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How can you maintain data lineage with surrogate keys?

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Data lineage is the process of tracking the origin, transformation, and usage of data in a data warehouse. It helps you understand the data quality, reliability, and dependencies of your data assets. Surrogate keys are artificial identifiers that are assigned to data rows in a data warehouse to maintain referential integrity and support slowly changing dimensions. Natural keys are the original identifiers that are derived from the source systems and reflect the business meaning of the data. How can you maintain data lineage with surrogate keys? Here are some tips and best practices.

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1 Use a metadata repository

A metadata repository is a central place where you can store and manage the metadata of your data warehouse, such as the definitions, structures, mappings, and lineage of your data elements. By using a metadata repository, you can document the relationships between the surrogate keys and the natural keys, as well as the transformations and rules that are applied to generate the surrogate keys. This way, you can trace the data lineage from the source systems to the data warehouse and vice versa.

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Jaishree Mazumdar (PMP)?

Project Manager - Capital Market|Banking at Accenture | Data Analytics | ETL
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With metadata repository, the relationship between surrogate keys and natural keys can be documented that can help with tracing of data lineage from source to DWH

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Sandeep Rangineni

Sr Data Test Engineer @ Pluto TV | Master's in Engineering
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A metadata repository is a central database for storing and managing metadata, facilitating efficient data organization, retrieval, and analysis.

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Jithin Majeed

Data Architect @Mawer Investment Management
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Maintaining data lineage with surrogate keys in a data warehouse involves diligent practices. Start by documenting the assignment of surrogate keys, detailing their sources, and transformation logic. Create metadata records associating surrogate keys with source data and define relationships. Use consistent naming conventions for surrogate keys and store historical data to track changes. Implement data lineage tracking tools or metadata repositories to automate the process and facilitate the tracing of surrogate key transformations. Regularly update and validate lineage information to ensure accuracy and facilitate impact analysis when changes occur, maintaining a clear and reliable lineage for effective data governance and analysis.

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2 Use consistent naming conventions

Naming conventions are the rules and standards that you follow to name your data elements, such as tables, columns, keys, and indexes. By using consistent naming conventions, you can make your data warehouse more readable, understandable, and maintainable. For example, you can use prefixes or suffixes to indicate the type of key, such as SK_ for surrogate key and NK_ for natural key. You can also use descriptive names that reflect the business meaning of the data, such as Customer_SK and Customer_NK.

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Kaushikkumar Patel

Data-Driven Solutions Architect | AWS Solutions | Credit Card Analytics
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During a complicated data integration job in the finance field, I learned how important it is to use consistent naming conventions. Using consistent naming, ?helped us a lot as we moved from testing to production. It made troubleshooting easier and cut down on mistakes that could happen. Along with these rules, I always stress how important it is to have clear documents. Because we are very good at naming things and writing things down, a team member who wasn't familiar with the project could quickly understand and fix a problem.

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3 Use comments and descriptions

Comments and descriptions are the textual annotations that you can add to your data elements to provide additional information and context. By using comments and descriptions, you can explain the purpose, logic, and source of your surrogate keys and natural keys, as well as any assumptions, limitations, or dependencies that affect them. For example, you can use comments and descriptions to specify the source system, table, and column that are used to generate the surrogate key, or the business rule that determines the natural key.

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4 Use audit columns and tables

Audit columns and tables are the data elements that you can use to record the history and changes of your data in a data warehouse. By using audit columns and tables, you can track the creation, modification, and deletion of your surrogate keys and natural keys, as well as the timestamps, users, and actions that are involved. For example, you can use audit columns such as Created_Date, Created_By, Modified_Date, and Modified_By to capture the metadata of your keys. You can also use audit tables to store the snapshots or logs of your keys over time.

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5 Use mapping tables and views

Mapping tables and views are the data elements that you can use to join and query your data in a data warehouse. By using mapping tables and views, you can create and maintain the mappings between your surrogate keys and natural keys, as well as any other attributes that are related to them. For example, you can use mapping tables to store the associations between your keys, such as Customer_SK, Customer_NK, Customer_Name, and Customer_Address. You can also use mapping views to expose the mappings to the users and applications that need them.

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6 Use data quality and validation tools

Data quality and validation tools are the software applications that you can use to check and improve the accuracy, completeness, consistency, and timeliness of your data in a data warehouse. By using data quality and validation tools, you can monitor and verify the integrity and lineage of your surrogate keys and natural keys, as well as identify and resolve any errors, anomalies, or discrepancies that may occur. For example, you can use data quality and validation tools to perform data profiling, cleansing, matching, and reconciliation of your keys.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you maintain data lineage with surrogate keys?

1

2

3

4

5

6

7

1 Use a metadata repository

2 Use consistent naming conventions

3 Use comments and descriptions

4 Use audit columns and tables

5 Use mapping tables and views

6 Use data quality and validation tools

7 Here’s what else to consider

Data Warehousing

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How can you maintain data lineage with surrogate keys?

1

2

3

4

5

6

7

1 Use a metadata repository

2 Use consistent naming conventions

3 Use comments and descriptions

4 Use audit columns and tables

5 Use mapping tables and views

6 Use data quality and validation tools

7 Here’s what else to consider

Data Warehousing

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