For the last two decades, software observability tools have been in great demand in software development. Examples include Datadog, New Relic, and other tools that help software developers monitor logs, metrics, and traces to reduce downtime and improve performance.
Over time, the principle of observability went beyond software and became popular in the data management domain. That’s how data observability, a tried and trusted way to data trust, emerged as a hot trend.
Although these concepts are very different, data observability and software observability share common features.
In this article, we highlight these similarities and compare data observability to software observability.
Log monitoring is the core principle behind observability tools.
Every time an event occurs within a software or data system, the system creates a log describing the event. In particular, a log records:
Software observability solutions access log data to collect the information required to identify issues that harm or disrupt system performance.
Many companies that provide data observability tools claim their products follow a log-centered approach. In the ideal world, data observability tools provided by these companies continuously monitor log data and notify the data team about a data anomaly the moment it occurs.
However, in the real world, most data observability tools don’t monitor logs. Instead, they run costly SQL queries that access data within tables to monitor data volumes, schemas, distribution, and freshness.
Without further ado, let’s compare software observability tools and most data observability tools in detail.
Both software observability and data observability should not be confused with quality assurance testing, which implies modeling predictable situations and analyzing system performance in these modeled situations. Meanwhile, software and data observability solutions continuously monitor logs or data to detect and deal with data anomaly situations that can be challenging to predict.
Additionally, both software observability and data observability tools are built to quickly respond to problems that can grow over time and become more challenging to solve. That’s why software observability and data observability solutions are focused on detecting issues in real time. With such tools, software engineers and data teams can solve issues before they harm system performance or lead decision-makers to poor decisions.
Despite numerous similarities, software observability and data observability tools are more different than they are similar.
First, they are built for different purposes. Software observability tools help software engineers reduce application downtime and maintain high performance. Data observability tools are designed to help data teams and data engineers ensure data trust by keeping data quality and data reliability high.
Second, these two types of tools typically monitor different things. Observability systems like Datadog monitor software system logs, metrics, and traces. Meanwhile, most data observability tools access data tables to monitor data volumes, schemas, distribution, and freshness.
One more difference between the two types of tools is how they visualize dependencies to help users find the root causes of problems. While software observability tools provide software engineers with service maps, data observability tools provide data teams with data lineage, displaying upstream and downstream data flows.
Finally, the underlying difference between software observability and data observability tools is that they have completely different integration points. Software observability solutions integrate with Infrastructure as a Service (IaaS) platforms such as AWS, Azure, and Google Cloud Platform. Data observability tools integrate with data storage, orchestration, transformation, and business intelligence system tiers.
Although most data observability tools run SQL queries to retrieve information on data volumes, schemas, distribution, and freshness, log-focused data observability is an ideal scenario.
The main benefits of a log-focused approach compared to more traditional data observability methods are:
If you find the benefits we have mentioned above attractive, try Masthead! We keep your data quality and reliability high without compromising your security or increasing your cloud costs with SQL queries. And Masthead provides you with real-time notifications on data quality so you can ensure no data errors reach end users and impact their decisions.
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BigData, Data Culture, Data Engineering Practices, Data Quality