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What are the best ways to assess data lineage in an ETL tool?

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Data lineage is the process of tracing the origin, transformation, and destination of data in an ETL (extract, transform, load) tool. It is essential for ensuring data quality, integrity, and governance in a business intelligence (BI) system. However, assessing data lineage can be challenging, especially when dealing with complex and dynamic data sources, transformations, and targets. In this article, you will learn about some of the best ways to assess data lineage in an ETL tool and how they can benefit your BI projects.

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Mihaela Stojkovi?

Business Intelligence Consultant at Koios Consulting Ltd
Ayoade Adegbite

Analytics Engineer | Data Analyst | Tableau & Airflow Certified | Data Storytelling | Data Analytics Tutor | Oct 23'…

1 Use a metadata repository

A metadata repository is a centralized database that stores information about the data sources, transformations, and targets in an ETL tool. It can provide a comprehensive and consistent view of the data lineage across the entire ETL process. By using a metadata repository, you can easily access, query, and visualize the data lineage for any data element, transformation, or target. You can also use it to perform impact analysis, data quality checks, and data governance audits.

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Mihaela Stojkovi?

Business Intelligence Consultant at Koios Consulting Ltd
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This method has proven to be incredibly helpful, making various processes more efficient. Specifically, it has sped up tasks such as creating DSA layers and generating tables with the same structure as the source tables. We achieved this through research in ODI (Oracle Data Integrator) work repository. It involves extracting essential data that describes our processes and transforming it into a valuable resource. Data lineage can be challenging, especially when dealing with complex calculations in dataflow. I had the opportunity to see this method in action during a bank's transition to a new local currency. Structural changes were required, and we needed to identify which tables needed adjustments and assess the impact of those changes.

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Ayoade Adegbite

Analytics Engineer | Data Analyst | Tableau & Airflow Certified | Data Storytelling | Data Analytics Tutor | Oct 23' DataFam Community Spotlight | Top Business Intelligence (BI) Voice 24' | Airflow Champion 24'
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Assessing data lineage in an ETL tool involves leveraging built-in features, reviewing source-to-target mappings, understanding transformation logic, tracing data flow, and considering specialized tools for visualization. ETL tools known for their data lineage capabilities include Apache NiFi, Informatica, Talend, Apache Atlas, etc. You can also implement a custom data lineage within Airflow by creating a custom Python script or using libraries to capture and visualize the metadata within your Airflow environment.

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2 Implement data lineage standards

Data lineage standards are a set of rules and guidelines that define how to document, label, and manage the data lineage in an ETL tool. They can help you to ensure that the data lineage is clear, accurate, and consistent throughout the ETL process. By implementing data lineage standards, you can improve the readability, traceability, and reliability of the data lineage. You can also use them to facilitate collaboration, communication, and compliance among the ETL developers, analysts, and stakeholders.

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Ayoade Adegbite

Analytics Engineer | Data Analyst | Tableau & Airflow Certified | Data Storytelling | Data Analytics Tutor | Oct 23' DataFam Community Spotlight | Top Business Intelligence (BI) Voice 24' | Airflow Champion 24'
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In my experience, I usually begin by defining the scope and objectives of the data lineage efforts. Then clearly outline what aspects of data flow will be tracked and specify the desired outcomes. After, I Implement a governance framework to assign clear roles and responsibilities for managing data lineage documentation and maintenance. I also leverage specialized data lineage tools or platforms that streamline the documentation, visualization, and management of data lineage. These tools often offer features for automatic lineage tracking, reducing manual efforts. Create standardized templates for documenting data lineage.

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3 Use data lineage tools

Data lineage tools are software applications that automate the extraction, analysis, and visualization of the data lineage in an ETL tool. They can help you to save time, effort, and resources in assessing the data lineage. By using data lineage tools, you can quickly and easily discover, map, and monitor the data lineage for any data element, transformation, or target. You can also use them to identify and resolve data quality issues, data dependencies, and data anomalies.

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4 Review and test the data lineage

Reviewing and testing the data lineage is the process of verifying and validating the accuracy, completeness, and correctness of the data lineage in an ETL tool. It is a crucial step for ensuring the quality and integrity of the data lineage. By reviewing and testing the data lineage, you can detect and fix any errors, inconsistencies, or gaps in the data lineage. You can also use it to evaluate the performance, efficiency, and scalability of the ETL process.

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5 Document and communicate the data lineage

Documenting and communicating the data lineage is the process of creating and sharing the data lineage documentation and reports in an ETL tool. It is an important step for ensuring the transparency and accountability of the data lineage. By documenting and communicating the data lineage, you can provide a clear and comprehensive overview of the data lineage to the ETL developers, analysts, and stakeholders. You can also use it to support decision making, reporting, and auditing activities.

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6 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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Ayoade Adegbite

Analytics Engineer | Data Analyst | Tableau & Airflow Certified | Data Storytelling | Data Analytics Tutor | Oct 23' DataFam Community Spotlight | Top Business Intelligence (BI) Voice 24' | Airflow Champion 24'
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Providing training and education on data lineage standards to relevant stakeholders, including data engineers, analysts, and business users is also important as the tools used for data lineaging. Also, ensuring that they understand how to access and interpret the documentation can empower them to make informed decisions regarding data flow.

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What are the best ways to assess data lineage in an ETL tool?

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3

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1 Use a metadata repository

2 Implement data lineage standards

3 Use data lineage tools

4 Review and test the data lineage

5 Document and communicate the data lineage

6 Here’s what else to consider

Business Intelligence

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What are the best ways to assess data lineage in an ETL tool?

1

2

3

4

5

6

1 Use a metadata repository

2 Implement data lineage standards

3 Use data lineage tools

4 Review and test the data lineage

5 Document and communicate the data lineage

6 Here’s what else to consider

Business Intelligence

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