How to Prevent Broken Data Pipelines with Data Observability
By: Barr Moses
Tags: Data Analysis, Data Quality, Data, Data Engineering, Data Science

How to Prevent Broken Data Pipelines with Data Observability

And other important lessons for data teams

Image courtesy of Amarnath Tade on Unsplash.

If you work in data, these questions are probably a common occurrence:

“What happened to my dashboard?” “Why is that table missing?” “Who in the world changed the file type from CVS to XLS?!”

And these just scratch the surface. As the number of data sources and complexity of data pipelines increase, data issues are an all-too-common reality, distracting data engineers, data scientists, and data analysts from working on projects that actually move the needle.

In fact, companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and 1 in 5 companies have lost a customer due to incomplete or inaccurate data.

So, how do you prevent broken data pipelines and eliminate downtime? The answer lies in traditional approaches to reliable software engineering.

Introducing Data Observability

Developer Operations teams have become an integral component of most engineering organizations. DevOps teams remove silos between software developers and IT, facilitating the seamless and reliable release of software to production.

Observability, a more recent addition to the engineering lexicon, speaks to this need, and refers to the monitoring, tracking, and triaging of incidents to prevent downtime. In the same way that New Relic, DataDog, and other Application Performance Management (APM) solutions ensure reliable software and keep application downtime at bay, Data Observability solves the costly problem of unreliable data.

Instead of putting together a holistic approach to address data downtime, teams often tackle data quality and lineage problems on an ad hoc basis. Much in the same way DevOps applies observability to software, I think it’s about time we leveraged this same blanket of diligence for data.

Data Observability, an organization’s ability to fully understand the health of the data in their system, eliminates data downtime by applying best practices of DevOps Observability to data pipelines. Like its DevOps counterpart, Data Observability uses automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, leading to healthier pipelines, more productive teams, and happier customers.

To make it easy, I’ve broken down Data Observability into its own five pillars: freshness, distribution, volume, schema, and lineage. Together, these components provide valuable insight into the quality and reliability of your data.

Image courtesy of Barr Moses.

A robust and holistic approach to data observability requires the consistent and reliable monitoring of these five pillars through a centralized interface that serves as a central source of truth about the health of your data.

Data Observability provides an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, using machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster.

Data observability facilitates greater collaboration within data teams by making it easy to identify and resolve issues as they arise, not several hours down the road. Image courtesy of Barr Moses.

Such an approach to data quality and reliability uniquely delivers:

End-to-end observability into all of your data assets. A strong Data Observability solution will connect to your existing data stack, providing visibility into the health of your cloud warehouses, lakes, ETL, and business intelligence tools.

A strong Data Observability solution will connect to your existing data stack, providing visibility into the health of your cloud warehouses, lakes, ETL, and business intelligence tools. ML-powered incident monitoring and resolution. It automatically learns about data environments using historical patterns and intelligently monitors for abnormal behavior, triggering alerts when pipelines break or anomalies emerge. No configuration or threshold setting required.

It automatically learns about data environments using historical patterns and intelligently monitors for abnormal behavior, triggering alerts when pipelines break or anomalies emerge. No configuration or threshold setting required. Security-first architecture that scales with your stack. Implicitly, Data Observability intelligently maps your company’s data assets while at-rest without requiring the extraction of data from your environment and scalability to any data size.

Implicitly, Data Observability intelligently maps your company’s data assets while at-rest without requiring the extraction of data from your environment and scalability to any data size. Automated data catalog and metadata management. Real-time lineage and centralized data cataloguing provide a single pane-of-glass view that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements unlike manual catalogs.

Real-time lineage and centralized data cataloguing provide a single pane-of-glass view that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements unlike manual catalogs. No-code onboarding. Code-free implementation for out-of-the-box coverage with your existing data stack and seamless collaboration with your teammates.

In the same way that software engineering teams shouldn’t have to settle for buggy code, data engineering teams don’t have to settle for broken data pipelines. By applying the same principles of software application observability and reliability to data, these issues can be identified, resolved and even prevented, giving data teams confidence in their data to deliver valuable insights.

As companies continue to move to the cloud, embrace more distributed data stacks (see: data mesh) and increasingly rely on AI to power previously manual functions (i.e., metadata management), I expect that data will increasingly rely on best practices of DevOps and software engineering to accommodate the growing data needs of the enterprise.

I don’t know about you, but I’m looking forward to a world in which the “why, how, who, and where?” of your data is much easier to answer.

To learn more about data observability, reach out to Barr Moses and the Monte Carlo team.