Introduction

In today’s data-driven technology landscape, organization rely on data pipelines to fuel their decision process and business decisions. However, with the rise in volume of data and velocity in which it’s increasing maintaining quality, reliability and trust worthiness of the data in cloud environment have become a challenging task. This is where data observability comes into play that allows the team to monitor, understand and optimize their data workflows.

What is Data Observability?

Data observability is the ability to understand, track, manage the health, quality, reliability and performance of data throughout its entire lifecycle. It involves various steps such as monitoring data pipelines, identifying issues related to data and data pipelines, providing insights to improve data quality, reducing errors and optimizing data-driven decision-making(DDDM)

Importance of Data Observability

Data Observability is important because of many reasons. Some of the important ones are listed below

• Data Quality: It helps to ensue accuracy, completeness, and consistency in data with reducing in error, anomalies, inconsistencies and mistrust of the data. Example: Organization can monitor schema changes in the pipeline to prevent any data structure issues.
• Reliability of Data Pipelines: With real-time monitoring and alerting, teams can quickly identify and resolve pipeline issues while miming the downtime and improving Service Level Agreement(SLA) in the organization. This allows organizations to send automated notifications or alerts for any issues or anomalies of the data
• Performance Optimization: It provides insights to various technique that can be used to provide insights to optimize data processing, storage, and retrieval, improving overall efficiency of the pipeline.
• Governance and Compliance: Organization have various regulatory, Governance and compliance requirements that are kept in place to ensure privacy of the data is secured. This is done by defining policies procedures and standards for managing the data. This allows for organizations to track lineage and provenance of the data, thus making sure origin, movement and transformation of data is tracked.

Metrics provided by Data Observability

Data observability can provide a wide range of metrics to help organizations monitor, understand, and optimize their data workflows. Here are some examples of metrics data observability can provide.

• Data Quality and Error Rates
• Ingestion Rates and Processing Times
• Data Storage Capacity
• Data Lineage and Provenance Metrics
• Data Origin Source and Transformation, Processing History
• Ownership and Stewardship
• Resource Utilization
• Schema changes and Data Evolution
• Metrics on Data Governance
• Metrics on Masking, anonymization, compliance and regulations followed
• Query Optimization and Performance Details

Popular tools for data observability

Below are some of the popular tools for data observability.

• Monte Carlo
• GigaOm
• Datadog
• Dynatrace
• Databand
• Honeycomb

Conclusion

In this blog post, we learned Data Observability and Its Importance in Modern Data Tech Stack, along with some popular tools for data observability.