Data observability is the big buzzword these days, but do you know what it is or what it does? In particular, do you know why data observability is important for data pipelines?
You use a data pipeline to move data into and through your organization. You use data observability to ensure that your data pipeline is working as effectively and efficiently as possible. They are two synergistic concepts working together to deliver high-quality data to the people in your organization who need it.
- A data pipeline moves data from various sources to the end user for consumption and analysis
- Data observability monitors the health of the data pipeline to ensure higher-quality data
- Data observability manages data of different types from different sources
- Data observability improves system performance
- Data observability provides more useful data to end users
What Is a Data Pipeline?
The world runs on data. According to current estimates, the average person creates 2.5 quintillion bytes of data every day—and a lot of that data flows into your company to use.
The flow of data into and through your organization is your data pipeline. Raw data enters your pipeline from various sources and transforms into structured data you can use for operations and analysis. The transformation and delivery of that data involve multiple processes, all part of the pipeline.
Unfortunately, data doesn’t always flow smoothly through the pipeline. Data ingested is often rife with errors and inaccuracies. Flaws in the pipeline itself can compromise even the cleanest data. For example, a pipeline can drop data when it gets out of sync, resulting in data leaks.
How can you ensure that your data pipeline does more good than harm and delivers the highest possible quality data? That’s where data observability comes in.
What Is Data Observability?
Did you know that poor-quality data can cost your organization between 10% and 30% of its revenue? It’s a potential problem so large you can’t ignore it—which is where data observability comes in.
Data managers and engineers use data observability to make all the parts of the data system more visible. Unlike traditional data monitoring, which is concerned with improving the quality of data flowing through the system, data observability is concerned with the quality of the overall system. Data observability creates better systems that, indirectly, result in higher-quality data.
The 360-degree data view provided by data observability exposes potential issues affecting data quality. By monitoring the data flow in real-time, data observability can predict and plan for increased data loads, eliminating potential bottlenecks.
Data observability builds on the following five pillars:
- Freshness. Is the data as current as possible?
- Distribution. Does the data fall within an acceptable range?
- Volume. Are the data records complete?
- Schema. How is the data pipeline organized?
- Lineage. What is the status of the data as it flows through the pipeline? How Does Data Observability Work with a Data Pipeline?
Building on these five pillars, data observability can determine how effectively and efficiently a data pipeline works. It can also identify areas that aren’t working as well as others and propose solutions to improve pipeline quality and performance. By enhancing the pipeline itself, data observability improves the quality of the data flowing out of the pipeline.
How Does Data Observability Work With Your Data Pipeline?
Think of data observability as a way to monitor the performance of your data pipeline. It works across the entire pipeline from beginning to end.
On the Front End
Data observability monitors and manages data health across multiple data sources at the beginning of the pipeline. Data observability allows you to ingest all structured and unstructured data types without affecting data quality.
One way data observability handles disparate data types is by standardizing that data. Data observability works with data quality management tools to identify poor-quality data, clean and fix inaccurate data, and convert unstructured data into a standard format that’s easier for your system to use.
Throughout the Pipeline
Throughout the entire pipeline, data observability monitors system performance in real time. Data observability tracks all aspects of your system performance, including:
- Memory usage
- CPU performance
- Storage capacity
- Data flow
By closely tracking data as it flows through the pipeline, data observability can identify, deter, and resolve any data-related issues that may develop. This helps to maximize system performance, which is essential when your system is ingesting and moving large volumes of data that can slow down more traditional systems.
Data observability tracks and compares large numbers of pipeline events and identifies significant inconsistencies. Focusing on these variances helps data managers identify flaws in the system that might impact the flow and quality of data in the pipeline. You can identify potential issues before they become debilitating problems, keeping the pipeline open and avoiding costly downtime.
On the Back End
Most users interact with your organization’s data at the end of the pipeline. Data observability creates a system that ensures clean and accurate data from which your users can gain the most value and insights.
In addition, data observability uses artificial intelligence (AI) and machine learning (ML) to track current system usage, redistribute workloads, and predict future usage trends. This helps you manage data resources, plan for future needs, and control IT costs. Data keeps flowing, no matter what, thanks to data observability.
Create a More Efficient Data Pipeline with Data Observability—and DataBuck.
Data observability improves your organization’s data flow and increases productivity. Data observability gives you a pipeline that provides more usable and higher-quality data.
You can enhance data observability for your data pipeline with DataBuck from FirstEigen. DataBuck is an autonomous data quality management solution powered by AI/ML technology that automates more than 70% of the data monitoring process. It can automatically validate thousands of data sets in just a few clicks and constantly monitor data ingested into and flowing through your data pipeline. Include DataBuck as part of your data observability and create a true data trustability solution.
Contact FirstEigen today to learn more about using data observability for your data pipeline.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)