Below are some mechanisms and practices that can be implemented for data lineage and metadata management:
(A) Metadata Repository:
– Implement a centralized metadata repository to store and manage metadata information of data warehouse.
– The metadata repository should capture information such as data definitions, data sources, transformations, dependencies and data lineage.
(B) ETL Metadata Capture: Configure the Extract, Transform, Load (ETL) processes and tools to automatically capture metadata. Metadata captured can include source systems, transformations, mappings, job details, and execution logs.
(C) Data Modelling& Documentation:
– Maintain detailed documentation of the data models, data structures and data dictionaries within the data warehouse.
– Use data modelling tools to capture ER information, attribute definitions and business rules.
(D) Data Lineage Tracking: Implement Data lineage tracking to capture the flow of data, transformations applied and any dependencies between data elements.
(E) Impact Analysis: Leverage data lineage information to perform impact analysis when changes are made to data sources, data models or transformation logic.
(F) Metadata Search & Discovery: Implement search and discovery capabilities within the metadata repository, allowing users to easily locate and understand data assets within the data warehouse.
(H) Metadata Quality & Maintenance:
– Implement processes to ensure the accuracy and completeness of metadata within the repository.Regularly review and update metadata to reflect changes in data sources, transformations, or business requirements.
Tools like Ask On Data, with its simple chat interface powered by AI, can help you simply type and load the data into the data warehouse as well as do the required transformations. It can help in saving around 93% time in creating data pipelines as compared to tradition ETL tools.
If you are looking for some professional guidance you can reach out on www.helicaltech.com