly to better use their data for strategic decision making, user experience, and efficient operations. All of those use cases assume that the data they run on is reliable.

The reality is that all data pipelines will experience failures. It’s not a question of if, but when, and how often. What the data team can control is how often issues tend to occur, how big the impact, and how stressed out they are when resolving these failures.

A data team that lacks this control will lose the trust of their organization, therefore limiting organizational willingness to invest in things like analytics, machine learning, and automation. On the other hand, a data team who consistently delivers reliable data can win the trust of their organization, and fully leverage data to drive the business forward.

Data observability is important because it is the first step toward having the level of control needed to ensure reliable data pipelines that win the trust of the organization and ultimately unlock more value from the data.

What are the benefits of data observability?

What do you get once you have total observability over your data pipelines? The bottom line is that the data team can ensure that data reaching the business is fresh, high quality, and reliable—which unlocks trust in the data.

Let’s break down the tangible benefits of data observability a little further:

Decreased impacts from data issues—when problems do occur, they’ll be understood and resolved faster; ideally before they reach a single stakeholder. Data outages will always be a risk, but with observability, their impacts are greatly reduced.

Less firefighting for the data team—you’ll spend less time firefighting data outages, and being reactive. That means more time building things, creating automation, and the other fun parts of data engineering and data science.

Increased trust in the data by stakeholders—once they stop seeing questionable data in their analytics, and stop hearing about ML model issues, they’ll start trusting the data and assuming it’s good for making decisions with or integrating into their products and services.

Increased investment in data from the business—once stakeholders can trust the data, they can feel comfortable using data in more places across the business, which means allowing a bigger budget on data and the data team.

With a clear understanding of what data observability is and why it's crucial, let's explore how to choose between standalone and embedded tools for implementing a data observability strategy.

Making the Right Choice

The decision between standalone and embedded tools for data observability hinges on various factors, including your organization's size, existing infrastructure, budget, and specific observability needs.

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.

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.

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. The evolution of embedded observability features within data platforms will gradually reduce the burden on standalone tools, making them more efficient and integrated over time.

Ultimately, the right choice for your organization will depend on your current and future requirements, the complexity of your data environment, and the specific benefits each type of tool can offer.

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.