15 minute read · January 17, 2025
Governance in the Era of the Data Lakehouse

· Senior Tech Evangelist, Dremio

The evolution of data architectures has reshaped how organizations manage, access, and derive value from their data. Traditional systems like data warehouses and databases offered centralized control, making governance relatively straightforward but often at the expense of scalability and cost. The emergence of data lakehouses has transformed this paradigm, combining the scalability and flexibility of data lakes with the structure and performance of data warehouses.
While lakehouses deliver significant benefits—such as reduced costs, improved accessibility, and faster time to insight—they also introduce new governance challenges. The decentralized nature of data access in lakehouses, with multiple access points at the file, catalog, and query engine levels, complicates traditional governance models. As organizations adopt this modern architecture, they must rethink and adapt their governance strategies to ensure security, compliance, and reliability without sacrificing the advantages of decentralization.
What is Data Governance?
Data governance is the framework of policies, processes, and technologies that ensure data is managed securely, consistently, and effectively across an organization. It provides a structured approach to maintaining data quality, accessibility, compliance, and security, empowering organizations to make reliable, data-driven decisions.
Good governance practices are characterized by clear ownership and accountability for data, well-defined data access controls, comprehensive metadata management, and the enforcement of compliance with industry standards and regulations. Effective governance ensures that data is trustworthy and accessible to the right people while minimizing risks associated with data misuse or breaches.
The desired outcomes of strong data governance include:
Data Quality: Ensuring accuracy, completeness, and consistency across datasets.
Compliance: Adhering to legal and regulatory standards to mitigate risks.
Security: Protecting sensitive information from unauthorized access.
Accessibility: Providing users with timely and reliable access to the data they need.
Trust and Transparency: Creating confidence in data-driven decisions by ensuring data provenance and integrity.
In the era of the data lakehouse, where data resides across multiple platforms and systems, these outcomes are more critical—and complex—than ever. Implementing governance practices tailored to this architecture is essential for balancing flexibility with control.
Governance of Data Quality
Data quality is a cornerstone of effective governance in the data lakehouse era. Without accurate, consistent, and reliable data, organizations risk making flawed decisions and eroding trust in their data-driven processes. Governing data quality requires a proactive approach that integrates tools, processes, and collaboration across teams.
Modern tools like dbt (Data Build Tool) and Great Expectations empower organizations to enforce data quality at every step of the pipeline. dbt enables data engineers to transform, test, and document data as part of their workflows, ensuring consistency and transparency. Great Expectations provides a framework for applying validation checks, helping identify issues such as missing values, incorrect data types, or invalid ranges before they affect downstream systems. Together, these tools allow teams to enforce “data contracts”—agreements between upstream developers and downstream data engineers and consumers about the structure, format, and expectations of the data being collected.
Implementing a data contracts mechanism ensures alignment across teams, reducing miscommunication and data quality issues. Upstream application developers can design their systems to adhere to these contracts, while downstream teams can trust that the ingested data meets their requirements.
Automation is another critical aspect of data quality governance. Tools like Apache Airflow and GitHub Actions can be used to orchestrate CI/CD pipelines for data workflows. These pipelines can include automated quality checks to verify that incoming data meets predefined standards. If a check fails, the pipeline can prevent the ingestion of flawed data into the lakehouse and trigger notifications to the relevant teams. By identifying and addressing issues as they arise, organizations can maintain the integrity of their data and reduce the risk of costly downstream errors.
Incorporating these practices and tools ensures that data quality is not an afterthought but a continuous, free enforceable process, supporting trust and reliability across the data ecosystem.
Compliance in the Era of Data Lakehouses
Regulatory compliance is a critical aspect of data governance, particularly as organizations face increasingly stringent requirements to protect and manage sensitive information. Regulations like GDPR, CCPA, and others mandate strict controls over the storage, access, and destruction of personal and sensitive data. Failure to comply can result in hefty fines, reputational damage, and loss of customer trust.
When it comes to protecting Personally Identifiable Information (PII), fine-grained access controls (FGAC) are essential. These controls allow organizations to define row- and column-level rules that restrict who can access specific data. By using engines with robust FGAC capabilities, compliance teams can ensure sensitive data is shielded from unauthorized access. Dremio, for example, enables organizations to model data from multiple sources in one centralized platform and apply FGAC rules seamlessly across datasets. This simplifies compliance efforts by creating a unified, secure data access layer that meets regulatory requirements.
For data destruction, the governance challenges become even more nuanced in the context of lakehouse table formats like Apache Iceberg, Apache Hudi, and Delta Lake. These formats are designed for scalability and performance, but their approach to managing data deletions requires careful consideration. Typically, when a delete query is executed, the original data files containing the records to be deleted are not immediately removed. Instead, new data files are generated that exclude the deleted records.
The old files, however, remain in storage until the snapshots referencing them are invalidated. To fully comply with regulations that mandate the destruction of specific data, organizations must ensure these snapshots are invalidated and follow up with cleanup operations to permanently delete the old files. This process highlights the importance of understanding how your chosen table format handles data lifecycle management and implementing clear workflows to maintain compliance.
By combining fine-grained access controls with a deep understanding of lakehouse table formats and proper cleanup procedures, organizations can meet compliance requirements while maintaining the flexibility and scalability of the data lakehouse architecture.
Security in the Data Lakehouse
Security in the era of the data lakehouse requires a multi-layered approach. Unlike traditional data systems, where storage and access are centralized, the decentralized nature of modern table formats like Apache Iceberg, Delta Lake, and Apache Hudi introduces unique security challenges. These formats abstract datasets as tables, which are often composed of numerous underlying data files stored in distributed storage systems. Securing these files and their access points requires careful consideration across multiple layers.
At the storage layer, it’s essential to implement strong policies within the storage system. These policies should govern who can access the raw data files, limiting exposure to unauthorized users. Additionally, employing file encryption with rotating keys enhances security by ensuring that even if files are accessed, they remain unreadable without the appropriate decryption keys.
Although table formats abstract the complexity of managing individual data files, these tables must be tracked in a catalog service to function as a unified dataset. Catalogs allow query engines to discover and access tables seamlessly. A catalog with robust Role-Based Access Control (RBAC) features, like Apache Polaris, can provide consistent, table-level access controls across multiple engines and users. By enforcing RBAC, catalogs can reject unauthorized requests at the table level, ensuring only the right users gain access.
However, fine-grained access control (FGAC)—which limits access to specific rows and columns within a table—is more complex. FGAC requires reading the data to apply rules dynamically, making it challenging to enforce at the catalog level. This is why FGAC is best implemented within a query engine capable of integrating all your data sources into a single semantic layer. This layer serves as the central point for applying FGAC rules, providing secure and controlled access for end-user consumption.
A tool like Dremio excels in this role. By enabling you to unify data from diverse sources and enforce FGAC rules in real-time, Dremio ensures that security does not come at the expense of accessibility or performance. While tools for ingestion, curation, and auditing each play their part in the data ecosystem, centralizing security at the engine level ensures that governance is applied consistently and effectively across your lakehouse.
By combining security measures at the storage, catalog, and engine layers, organizations can safeguard their data assets while maintaining the flexibility and scalability of data lakehouse.
Accessibility in the Data Lakehouse
Accessibility is a critical component of effective data governance in the data lakehouse era. While lakehouse architectures provide unparalleled flexibility by integrating decentralized sources of data, this very decentralization can create challenges in managing and enforcing governance. To maintain governance while empowering users, organizations need a central, easy-to-use access layer that unifies data from various sources into a single, governed interface.
A centralized access layer simplifies governance by providing a unified point to enforce policies, track usage, and apply consistent controls. It also enhances accessibility for data consumers by eliminating the need to navigate the complexities of disparate systems. Users can focus on deriving insights rather than struggling with data silos or inconsistent access rules, streamlining workflows and increasing productivity.
Dremio is uniquely suited to serve as this centralized access layer. Its semantic layer enables organizations to organize, unify, and govern data from decentralized sources while providing users with a consistent view of the data. By abstracting the underlying complexity of a lakehouse, Dremio empowers teams to access and analyze data without sacrificing flexibility or governance.
Dremio also supports seamless integration with popular tools, ensuring that users can work with data in their preferred environments. Whether it’s a business analyst building dashboards in Tableau or Power BI, a data scientist running models in Jupyter Notebooks, or an engineer powering a data application, Dremio makes it easy to leverage the data organized in its semantic layer. This flexibility ensures that data is both accessible and actionable, fostering collaboration and enabling faster insights across the organization.
By providing a central platform for data access and governance, Dremio bridges the gap between the decentralized nature of modern data architectures and the need for consistent governance. It allows organizations to enforce policies, track usage, and provide users with the data they need, all while maintaining the scalability and performance of a lakehouse architecture.
Trust and Transparency in Data Governance
Trust and transparency are foundational to effective data governance. Without them, stakeholders across an organization cannot confidently rely on the data for decision-making, leading to inefficiencies, missed opportunities, and potential risks. Achieving trust and transparency requires clear visibility into the origin, structure, and handling of data, along with processes that ensure accountability and compliance at every stage of the data lifecycle.
One key element in building trust is data lineage—the ability to trace the origin, transformation, and flow of data from its source to its final destination. Data lineage provides stakeholders with visibility into how data is collected, processed, and used, offering assurance that it has been handled correctly and in compliance with regulations. Tools that track and expose lineage, such as Dremio’s semantic layer, make this information readily accessible, enhancing trust in the data.
Transparency in data governance also involves ensuring that users have access to relevant metadata. Metadata offers essential context about datasets, such as their schema, data quality metrics, and usage history. This context enables users to understand the meaning, limitations, and reliability of the data they work with. Platforms like Dremio simplify metadata management by consolidating and exposing this information across decentralized sources, providing users with a unified view of their data ecosystem.
Additionally, auditing and monitoring play a crucial role in fostering transparency. Detailed logs of data access, transformations, and modifications provide a record of who interacted with the data and how. These logs not only help enforce accountability but also support compliance efforts by demonstrating adherence to regulatory requirements.
Trust and transparency are further strengthened by establishing clear data ownership and accountability. Assigning roles and responsibilities for datasets ensures that there is always a point of contact for resolving issues, answering questions, or implementing changes.
When these elements—data lineage, metadata management, auditing, and accountability—are combined, organizations can foster a culture of trust and transparency. Users feel confident in the quality and reliability of the data they access, while governance teams gain the tools needed to ensure compliance, security, and accuracy. Platforms like Dremio, with their robust governance capabilities, make it easier to achieve this balance, enabling organizations to unlock the full potential of their data with confidence.
Conclusion
The data lakehouse has revolutionized the way organizations manage and access data, combining the scalability of data lakes with the structure of data warehouses. However, this shift also introduces new governance challenges that must be addressed to fully realize its potential. Effective governance in the lakehouse era requires a multi-faceted approach that prioritizes data quality, compliance, security, accessibility, and trust.
By leveraging modern tools like dbt, Great Expectations, and Dremio, organizations can implement robust governance frameworks that ensure data is accurate, secure, and accessible. These tools empower teams to enforce quality checks, manage sensitive data in compliance with regulations, secure decentralized data at multiple layers, and provide a centralized semantic layer for consistent access. At the heart of governance is transparency and trust, achieved through data lineage, metadata management, and accountability, enabling stakeholders to confidently rely on their data.
As data architectures continue to evolve, organizations that adopt governance practices tailored to the unique needs of the lakehouse will unlock greater flexibility, faster insights, and more reliable decision-making. With platforms like Dremio, businesses can bridge the gap between decentralized data and centralized governance, empowering teams to innovate while maintaining control. Governance in the era of the data lakehouse isn’t just a necessity—it’s a strategic advantage.