Data quality is one of the most critical factors that determines the success or failure of a data warehouse. Even with the right tools, frameworks, and processes, failure to implement end-to-end data quality practices can prevent organizations from deriving real value from their data.
Data quality in a data warehouse takes many shapes and forms. You can:
- Cleanse data to improve its quality by deduplication and standardization
- Apply custom business rules to validate data to comply with your business’s needs
- Ensure data integrity and consistency across a broad data model
All these things and more contribute to higher confidence in the data warehouse’s outputs, enabling better decisions, compliant reporting, and reliable AI/ML analytics.
In this article, you’ll explore:
- The importance of data quality in a data warehouse
- What high-quality data looks like in practice
- Implementation options: native features vs external tools
- The role of a metadata control plane like Atlan in enabling quality at scale
- How Atlan integrates with warehouses like Snowflake and other data quality tools to automate and operationalize data quality
Table of contents #
- What does data quality look like in a data warehouse?
- How can you implement data quality in a data warehouse?
- Why is a metadata control plane essential for enhancing data quality in a data warehouse?
- How does Atlan help with data quality in a data warehouse?
- Final thoughts about data quality in a data warehouse
- Data quality in a data warehouse: Frequently asked questions (FAQs)
What does data quality look like in a data warehouse? #
A data warehouse is a proven and tested data architecture pattern that powers analytical workloads. It connects to and brings data from various operational systems, both internal and external. This allows you to cleanse, transform, remodel, and reshape data into a singular model using a variety of modelling techniques – star schema, data vault, and snowflake schema.
In the process of preparing the data for analytical use, it is also crucial to ensure that data quality is maintained and, in many cases, improved.
Some of the most common tests relate to the following aspects:
- Data completeness tests track the completeness of data with the
NOT NULLconstraint and the empty string equality check on a field level. This test is also conducted at a record level to understand the values that are missing from a record. - Data duplication tests measure the instances of duplicate records based on either a few fields or the whole record. To fix the duplication, a deduplication process can be implemented as part of the data pipeline workflow.
- Data standardization tests verify whether the data conforms to the prescribed standards for specific field values, data types, and precision. These tests are crucial in implementing standardization based on reference and master data.
- Data integrity tests allow you to have an insight into how well the relationships between data across different tables are maintained across the data model. For example, if two tables have a 1:n relationship, is it maintained or violated?
- Data consistency tests give you a picture of the consistency of data across various tables, which is especially important in data warehousing. Most data models used for data warehousing introduce denormalization in data with fact-dimension (in dimensional modelling) or link-satellite-hub (in data vault modelling) table groups, which results in redundancy. Hence, it becomes important to maintain data consistency across the redundant copies.
- Data transformation tests measure the accuracy of transformations based on business logic and data model requirements, ensuring that you have a view of whether the data was correctly reshaped, remodelled, and transformed.
Now, these are just a few examples to give you a concrete idea of what data quality looks like in a data warehouse. Such tests form the baseline for reliable analytics and compliance reporting.
Let’s now see how you can implement data quality in a data warehouse.
How can you implement data quality in a data warehouse? #
Implementing data quality in a data warehouse has become slightly easier in recent years, thanks to the data quality-related features packaged with platforms like Snowflake. It has also become easier to integrate with external data quality tools (like Soda) for data quality and testing purposes.
Broadly, there are three approaches you can take for implementing data quality for your data warehouse:
- Native data quality features: A platform like Snowflake offers data quality features, such as data quality and data metric functions, which you can build into your data pipelines and workflows. These native features can help you meet service-level agreements for accuracy and freshness, help build trust in data by adding validations, and also facilitate compliance.
- External data quality tools: Sometimes, the native data quality functionality isn’t enough, or there is a need for a platform-agnostic data quality and testing layer. For instance, a data warehouse platform’s native functionality may only allow for unit tests that can verify specific validations and constraints. For a more holistic approach that covers unit tests, data tests, and other types of tests, tools like Soda and Monte Carlo can be leveraged.
- The best of both worlds: In practice, none of the above options provide end-to-end coverage and confidence in terms of data quality. Bringing these two options together, you can leverage the native functionality of the data warehousing platform, along with the additional functionality that the external data quality tools provide. However, to achieve this confluence, you need a unified metadata control plane like Atlan. Such a platform, besides having its own data quality features, also integrates with a variety of data warehousing platforms, databases, and external data quality tools.
Why is a metadata control plane essential for enhancing data quality in a data warehouse? #
Managing data quality using the native functionality of a data warehouse platform and an external data quality tool can be extremely inconvenient, as it adds both operational and maintenance overhead for the data teams.
Instead of these direct and, often, shallow integrations between the data warehousing platforms and data quality tools, you need to leverage metadata to drive data quality.
This is why you need a control plane for metadata, which not only integrates with the metadata layers of the data warehousing platform and the data quality tools but also has its own value-adding data quality features, giving you an end-to-end data quality management experience.
Let’s explore what a control plane for data looks like and how it can help with data quality.
How does Atlan help with data quality in a data warehouse? #
Atlan’s unified metadata control plane not only allows you to catalog, discover, and govern data across your ecosystem, but also track and fix data quality issues. Its prominent features and integrations include (but aren’t limited to):
- Integration with data warehouse platforms: Atlan integrates with platforms like Snowflake on multiple levels, primarily through its data dictionary and the Snowflake Horizon Catalog. This enables Atlan to utilize features such as Snowflake’s native data quality and data metric functions.
- Integration with external tools: Atlan integrates with external tools, such as Anomalo, Monte Carlo, and Soda. This enables you to elevate data quality and testing to the next level.
- Native features: Atlan also offers its native data quality features, which include data contracts, policy enforcement, surfacing trust signals, such as data freshness, test status, and issue history. Plus, you can use metadata triggers to flag data quality issues.
- Automation for discovery, classification and lineage: Atlan enables automated discovery and classification of data assets. It establishes actionable, column-level lineage and drives tag propagation via lineage mapping.
- Active metadata orchestration: Through automated lineage-based impact analysis and quality rule propagation, Atlan lets you respond to data issues proactively.
- Bidirectional sync: Tags created in Atlan can be pushed back into Snowflake (and vice versa), ensuring your data quality, governance, and compliance controls remain aligned across platforms.
Atlan + Snowflake: A powerful combination to strengthen data quality in your Snowflake data warehouse #
The Atlan + Snowflake integration is deep. You can crawl metadata, mine data, work with tags, and integrate with Snowflake Horizon Catalog for enabling data quality into the organization’s workflow.
That’s exactly what TechStyle’s VP of Data & Analytics, Danielle Boeglin, did, “We were looking for a tool that natively integrated with our modern data stack — Snowflake and Tableau. Atlan was very easy to set up. We had all our data sources flowing in within a day.”
For more information, head over to the latest documentation.
Final thoughts about data quality in a data warehouse #
Data quality is crucial for the correct functioning of a data platform, irrespective of the data architecture pattern (such as data warehouse, lakehouse, etc.) it follows.
It’s quite common for data warehousing platforms to have some native data quality functionality, which is often not enough. That’s why there’s a need to leverage external tools like Soda, Monte Carlo, and Anomalo.
Atlan, with its unified metadata control plane, supports all these tools for enhancing data quality in the data warehouse, particularly through its deep integration with Snowflake’s Horizon Catalog, for example. It also integrates with other data platforms that you may use to set up your organization’s data warehouse.
Learn more about data quality on Atlan’s official documentation.
Data quality in a data warehouse: Frequently asked questions (FAQs) #
1. What are the data quality rules in a data warehouse? #
Data quality rules in a data warehouse define expected behaviors or constraints for your data, like uniqueness, completeness, format validation, and referential integrity. These can be technical or business-specific. You can codify these in data pipelines, QA tools, or metadata policies to automate enforcement.
2. What are the types of data quality in a data warehouse? #
The most common types include completeness, accuracy, consistency, integrity, uniqueness, and timeliness. These dimensions help assess whether your data is reliable, aligned across systems, and fit for analytics or reporting. You can track them using rule-based checks or anomaly detection tools.
3. What are some common data quality tests in a data warehouse? #
Some common data quality tests in a data warehouse include checking for null values and duplicates, ensuring data type and schema adherence, performing allowed value checks, and conducting referential integrity tests, among other things.
4. What methodologies exist for addressing data quality needs in a data warehouse? #
You can utilize the native data quality features of a data warehouse platform. You can also employ external tools like Soda, Anomalo, or Monte Carlo to address data quality issues.
But the best way forward is to take a hybrid approach with a metadata control plane facilitating data quality in your data warehouse using deep integrations with various tools.
5. Why is data quality important in a data warehouse? #
A data warehouse powers critical business decisions, so poor data quality can lead to faulty insights, compliance issues, and lost trust. High-quality data ensures consistency, enables cross-functional alignment, and supports accurate forecasting, modeling, and compliance reporting.
6. How do you improve data quality in a data warehouse? #
Start by profiling data to identify issues, then apply targeted rules for cleansing, standardization, and validation. Implement testing in pipelines (unit tests, transformation tests), monitor quality metrics, and leverage tools like Soda or Monte Carlo for continuous checks.
A metadata control plane can help automate this by linking quality with lineage, ownership, and business context.
7. What role does metadata play in data quality? #
Metadata is core to all data-related activities, whether it is data governance, cataloging, search, or discovery. Data quality is no exception. Metadata provides essential context for data, including its origin, structure, and relationships. This metadata is not good-to-have, but essential for implementing data quality and testing frameworks in your data platform.
8. How does Atlan integrate with Snowflake for data quality? #
Atlan integrates with Snowflake on many levels. It integrates with Snowflake’s data dictionary to crawl metadata. It also communicates with Snowflake to synchronize tags in both directions.
Moreover, it integrates with Snowflake’s native data catalog called Snowflake Horizon Catalog to get the most out of Snowflake’s native data quality, lineage, and governance features.