Open Source Data Observability Tools: When Free Isn’t Always Better

Open source software is a democratizing force, lowering the barrier to entry for those looking to take advantage of and contribute to software. The data world has certainly benefited tremendously from open source solutions like Spark, dbt, and Airflow, but when it comes to data quality, it’s important to assess the pros and cons of open source data observability tools.

But it’s clear open source software is not always the best solution for every component of a modern data platform. In some cases, commercial B2B SaaS can offer better features, support, and security. That’s why data teams need to carefully evaluate their build vs. buy decisions.

Benefits of using open source data observability tools

First and foremost, open source tools are free. This can be a major advantage for organizations that are on a tight budget.

Second, open source tools are often more customizable than commercial solutions. The source code is available for anyone to view and modify. This means if your team has the skills and time, you can tailor the tool to your specific needs.

Limitations and challenges of open source solutions

However, open source solutions also have limitations and challenges. This is especially true when it comes to data observability. Data observability is the practice of monitoring data and data pipelines to identify and fix problems before they impact users. It is an essential tool for any organization that relies on data to make decisions.

There are a number of open source data observability tools available, although the most commonly referenced would more accurately be categorized as programmatic data testing.  

Open source data observability tools have failed to gain traction in the market largely because they lack the features, support, and security of more robust data observability platforms. Let’s look at each in turn.

Open source data observability tools lack key features

The core of data observability solutions are a variety of machine learning monitors and models that can help detect and resolve anomalies. While building basic versions of a data pipeline monitor in SQL is not overly complex, doing so with a minimum of false positives at scale requires:

• Significant data science talent 
• A feedback loop from a diverse range of organizations
• Robust infrastructure

This is why open source solutions like Great Expectations rely on scaling static data tests rather than more sophisticated machine learning models. This has major implications for data teams as a data testing led data quality strategy will not have as robust coverage, lack the context required for root cause analysis, and will require more time spent from the data engineering team. 

Let’s also not forget time to value, which was a key requirement for Swimply head of data Michael Sheldon who told us, “…what it enabled us to do is right away, especially during our peak season, [was] leverage that data for insights. The speed of being able to get up and running, and know that the quality is there, really enabled us to move extremely quickly—at a time and place where we really needed to do so.” 

Open source data observability tool support issues

In addition, open source tools are often not as well-supported as commercial solutions. This means that if you encounter a problem with an open source tool, you may have to wait longer for a fix. Open source projects are often run by volunteers who don’t have time to respond to every bug report.

The challenge with open source data observability solutions specifically, is that this is a technology that needs to integrate with dozens of solutions across the data stack to ensure both quick time-to-detection and time-to-resolution. Keeping these integrations seamless is difficult work as each solution is issuing new releases at a rapid rate. 

This was a decisive factor in why Choozle CEO Adam Woods chose a commercial data observability solution saying, “I understand the instinct to turn to open source, but I actually have a lower cost of ownership with a tool like Monte Carlo because the management burden is so low and the ecosystem works so well together….I love that with Snowflake and Monte Carlo my data stack is always up-to-date and I never have to apply a patch. We are able to reinvest the time developers and database analysts would have spent worrying about updates and infrastructure into building exceptional customer experiences.”

Finally, it’s important to consider data observability is a new technology category. Most data teams, regardless of the platform they choose, will need to develop new operational muscles to respond to flagged anomalies. Customer success teams are a crucial component to helping these teams adopt best practices developed by similar organizations.

Open source data observability tool security concerns

Data is one of the most valuable assets an organization has, and it is often tightly regulated. In other words, most organizations rightly have stringent security requirements about how their data is handled.

This could make adopting an open source solution challenging if it does not have the ability to meet the requirements of SOC 2 compliance.

And while the vast majority of open source solutions prove safe to use, it is nonetheless important to note that they can and have been exploited in the past by malicious actors. 

An infamous example of this is the Heartbleed bug in OpenSSL, a widely used open-source library for implementing HTTPS. This bug was exploited to steal protected information, and the open source nature of OpenSSL made it easier for attackers to understand and exploit the vulnerability.

Data observability with Monte Carlo

For these reasons, it is important to carefully consider the pros and cons of open source data observability tools before making a decision. If you need a tool that offers the best features, support, and security, then a commercial solution like Monte Carlo may be the better option.

With Monte Carlo, your data team can analyze data at-rest, deploy ML-powered anomaly detection to automatically detect incidents, integrate tools to quickly resolve issues, and ultimately - finally -  trust the data.

Learn why the data teams at Fox, JetBlue, and more leading companies trust Monte Carlo with their data reliability needs by requesting a demo.

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