As the importance of data takes more center stage, we have more and more businesses that claim to be data-driven. As companies increase their sources of data, their data storage, pipelines, and usage tend to grow at an enormous speed. With the growth of data, the chances of inaccuracy, errors, and data downtime grow as well. As we are much aware that the decision-making of a company springs from data and the unreliability of data is a pain point for every industry today. It is difficult to make decisions based on capricious data and hence eliminating instances of downtime, bad data, missing data, and the like is going to reach new heights by prioritizing data observability.
What is Data Observability?
For data engineers, the next crucial step to effectively manage any incident detection within their data pipelines is to establish data observability. In their organizations, data engineers devote half of their time to maintaining these pipelines due to frequent disruptions and breakdowns, which hinder them from effectively constructing data-driven products. This is where Data Observability comes into the picture. Data observability refers to an organization's comprehensive awareness of the well-being and condition of the data present in its systems. Ultimately, it all boils down to the ability to closely track and oversee a pipeline of data that is observed by someone. Let’s walk through the problems that data engineers face:
Process quality
Data quality or Data integrity
Data lineage
Process Quality
First concern is if the data is moving, or the pipeline is operational. Speed in data processing could be core to the business.
Data Integrity
Once the functionality of the pipeline has been confirmed, the next step is to examine the activities occurring at the level of the data set. Imagine if data becomes vulnerable, misplaced, or corrupted. As an example, there may be a modification in the schema where we anticipate having 10 columns, but the new schema only has 9 columns. This could pose an issue as the data will have consequences for a downstream process that relies on the data set. Alternatively, if there are any modifications to the data, it will ultimately cause corruption of the subsequent data.
Data Lineage
This is about how things are connected to dependent pipelines and data sets downstream.
The essence of data observability is captured in this statement! To put it simply, Data observability refers to the process of taking action to identify incidents in the original data source, data warehouse, or downstream at the product level. This allows the data engineers team to be promptly notified whenever there is a problem. The team would have the capability to rectify and proactively address the issue, thereby ensuring that it does not affect customers further down the line and, ultimately, avoid significant and expensive consequences for the business. The principles of data observability involve promptly identifying anomalies at their origin, resolving them quickly, understanding their exact location, and predicting their impact on subsequent individuals or processes.
To proactively identify, resolve, and prevent irregularities in data, data observability tools utilize automated monitoring, root cause analysis, data lineage, and data health insights. Using this method leads to improved data pipelines, heightened team efficiency, strengthened data management strategies, and ultimately, increased customer contentment.
Salient Features of Data Observability
The purpose is to understand the essential changes in both organizational and technological perspectives to establish a data observability system that enables flexible data operations. To safeguard the practicality of data observability, it is vital to merge the following actions into its configuration.
Monitoring
A dashboard that allows a pragmatic viewpoint of your pipeline or system is referred to as monitoring.
Alerting
Notifications about predictable incidences and anomalies. Alerting permits you to detect complex conditions defined by a rule within the Logs, Infrastructure, Uptime, and APM apps. When a condition is met, the rule tracks it as an alert and responds by triggering one or more actions.
Tracking
Competence to establish and monitor specific occurrences.
Comparison
Observations made at different intervals will be compared and any abnormal alterations will be identified through alerts.
Analysis
Involuntary issue detection that regulates your pipeline and data state, referred to as analysis.
Logging
maintaining track of an occurrence using a standardized method to enable more rapid resolution.
SLA tracking
The characteristic of SLA Tracking involves measuring the cohesion of data quality and pipeline metadata to established standards.
Data Observability - a future must-have
The ability of data teams to be agile and make improvements to their products largely depends on their data observability. If there is no such system, a team's infrastructure or tools cannot be dependable as the identification of errors would take too long. If you do not invest in this important component of the DataOps framework, you will have reduced flexibility in creating new features and enhancements for your customers, resulting in a waste of money.
Once Data observability is in place data teams will prevent time consumption in debugging and error fixing and there will be more businesses that will strive to be data driven.