Data Lineage

Automated SQL Lineage

Blindata SQL Lineage helps you effortlessly track and manage data movements within your database. The SQL Lineage module uses schema metadata and extracted SQL statements to infer data flows and transformations, including standard database objects such as views and routines, query logs, and scripts generated by ELT tools.



Blindata SQL Lineage empowers you to efficiently oversee and monitor data movements, accelerating root cause analysis and impact assessment. This user-friendly and highly configurable software module ensures seamless customization to align with your unique requirements. Additionally, the open API of Blindata facilitates effortless integration into your existing tech stack, enhancing its versatility and adaptability.

Automated SQL Lineage Screenshots


The automated SQL parser within Blindata generates a SQL syntax tree that vividly illustrates data flows and transformations present in the statements. Subsequently, it streamlines this representation by eliminating transformations, crafting a concise lineage graph that exclusively links tables and columns.

Enhancing user accessibility, the lineage visualization incorporates drill-down capabilities. This feature empowers users to swiftly identify the script or routine responsible for generating a specific dataflow. Furthermore, users can easily delve into transformation details with just a few clicks, facilitating a comprehensive analysis of the underlying processes.

Blindata’s SQL Lineage module employs automated SQL parsing to enhance user understanding of data flows and transformations within SQL statements. By utilizing schema metadata and SQL statements, it constructs a detailed SQL syntax tree that precisely illustrates these dynamics.

Users benefit from the simplicity of comprehending the intricacies of data flows and transformations within their SQL statements even in the most complex scenarios.

Moreover, Blindata offers a versatile approach to script analysis, incorporating automatic, manual, and markup comment-driven methods. These techniques enable effective identification of SQL statements within scripts. Users can opt for instantaneous generation of data flows at the catalog level or choose to meticulously review each routine or statement based on their specific needs. This adaptable approach ensures users can tailor their analysis method to align with their unique requirements.

The comprehensive lineage representation provides a thorough overview of data lineage, encapsulating all transformations made within a SQL statement. This representation serves to enhance the clarity of intricate statements and expedites the problem-solving process.

Conversely, the condensed lineage representation prioritizes dependencies by removing transformations and constructing a lineage graph connecting tables and columns. This streamlined representation offers a clearer perspective of data lineage, simplifies the analysis process, and facilitates the swift identification of issues that demand attention.

Thanks to its extensible preprocessing rules, Blindata’s parser can effortlessly handle uncommon keywords and vendor-specific syntax, delivering a personalized experience that perfectly aligns with your specific needs.

Blindata prioritizes transparency, providing users with access to all aspects of the analysis process, including configurable metadata crawling, syntax tree reconstruction, and data lineage generation. Our approach ensures that all results, successful or not, are provided to our users, giving them complete control over their data and the ability to make informed decisions. With Blindata, users can trust that they have access to advanced tools to optimize their analysis process and get the most value from their metadata.

The SQL Lineage module offers transparent analysis with inspectable outputs and seamless integration with external tools through REST API.

How to

Data Catalog Crawling

Extraction and loading of schema information: tables and columns definitions.

Analyze Views

Analysis of views definitions through SQL automatic parsing.

Analyze Procedures and Query Log

Analysis of routines and sql statements form executions logs, through automatic query parsing.

Augment data lineage

Fill the gap between different systems through the link between different data sources.