Data Observability What, Why, How, Pillars

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What is Data Observability?

Data observability defines the health of data in the organization and mostly eliminates the data downtimes by appealing best practices of DevOps observability to data pipelines. It covers all the base level monitoring fundamentals that help in Governing the data at the Top-level.
In other words, "To see and let the problem passing is not Data Observability, to understand the root causes and steps to fix defines Data Observability."

Background of Data Observability

Data Engineering and DevOps teams try to visualize the problems by first selecting the metrics, then defining the role of a metric to monitor, and then going back to the implementation to follow up on the issues that occurred during monitoring or by the consumer of the applications, i.e., customer, client, software, to name a few.


Workflow of Data Observability

Observability is the practice of accomplishing actionable insights from data generated by instrumented IT and software systems. The aim is to understand both when an event or issue happened and why. The concept isn't new – it comes from the control theory introduced by Rudolf Kalman in linear dynamic systems. It is defined as a measure of how skillfully the internal states of a system can be concluded from external outputs. It is no surprise that observability is trending in the DevOps and SRE communities. In today's complex infrastructure, it is hard to easily observe and know what's happening, where it is happening, and why.

This is unquestionably an incomplete list, but given that the application of observability is so big to seize, we thought it may be helpful to provide a sampling of possible questions that an effective observability solution can convey:

  1. Why is x broken?

  2. What went wrong during this release?

  3. Why did my pager go off?

  4. Why did the performance degrade over the past quarter?

  5. What did my service look like at time point x?

  6. What changed? Why?

  7. What logs should we look at right now?

  8. Are we out of SLO?

  9. Should we roll back this canary?

  10. What SLO should we set?

  11. What was the relationship of attributes across the system before we deployed? What's it like now?

  12. What is most likely giving to latency now or not?

  13. Are these performance optimizations on the critical path?

Why do we need Data Observability?

Data and analytics teams would be flying blind if they didn't have insight into data pipelines and infrastructures, i.e., they wouldn't be able to fully comprehend the pipeline's health or what was happening between data inputs and outputs. The inability to grasp what's going on across the data lifecycle has several drawbacks for data teams and organizations.

Organizations' data teams grow in size and specialization as they become more data-driven. Complex data pipelines and systems are more prone to break in such settings due to a lack of coordination, miscommunication, or concurrent changes made by team members. Data engineers don't always get to work on revenue-generating tasks because they're continuously resolving one data or pipeline issue or attempting to figure out why a business dashboard looks out of whack. I understand that this can be a pain in the neck at times.

How does Data Observability impacts Productivity?

Data Observability abolishes the need for debugging in a respective deployment environment with monitoring the performance of applications. Data Observability also helps in identifying the root causes of issues and helps in troubleshooting. Observability helps in enhancing the data and provides information fast.

Importance of Data Observability

These are the following reasons why data Observability matters:

  1. Data Observability help improve the development application environment with information to enhance it.

  2. It helps understand behind the scenes for any system or IT issues.

  3. Unknown catching issues tracking assists in adopting the changes to manage them.

  4. Smart analysis of development helps DevOps Cycle reduction.

  5. Helps Engineering and DevOps team understand what is going on in the development phase.

  6. Root cause identification and troubleshooting time are reduced.

  7. Allows self-healing infrastructure, also known as IT automation.

Myths about Observability

Observability and monitoring are two different concepts. They are often confused about the same thing. While observability is besotted by DevOps & IT Ops teams, monitoring is the NetOps team's backbone. Observability Platforms and monitoring tools have a lot in common, such as:

  1. Problems Detection: Alarms, monitoring charts

  2. Problem Resolution: FAQs

  3. Continuous Improvement: Reporting & documenting

Five pillars of Data Observability

Pillar of data observability are mentioned below:

Freshness

Freshness attempts to determine how current your data tables are, as well as the frequency with which they are updated. When it comes to making decisions, freshness is especially vital; after all, old data is practically associated with squandered time and money.

Distribution

The distribution of your data's possible values, in other words, informs you if your data is inside an acceptable range. Depending on the data you have, data distribution helps you to assess whether or not your tables can be trusted.

Volume

The size of your data tables is a measure of their completeness, as well as information on the health of your data sources. If the number of rows drops from 200 million to 5 million, you should be aware.

Schema

Broken data is frequently indicated by changes in the arrangement of your data, or schema. Tracking who makes updates to these tables and when is crucial for understanding the health of your data environment.

Lineage

When data goes wrong, the first inquiry is always, "Where did it go wrong?" Data lineage tells you whose upstream sources and downstream consumers were affected, as well as which teams are producing the data and who is accessing it. Good lineage also gathers data-related information (also known as metadata) that pertains to governance, business, and technical rules for specific data tables, acting as a single source of truth for all users.

Observability Benefits by role

Here are observability benefits by role:

Developers

  1. Stress to deploy the code or make any changes reduced with Observability.

  2. Easy to roll back and fix the customer-affecting issues.

  3. Better Hypotheses to test and investigate

Teams

  1. The same information can be available as a Shared view.

  2. Real-Time metrics helps teams in spending less time transferring the information

Businesses

  1. Easy to manage and deploy the code [faster release engineering]

  2. Cost-saving in terms of human resources spending less time to find and fix the errors

  3. More confident releases of the product make consumers happy with faster and more responsive systems.


Click to explore Adopt or not to Adopt Data Mesh? - A Crucial Question



How is Data Observability different from Data Monitoring?

A visual system helps understand and measure the architecture details to navigate from the happenings to the root cause. It also has the fix for complex microservice architecture. Monitoring is what and how you do after a system is observable. Without some level of observability, monitoring can't be done or impossible.

Observability and monitoring enriched each other, with each one serving a different purpose. Monitoring tells you when something goes wrong, while observability enables you to understand why this happened. We can say monitoring is a subset of necessary action for observability. You can only monitor an observable system.


Conclusion

Thanks to DevOps, we have an easy sight to view the importance of observability as-applied data. By eliminating data downtime incidents as soon as they arise, we know what observability and monitoring are and how they complement each other.
Read more about What is Data Quality?

Click to explore What is a Data Pipeline?

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