Practical Data Observability Explained

Learn about the importance of data observability in the age of big data. Discover how practical solutions can ensure data integrity and trustworthiness.



October 24, 2023


In the dynamic world of data engineering, "Data Observability" has become a focal point of discussion and implementation. While the concept has evolved to include complex elements like DataFinOps and DataBizOps, there is a growing need for a more streamlined approach. This article introduces the concept of Practical Data Observability, designed to offer immediate value without contributing to existing #datadebt, catering specifically to medium and large enterprises.

Data observability is the ability to understand the health and state of your data pipelines and data ecosystem. It is essential for ensuring that your data is reliable, accurate, and trustworthy. Also DO helps in data lineage which is powerful module to find dependencies and easy to debug the data incidents. Lineage is also quite crucial in overall data visibility of the infrastructure.

In recent years, data observability has become increasingly important for mid-size and large companies. These companies are increasingly reliant on data to make decisions, and they need to be able to trust that their data is accurate and up-to-date.

However, traditional data observability solutions can be complex and expensive to implement and maintain. This can make them out of reach for many mid-size and large companies.

The Evolution of Data Observability

Data Observability has undergone significant transformation from its initial focus on basic data monitoring. Industry thought leaders have expanded its scope to include what are now considered the 5-pillars of Data Observability. However, this evolution, while comprehensive, often adds layers of complexity that can be overwhelming even for well-resourced enterprises.

What is the difference between data observability and data monitoring?

Data observability and data monitoring are both important concepts in ensuring the quality and reliability of data. While they share similarities, there are distinct differences between the two.

Data monitoring refers to the process of continuously tracking and measuring data to ensure its correctness, completeness, and consistency. It involves setting up alerts and notifications to detect anomalies, errors, or deviations from expected patterns in the data. Data monitoring focuses on real-time or near-real-time analysis of data to identify issues as they occur. It provides a reactive approach to data quality by addressing problems once they are detected.

On the other hand, data observability goes beyond data monitoring by incorporating a proactive and holistic approach to data quality. It involves not only monitoring the data but also understanding its behavior, characteristics, and lineage throughout its lifecycle. Data observability provides insights into the internal workings of data systems, allowing data professionals to gain a deeper understanding of how data is collected, processed, and transformed.

Data observability tools enable data professionals to track and trace data from its origin to its destination, ensuring transparency and accountability. They provide visibility into data pipelines, transformations, and dependencies, helping to identify bottlenecks, errors, or inconsistencies. By understanding the context of data, data observability enables proactive problem-solving and optimization of data systems.

In summary, while data monitoring focuses on real-time tracking and identification of data issues, data observability takes a more comprehensive approach by providing visibility, transparency, and understanding of data systems. Both data observability and data monitoring

Why Practical Data Observability?

1. Onboarding

The initial interaction with a data solution sets the stage for its future adoption. Practical Data Observability emphasizes quick and straightforward deployment, allowing data teams to immediately assess its value.

2. Capabilities

Data ecosystems are inherently complex. Practical Data Observability aims to simplify this complexity by offering essential features like standard Data Quality (DQ) checks, data health, freshness evaluations, and checks for volumetric drifts and anomalies. It also provides configurable options for data pipeline circuit-breakers, data cataloging, and governance.

3. Integrations

Enterprises often find themselves burdened with #datadebt due to the use of multiple point tools. Practical Data Observability advocates for seamless integrations, offering common out-of-the-box solutions that can evolve with the enterprise's data needs.

4. Costs

Cost-efficiency is a critical aspect of Practical Data Observability. The approach ensures that the total cost of ownership is proportional to the value it delivers, eliminating the need for multiple, costly "best of breed" solutions.

5. Innovations

Technological stagnation is not an option in today's fast-paced world. Practical Data Observability prioritizes innovations that align with shifting customer priorities, such as AI, active metadata, cloud computing, and SaaS.

Pain Points Faced by Data Engineers

  • Pipeline Observability: Lack of real-time insights into data pipelines, making it difficult to monitor and troubleshoot issues.
  • Unreliable Data (data issues): Inconsistent data quality leading to unreliable analytics and decision-making.
  • No Visibility on the Entire Pipeline: Fragmented view of data pipelines, making it challenging to understand end-to-end data flow.
  • Duplicate Data: The presence of redundant data, leading to increased storage costs and analytical inaccuracies.
  • Data Infra Failure (data downtime): Infrastructure vulnerabilities leading to data loss, downtime, and compromised data integrity.
  • Data Inaccessibility: Difficulties in accessing and retrieving data due to complex data architectures or restrictive data access controls.
  • Data Silos: Data stored in isolated systems or departments, hindering collaboration, data sharing, and holistic data analysis.
  • Data Inconsistency: Inaccurate or conflicting data across different sources, making it challenging to establish a single source of truth.
  • Data Security Risks: Vulnerabilities in data systems and unauthorized data access, posing a risk to data privacy and compliance.
  • Data Governance Challenges: Lack of standardized data governance policies and frameworks, leading to difficulties in ensuring data compliance and accountability.
  • Lack of Documentation: Insufficient documentation about data pipelines, transformations, and business rules, making it difficult to understand and maintain data processes.

The Business Case for Practical Data Observability

For medium and large enterprises, adopting Practical Data Observability is a strategic move. These organizations often have complex data ecosystems and are looking for solutions that offer immediate value without adding to their existing data complexities. Practical Data Observability, with its focus on quick onboarding, essential capabilities, seamless integrations, cost-efficiency, and continuous innovation, provides a compelling answer to the data challenges faced by these enterprises.

Benefits of Practical Data Observability

Practical data observability offers a number of benefits for mid-size and large companies, including:

Improved data quality: Practical data observability can help data engineers to identify and fix data quality problems more quickly and easily. This can lead to significant improvements in the quality of the data that is used to make decisions.

Reduced downtime: Practical data observability can help data engineers to identify and fix data problems before they cause downtime. This can lead to significant reductions in downtime and associated costs.

Increased productivity: Practical data observability can help data engineers to automate their data observability tasks. This can free up their time to focus on more strategic initiatives.

Improved collaboration: Practical data observability can help data engineers to collaborate more effectively with other data stakeholders. This can lead to better decision-making and more efficient data operations.


The data landscape is complex and ever-changing. While Data Observability is crucial, enterprises need a more practical approach. Practical Data Observability offers a balanced, value-driven solution that addresses immediate needs without adding to existing #datadebt or data lifecycle. It's not just about observing your data; it's about doing it in a way that is practical, efficient, and actionable.

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