Data lineage is a critical concept in data management. It helps us understand how data flows between different systems, such as applications, data platforms, and client tools.
Modern data analytics architectures discourage connecting Business Intelligence tools, like Microsoft Power BI, directly to transactional databases that store data for business applications. This is because such connections can negatively impact the performance of business applications, making them unresponsive.
Additionally, the data models in transactional databases are often not optimized for reporting purposes and may require complex joins across multiple tables. Therefore, many organizations build dedicated data warehouses and data lakes. These systems store copies of data from various sources, including business applications. However, it’s important to remember that these analytical copies are still duplicates of the original data. Consequently, any data quality errors in the original data will propagate to all the copies, potentially impacting the quality of other datasets.
The primary purpose of data lineage is to track the complete path of data movement. This includes where the data is stored and which tools use it. By providing this knowledge, data lineage helps shorten the time required to investigate and identify the source of errors.
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Data Lineage Meaning
Data lineage comprehensively documents the lifecycle of data. It traces the journey of data from its origin to its various destinations, including where it is transformed and stored along the way. This “journey” is visually represented in a data lineage diagram, which maps out all source and target datasets, as well as the tools that interact with them, such as BI platforms.
Data lineage is typically defined at two levels of granularity:
Table-level data lineage
Table-level lineage tracks the movement of data, or portions of data, within source datasets across the data platform, highlighting where copies are stored.
Column-level data lineage
Column-level lineage provides a more detailed view by tracking the origin of data in target columns, following it through all transformations. This level of detail is crucial for understanding how individual data elements are derived and modified.
The Examples of Data Lineage Usage
Tracking data lineage offers several significant advantages, primarily in understanding data flow and ensuring regulatory compliance. The following examples of data lineage tracking provide the biggest benefits:
- Enhanced Understanding of Data Flows: Documenting the movement of data from upstream sources to downstream targets provides a clear picture of the data model. This comprehensive view helps data professionals understand how data is processed and transformed within the system.
- Improved Regulatory Compliance: Data lineage plays a crucial role in tracking copies of sensitive data. This is particularly important for complying with regulations like GDPR, especially when fulfilling data subject requests such as the “right to be forgotten,” which requires the deletion of all records related to an individual.
- Effective Root Cause Analysis: When data quality issues arise, knowing where the data originated enables efficient root cause analysis. By tracing the path of data, it becomes easier to pinpoint the origin of errors and implement corrective actions.
- Proactive Error Impact Prediction: Knowing all data destinations helps predict the potential impact radius of errors. This allows teams to identify which datasets and tools might be affected by data quality issues, enabling proactive communication and mitigation strategies.
Types of Data Lineage Tools
The data lineage tool landscape is diverse because different databases have varying capabilities for providing lineage information. To choose the best tool for your needs, consider these features:
- Manual Data Lineage Tracking: Offered by most tools, this method requires data teams to diligently document data flows, which can be time-consuming.
- Data Lineage Reporting APIs: Tools like Marquez, which expose APIs for reporting lineage information, are typically integrated into ETL pipelines during the early stages of data architecture design. This proactive approach ensures consistent lineage capture throughout data processing.
- SQL Query Parsing: Some modern data cataloging tools can parse SQL queries used for data transformation and loading. However, this functionality is limited to databases that provide access to query logs, such as SQL Server. For other databases, you can use SQLLineage, a Python package that can analyze SQL queries to detect source and target tables and columns.
- Data Similarity Analysis: This method identifies datasets containing similar or identical data, regardless of their format (file or table) or the database system used. This approach can be implemented at any stage of the data platform’s development and is particularly useful for discovering relationships between existing datasets.
- Built-in Data Lineage Tracing: Some modern data transformation tools, such as dbt Cloud or Databricks, support automatic data flow and transformation tracking for transformations performed within their platforms. While they may not have visibility into the ultimate source of the data or its use in analytics tools, they can effectively track data movement across different layers within their environments.
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Best Practices for Tracking Data Flows
Collecting data lineage information can be resource-intensive. However, these best practices can streamline the process:
- Automate lineage collection: Whenever possible, leverage the built-in data lineage tracking capabilities of your data loading tools. Activating these features can significantly reduce manual effort.
- Maintain an up-to-date lineage: Data lineage mappings can become outdated as data platforms evolve. Utilize data catalogs with usage tracking to identify unused datasets and decommission them along with their associated lineage flows. This practice ensures that your data lineage remains relevant and accurate.
- Integrate lineage reporting from the beginning: When designing new data platforms, incorporate data flow reporting as a non-functional requirement for all data pipelines. This can be achieved by mandating that your framework reports data transformations and storage locations through a data lineage API, such as OpenLineage. This proactive approach ensures comprehensive lineage capture for all data pipelines.
What is the DQOps Data Quality Operations Center
DQOps is a data quality and observability 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 automatically detecting data lineage to enable root-cause and impact analysis of data quality issues.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
