How often have you wished you had a crystal ball that could reveal what was happening inside your company’s data? What if you could see not just what happened in the past but also what is happening right now? And what if you could do all of this without needing to be a data scientist?
Data observability is the answer to these wishes. It is a term used to describe the practice of monitoring and understanding data as it flows through an organization. This can be done by tracking events, analyzing changes, and looking for patterns.
Data observability is important for businesses because it helps them to understand what is happening with their data. This understanding can then be used to make better decisions about how to manage and use data.
Keep reading as we explore what is data observability. how it works, and why businesses need it.
Data Observability: An Overview
Suppose you are the owner of a small business. You have a website where customers can purchase your products. You also have a customer relationship management (CRM) system to track sales and customer information.
Now, imagine that you want to know how many visitors come to your website, what pages they visit, and how long they stay on each page. You also want to know how many sales you make and what types of products are selling well. To get this information, you could log into your website analytics dashboard and CRM system.
But what if there was an easier way to get all of this information in one place? This is where data observability comes in.
Data observability is the act of monitoring, understanding, and management of data flows within a system. It helps businesses to optimize performance, troubleshoots issues, and resolve problems quickly.
What are the 5 Pillars of Data Observability?
There are five key pillars of data observability:
- Data collection: Collect data from all parts of your system, including applications, databases, servers, and storage systems.
- Data processing: Process the data to extract valuable information. This step may involve filtering, aggregating, or transforming the data.
- Data analysis: Analyze the processed data to identify trends, patterns, and insights.
- Data visualization: Visualize the data in a format that is easy to understand, such as charts or graphs.
- Data management: Manage the data to ensure it is accurate, complete, and accessible. This may involve storing the data in a central repository or creating
How do Data Catalog Services Come into the Picture?
Data catalog services help make data observability possible. A data catalog is a system that stores metadata about data assets. This metadata can include information such as the name of the asset, its location, who created it, when it was last updated, and so on.
Data catalog services also provide tools for managing, understanding, and using this metadata. These tools can be used to search for specific data assets, understand how they are related to each other, and analyze their contents.
Some data catalog services also provide features for collaboration, such as commenting and tagging. This makes it easier for team members to work together on projects and to share knowledge about data assets.
Data catalog services help businesses to achieve data observability by providing a centralized platform for managing and accessing data. Data catalogs provide a single point of access for all data within an organization, making it easy for businesses to find and use the data they need.
The Bottom Line
Given that data is becoming increasingly important to businesses, data observability is a critical capability. Data observability allows businesses to monitor and understand their data flows, so they can optimize performance, troubleshoot issues, and resolve problems quickly.
This blog is originally published on TechAnnouncer.
