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?

1. https://firsteigen.com/
What Is Data
Observability for Data
Pipelines?
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.
Quick Takeaways
 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?

2. https://firsteigen.com/
Image Source
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?

3. https://firsteigen.com/
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?

4. https://firsteigen.com/
 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

5. https://firsteigen.com/
 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

6. https://firsteigen.com/
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.
Quick Takeaways
 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?
Image Source
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

7. https://firsteigen.com/
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.

8. https://firsteigen.com/
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.

9. https://firsteigen.com/
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.

10. https://firsteigen.com/
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.