Data Lineage, in the context of Data Modeling, refers to the comprehensive and traceable journey of data from its origin through various stages of refinement, processing, and transformation until it reaches its final destination, which could be a report, a dashboard, or an application. Data Lineage plays a crucial role in understanding the flow of data, ensuring data reliability, accuracy, and consistency, and facilitating data auditing and governance.
At the core of the AppMaster no-code platform is its ability to visually create data models (database schema), which form the foundation for designing Business Processes and generating application source code. In this context, Data Lineage becomes an essential aspect of maintaining transparency, traceability, and accountability for the data used in these applications.
A typical Data Lineage process consists of several stages, which may vary according to the specific data modeling and data processing requirements of an organization:
- Data Ingestion: At this stage, data is acquired from various sources, such as external APIs, user inputs, databases, files or any other type of source. This stage sets the stage for the initial data that will pass through different data processing pipelines.
- Data Transformation: As the data moves through processing pipelines, it may be transformed, enriched, or cleaned, depending on the business requirements. Data transformation may involve operations like filtering, aggregation, joining, or pivoting, as well as the application of business rules and logic.
- Data Storage: After the data has been processed and transformed, it is stored in databases, data lakes, or other storage systems for future retrieval, analysis, or integration with other applications.
- Data Access: Users, applications, or services access the transformed data from storage systems based on specific requirements. This involves reading or querying the data by using APIs, SQL, or other querying languages.
- Data Consumption: The final stage of Data Lineage is the consumption of the processed data in the form of reports, analytics, dashboards, or visualizations, which provide insights, support decision-making, or drive application functionalities.
Data Lineage plays an important role in addressing various data management and governance challenges:
- Data Quality Assurance: By capturing metadata about the different transformations that data undergoes, Data Lineage helps identify anomalies, errors, or inaccuracies and resolve them, thus ensuring data quality and reliability.
- Data Compliance: Data Lineage provides an audit trail of the data, enabling organizations to demonstrate compliance with data protection, privacy, and security regulations, such kInstruction[label=data.labels.USER].onents in the Web BP designer and make.AutoScale(properties_input)roperties to demonstrate compliance with data]}>
