What is metadata
In the world of data, metadata is information that describes other data. It tells us things like where the data came from, what it means, and how it’s structured. This information is crucial for managing and understanding data in various tools, such as data pipelines (which move data around), data lakes (which store raw data), data warehouses (which store structured data), ETL processes (which transform data), data catalogs (which help users find data), and data quality platforms (which ensure data is accurate and reliable).
There are two main ways to manage metadata: passive and active. Passive metadata management is like taking a snapshot of the data landscape at a particular moment. It doesn’t change unless someone manually updates it. Active metadata management, on the other hand, is constantly updating and evolving alongside your data. This allows for a more dynamic and accurate understanding of your data environment.
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How metadata is used in data platforms
Metadata plays a crucial role in the smooth operation of various components within data platforms. For instance, data pipelines, responsible for moving and transforming data, rely on metadata to understand the structure and format of the data they handle. Data observability platforms, designed to monitor and alert on data issues, also depend on metadata to identify anomalies and track data lineage. The core of this metadata is the data schema, a blueprint that defines the structure of tables, their columns, data types, and relationships.
To function effectively, many data tools maintain their own internal metadata stores, which are essentially copies of the data schema and other relevant metadata. These metadata stores serve as a reference point for the tools, ensuring they operate correctly on all managed data assets. However, maintaining up-to-date metadata across all these tools can be challenging, especially when dealing with complex and evolving data environments. Data catalogs, for example, offer a centralized repository for metadata, often containing additional information such as documentation, descriptions, business glossaries, and data lineage, making them valuable resources for data discovery and governance.
Passive metadata management
Passive metadata management, as the name suggests, involves a manual approach to defining, curating, and documenting metadata associated with data assets. This encompasses meticulously documenting table schemas, data lineage, and business glossary terms, as well as manually configuring metadata within each tool or system that comprises the data platform. While this approach offers a degree of control and precision, it demands considerable human effort and can be prone to errors or inconsistencies, especially in complex data environments.
In essence, passive metadata management is akin to taking snapshots of the data landscape at specific points in time. These snapshots, while valuable, quickly become outdated as data evolves. This static nature poses challenges in maintaining synchronization between metadata and the actual data assets it describes, leading to potential issues with data pipelines, data quality checks, and overall data governance.
Metadata elements
Metadata can be categorized into several areas, which are listed below.
Collection & Update Frequency: This aspect of metadata management focuses on how metadata is gathered and kept up-to-date. In passive metadata management, this process is manual, requiring dedicated effort to collect and update information about data assets. Active metadata management, on the other hand, leverages automated mechanisms to continuously discover, scan, and parse data sources, ensuring that metadata is always in sync with the evolving data landscape.
Data Lineage & Relationships: This metadata element captures the origin, transformations, and relationships between data assets. Passive metadata often struggles to provide a complete picture of data lineage, making it difficult to trace data back to its source or understand its impact on downstream processes. Active metadata excels in this area, offering comprehensive, end-to-end lineage tracking and visualizing complex relationships between data, systems, and business processes.
Business Context & Enrichment: Metadata enriched with business context adds a layer of meaning and relevance to data assets. Passive metadata tends to lack this context, relying on basic descriptions and tags that may not be sufficient for understanding the data’s business value. Active metadata, through automated enrichment techniques, incorporates business terms, definitions, classifications, and ownership information, making it easier for users to discover and understand relevant data.
Governance & Compliance: This aspect focuses on ensuring that data is used and managed in accordance with organizational policies and regulatory requirements. Passive metadata management relies on manual checks and controls, which can be error-prone and time-consuming. Active metadata management leverages automated checks, alerts, and dashboards to proactively enforce policies, identify risks, and streamline compliance reporting.
Data Quality Management: Metadata plays a crucial role in monitoring and improving data quality. Passive metadata management typically provides limited visibility into data quality issues, relying on manual checks or scheduled reports. Active metadata management enables real-time monitoring of data quality metrics, proactive identification of issues through automated alerts, and efficient root cause analysis thanks to its comprehensive lineage tracking.
Scalability & Adaptability: As data volumes grow and business requirements evolve, the ability to scale and adapt metadata management becomes essential. Passive metadata management, with its manual processes and static nature, struggles to keep pace. Active metadata management, through automation and flexibility, easily scales to accommodate growing data volumes and adapts to changing needs, ensuring that metadata remains a valuable asset throughout the data lifecycle.
The need for metadata synchronization
The critical need for metadata synchronization becomes evident when considering the potential cascading effects of a seemingly minor change, such as an alteration to a table schema in the data source. If this change isn’t promptly reflected across the data pipeline and downstream systems, it can trigger a domino effect of failures. Data pipelines may break, reports may generate erroneous results, and analytics models may produce inaccurate predictions. These issues can disrupt critical business processes and erode trust in data-driven decision-making.
Without comprehensive, up-to-date metadata, including detailed data lineage and documentation of the business context of data assets, diagnosing and resolving such problems becomes a daunting task. Engineers responsible for downstream platforms may lack a complete understanding of the original data source’s structure and meaning. This necessitates time-consuming investigations, involving consultations with various stakeholders who possess the necessary domain knowledge. The longer it takes to identify the root cause and rectify the issue, the greater the impact on operations, productivity, and ultimately, the bottom line. If metadata was kept in synchronization between related systems, all these issues could be avoided.
Active metadata management
Active metadata management revolutionizes the way organizations handle metadata by embracing automation and self-service provisioning. It eliminates the need for manual intervention by automatically registering and synchronizing all components of the data platform. This ensures that metadata remains accurate, up-to-date, and consistent across the entire data ecosystem.
Within active metadata management, data domains, which represent implementations of business use cases within shared compute and storage infrastructure, often employ the concept of data products to organize metadata. Each data product encapsulates its metadata, including schemas, lineage, and business context, while adhering to corporate data governance policies. This approach promotes a unified interface for retrieving metadata from all data products, enabling efficient change detection and seamless integration with other platforms.
A key characteristic of active metadata management systems is their ability to automatically detect and apply metadata changes. For instance, upon registering a new table or adding columns within a data product, the system automatically propagates these changes to relevant tools like data quality systems and data catalogs. This eliminates the need for manual configuration and ensures that all components remain synchronized. Furthermore, active metadata management systems utilize policies to apply metadata configuration across multiple data assets, simplifying the process and reducing the risk of errors or inconsistencies.
The comparison between passive and active metadata management is summarized in the infographic below.
Business context aware metadata
Beyond the foundational capabilities of active metadata management, organizations are increasingly leveraging machine learning and generative AI, particularly vector embeddings, to unlock a deeper level of understanding within their data environments. By scanning the definitions of data assets and analyzing the actual data itself, these advanced techniques can automatically detect the meaning of values and identify similar data assets across the entire system. For instance, by recognizing that a column contains city names, the system can infer its context and suggest relevant relationships.
This intelligent approach to metadata management not only automates the creation of comprehensive documentation for data assets but also facilitates the construction of data lineage by tracing the flow of similar data throughout the organization. This enables a more holistic view of data, empowering data scientists, analysts, and business users to discover, understand, and leverage data assets more effectively. The result is a data environment that is not only well-organized but also self-aware, continuously learning and adapting to the evolving needs of the business.
Ensuring metadata health
In a data platform that relies on automation, metadata health monitoring is essential for two primary reasons. Firstly, it acts as an early warning system, detecting discrepancies between related systems or inconsistencies within the metadata itself. These issues, if left unchecked, can lead to cascading failures and data quality problems downstream. By identifying such issues promptly, automated alerts can be triggered, prompting corrective actions like restarting a job or initiating a metadata synchronization process.
Secondly, a robust active metadata management system must ensure that the information presented about data assets is always the most current and accurate. For instance, a data catalog should not only provide detailed documentation for each table but also include a summary of its current data quality status. Ideally, this would be presented as a comprehensive data quality KPI score for each table and column, encompassing various data quality dimensions like completeness, timeliness, validity, consistency, and uniqueness.
To achieve this level of metadata health monitoring, a data quality platform should be continuously running checks on all data assets. This platform should be capable of detecting not only data quality issues but also schema changes, volume fluctuations, and anomalies within each dataset. The data quality scores and insights generated by the platform should be continuously synchronized with the data catalog, ensuring that users have access to the most up-to-date information. Additionally, the platform should be configured to trigger alerts and notifications in real-time when data quality issues arise, ensuring that relevant teams (data owners, data operations, data engineering) are promptly informed and can take appropriate action.
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What is the DQOps Data Quality Operations Center
DQOps is a data quality platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow. DQOps is designed in a unique way, making it a perfect fit for monitoring data quality within an active metadata environment. The competitive advantage of DQOps is a dual interface model, allowing the storing of the data quality configuration in YAML files that are perfect for automated provisioning. Once the data quality configuration files are published, DQOps automatically discovers them and provides an alternative, no-code user interface for data consumers, data stewards, and data operations teams.
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