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Elevating Data Observability: Key Approaches for Leveraging dbt Cloud


Data Build Tool (dbt) has emerged as a critical component for streamlining data analytics workflows, thanks to its ability to keep up with the growing and evolving MarTech and data stacks. With its ability to transform raw data into clean, organized, and ready-to-use datasets, dbt has gained popularity among data teams who want to improve the efficiency and effectiveness of their data analytics processes.

As data becomes increasingly voluminous and complex, the importance of data quality testing cannot be overstated. Especially as the data is ingested into other systems and used for decision making. The principle “shit in, shit out” certainly applies to data. Conducting regular tests on the quality of data has become essential to guarantee its reliability, accuracy and consistency. By running tests, data teams can proactively identify and address potential data issues before they impact downstream systems, processes, and decisions.

In addition, data quality testing can also help to identify data discrepancies, such as missing or duplicated data, and inconsistencies in data structures and formats. By addressing these issues, data teams can ensure that their data is well-structured and meets the specific requirements of their organization's business needs.


Data observability is the process of measuring and monitoring the quality, completeness, accuracy, and consistency of data in a system or application. It involves setting up metrics and monitoring tools to ensure that data is available, reliable, and trustworthy.

Setting up a strong Data Observability Framework helps your company achieve excellent data quality. Data Observability relies on five pillars originating from the DataOps* movement:

  1. Freshness: data freshness is a term used to describe the extent to which data is current and up-to-date. It is a metric that determines how timely the data is and how closely it reflects the current state of the information.

  2. Lineage: data lineage tracks the movement of data throughout its lifecycle, including its origins, changes, and movements across systems and applications. It provides visibility into the flow of data, showing where it came from, how it was transformed, and where it went.

  3. Quality: Data quality is the measure of how well data meets defined standards and expectations, encompassing accuracy, completeness, consistency, and validity.

  4. Schema: data schema refers to the structure or blueprint of a data set, which defines how the data is organized and represented.

  5. Volume: data volume refers to the amount of data generated or processed by a system over time.

To achieve the objective of effectively leveraging the Data Observability Framework described in the paragraph, dbt can serve as an excellent solution. Thanks to dbt, a company can define and prioritize data quality metrics, implement automated monitoring features to continuously track these metrics, establish transparent data workflows, leverage dbt's built-in data validation checks, and utilize its analytical capabilities to analyze data quality trends, identify patterns, and proactively address potential issues, thus enhancing overall data observability and ensuring high-quality data.

*“DataOps is the ability to enable solutions, develop data products, and activate data for business value across all technology tiers from infrastructure to experience.” -Forrester


Even though dbt already offers basic vanilla testing features, such as data uniqueness or validity testing, they may not be sufficient for ensuring comprehensive testing of your data. The idea is to leverage code that has already been developed and shared by the community rather than reinventing the wheel. Here are the top four packages we use at Human37 for data quality testing.

  1. dbt utils: the most essential packages, prerequisite to most of the other dbt packages. It contains generic macros reusable across dbt projects that raise the testing capacity of data engineering teams.

  2. dbt profiler: by analyzing data quality metrics such as completeness, accuracy, consistency, and validity, data profiling can provide insights into data quality problems. This package contains macros that create data profiling tables of your models. These tables can be analyzed or visualized to detect anomalies.

  3. dbt elementary: helps you monitor your dbt project with additional tests, offers the possibility to create data quality monitoring reports and alerts.

  4. dbt expectations: inspired by the Python library, this package enables you to make assumptions (expectations) about your data distribution. By comparing incoming data to these expectations, it is possible to assess whether the data is behaving similarly to historical data.


In the realm of data engineering, data observability has emerged as a critical factor for success. To address this challenge, dbt has proven invaluable in streamlining data analytics workflows and transforming raw data into clean, organized, and analysis-ready datasets. With the ever-growing complexity of data, conducting regular data quality testing has become essential. By running tests and leveraging dbt's comprehensive packages, data teams can proactively identify and address potential issues, ensuring the reliability, accuracy, and consistency of their data. Implementing a robust Data Observability Framework alongside the utilization of dbt, empowers organizations to achieve excellent data quality, enhance their overall data observability, and make informed decisions based on reliable data.

If you would like to explore the details of our work with other clients, please feel free to reach out. We are always eager to engage in insightful discussions.


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