Enhance Your Data Governance with DataHub
The Metadata Platform for the Modern Data Stack
In today’s digital age, data has become the lifeblood of organizations. It is a valuable asset that enables organizations to make informed decisions, gain a competitive advantage, and achieve business goals. However, with the increasing volume and complexity of data, managing data assets has become a daunting task for organizations. This is where data governance comes in.
Data governance is a set of processes, policies, and standards that enable organizations to manage their data assets efficiently, accurately, and safely. It ensures that data is used ethically, consistently, and in compliance with regulatory requirements. The pillars of data governance include data catalog, data ownership, data quality, data lineage, metadata management, data security and privacy, and data integration.
In this article, we will focus on how DataHub LinkedIn satisfies the pillars of data governance. DataHub LinkedIn is a data management platform that enables organizations to manage their data assets efficiently and effectively. Its features and functionalities provide organizations with a data governance framework that enables them to manage their data assets with ease. LinkedIn DataHub provides a user-friendly web interface that allows users to search for data sets, explore the metadata associated with them, and view usage statistics. It also offers a RESTful API that can be used to programmatically access metadata and data. Let’s explore how DataHub LinkedIn’s features satisfy the pillars of data governance.
Data Catalog
One of the key features of LinkedIn DataHub is its data catalog. A data catalog is a centralized repository of metadata that provides information about data assets such as tables, columns, data types, and relationships between data assets. The platform provides a view of an organization’s data assets, including metadata such as data sources, data models, data dictionaries, and data lineage. The platform’s search capabilities allow data users to easily discover and understand the data available to them. Additionally, the LinkedIn DataHub provides tools for data classification and tagging, enabling organizations to maintain consistent naming conventions and organize their data assets more effectively. To integrate LinkedIn DataHub with a data catalog, organizations can use LinkedIn’s metadata ingestion API to extract metadata from various data systems and import it into the data catalog. This allows users to search and discover data assets in a unified way, regardless of where the data is stored.
Data Ownership
Another critical feature of LinkedIn DataHub is data ownership. The platform helps organizations define and enforce data access and usage policies, ensuring that data is only accessed by authorized personnel and that sensitive data is properly protected. The platform’s role-based access control enables data owners to define access levels for different groups of users. It also includes features such as data lineage tracking, audit trails, and logging, which help organizations monitor and enforce data usage policies.
Data Quality
Data quality is another essential aspect of data governance, and LinkedIn DataHub provides several features that help maintain data quality. The platform’s data profiling capabilities allow organizations to understand the characteristics of their data, while data validation ensures that data meets specified quality standards. DataHub allows organizations to manage metadata in a standardized way, which helps ensure that data assets are properly documented and that everyone is using consistent terminology. Standardization helps reduce confusion and errors caused by data inconsistencies. The platform’s data cleansing and enrichment tools enable organizations to improve the accuracy and completeness of their data. Additionally, the LinkedIn DataHub provides a data quality score, which provides an objective measure of the quality of the data and helps data users make informed decisions on how to utilize the data. Data quality is a team effort, and LinkedIn DataHub provides a platform for teams to collaborate and share knowledge about data assets. By working together, teams can identify and correct data quality issues more efficiently.
Data Lineage
Data lineage is also critical to effective data governance, and LinkedIn DataHub provides a complete view of an organization’s data lineage. This includes information on the data sources, transformations, and usage of the data. The platform allows data users to understand the context of the data and how it has been transformed throughout its lifecycle. Additionally, the LinkedIn DataHub provides tools for data lineage visualization, making it easier for data users to understand the flow of data and identify potential issues or inconsistencies.
Metadata Management
DataHub LinkedIn’s metadata management capabilities enable users to understand the structure and meaning of their data assets. The platform provides a comprehensive metadata management system that allows users to manage metadata across the enterprise.
Data Security and Privacy
In terms of data security and privacy, LinkedIn DataHub provides a security framework that includes encryption, authentication, and authorization. The platform’s data access policies ensure that data privacy is maintained, and organizations can define access levels based on their specific requirements. Finally, the LinkedIn DataHub provides tools for data integration, enabling organizations to bring data from different sources together and make it available to data users in a unified format.
Data Integration
Data integration is the process of combining data from different sources and presenting it in a unified and consistent way, which can help organizations gain a better understanding of their data and make more informed decisions. DataHub LinkedIn’s data integration capabilities enable organizations to easily integrate data assets from multiple sources. The platform provides powerful data integration capabilities, enabling organizations to automate data ingestion and processing. DataHub can provide a mapping layer that allows organizations to map data elements from different sources to a common set of attributes. This mapping layer can simplify the process of integrating data from different sources and ensure that data is presented in a consistent and meaningful way.
DataHub API
Using the DataHub API, developers can build custom applications and integrations that leverage DataHub’s metadata management capabilities. For example, a developer could build an application that automatically populates metadata for new datasets, or an integration that synchronizes metadata between DataHub and other data management systems.
To summarize, the DataHub API provides a powerful way to programmatically interact with DataHub’s metadata store, enabling developers to build custom applications and integrations that leverage DataHub’s metadata management capabilities.
The DataHub API can be exploited to get information about the owner of a table, which can be useful decide whether or not data must be shown. To do this, the choice would be to use the DataHub Search API to look for for the specific table and get the metadata associated with it. The metadata will include information about the owner of the table, as well as other information such as the table’s schema and data source.
To make an example, if the owner of the table belongs to a certain team or department, you can restrict access to related data to only members of that team or department.
Conclusion
In conclusion, LinkedIn DataHub’s features and functionalities satisfy the pillars of data governance by providing organizations with a complete data governance framework. The platform enables organizations to manage their data assets efficiently, accurately, and safely by providing a centralized inventory of data assets, enabling data ownership and access control, ensuring data quality and lineage, and providing robust data security and privacy controls.
Giuseppe Brescia ,Antonio La Macchia, Ivana Orefice, Michele Pini, Michele Lanotte
