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What is data tracing and how do you test it?

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Data tracing is a technique to track the flow and transformation of data from its source to its destination. It helps data engineers to verify the accuracy, quality, and consistency of the data, as well as to identify and troubleshoot any issues or errors that may occur along the way. In this article, you will learn what data tracing is, why it is important, and how you can test it using different methods and tools.

本文章的要点总结

Automated data tracing:

Implement software tools that automatically track data as it moves and changes, saving you significant time and ensuring accuracy. They're fast, handle large volumes of data, and can provide real-time insights into the data's journey.
Tailored tracing:

Focus your data tracing efforts on critical areas like specific database fields or ETL processes. This targeted approach helps you understand the impact of changes quickly and upholds compliance with ease.

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1 Data tracing vs data lineage

Data tracing and data lineage are often used interchangeably, but they are not exactly the same. Data lineage is the documentation of the data's origin, history, and dependencies, while data tracing is the process of following the data's movement and transformation across different systems and stages. Data lineage provides the context and metadata of the data, while data tracing provides the evidence and validation of the data. Both are essential for data governance, compliance, and quality assurance.

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Ankit Yadav ????

Top Voice | Consultant at Deloitte?? | Data Engineer | Ex-LTTS | 3x Azure Certified | 2x DataBricks Certified |Transforming Data into Insights for Impact ??
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Data Lineage is more about understanding the historical and dependency context of data, focusing on its origins and relationships. - Documentation of data's origin, history, and dependencies. - Provides context and metadata about the data. - Focuses on understanding where the data comes from, its path, and its relationships. Data Tracing is about actively following the data's journey in real-time, ensuring its accuracy and reliability at every stage - Process of tracking the movement and transformation of data across systems and stages. - Offers evidence and validation of how data changes during its journey. - Emphasizes following the data's flow and ensuring its accuracy and reliability.

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Pankaj Gupta

Enterprise Technology Leader ★ Data and Analytics Engineering at Discover Financial Services ★ Ex - JP Morgan ★ Tech Evangelist ?? / 2 x AWS Certified | Fellow BCS | Fellow IETE | Sr Member IEEE
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Data tracing, as the name implies, involves tracking the movement of data as it enters and exits an organization. This process can be tailored to specific areas, such as understanding how a field in a table is utilized within various queries or how a field from a file is read and transformed in downstream flows. For instance, from a database to an ETL process, followed by a report generation, and then extending externally to vendors. This comprehensive tracing proves invaluable when assessing the downstream impacts of changes, such as altering the length of a field from x to y. Additionally, data tracing serves regulatory purposes, ensuring compliance and transparency in data handling practices.

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Azim Mohamed

Senior Software Engineer @ IFS | Data Science | Business Intelligence
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Data tracing is tracking and documenting how data moves through a system. To test it: -Review documentation. -Analyze data characteristics. -Validate source-to-destination flow. -Map dependencies. -Implement data quality checks. -Conduct regression and automated testing. -Establish audit trails for changes. -Test different scenarios. These steps ensure accurate data flow, quality, and system integrity.

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Anindya Roy Choudhury

Data Architect | Data Engineer | Engineering Manager
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A great example of data tracing is an informatica mapping. In one of the projects which I worked on, there were Sybase tables as the source, transformation to the data happening using informatica mapping. And finally the data was loaded to the data mart. The informatica mappings were run using CA unicenter. Whenever there was an error in the final table. We did not have the fancy terminologies during that time. But the way we did our analysis was nothing but data tracing. Following are some of the steps which I followed:- 1. Get an example data in the source tables itself. 2. List the transformations in the informatica mapping one by one. 3. Each transformation's function were converted to SQL queries. 4. Finally comparing the target data.

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2 Data tracing challenges

Data tracing can be challenging for several reasons. First, data can come from multiple sources, such as databases, files, APIs, or streams, and have different formats, schemas, and standards. Second, data can undergo various transformations, such as cleansing, filtering, aggregating, or joining, and be stored in different locations, such as data warehouses, data lakes, or data marts. Third, data can be accessed by different users, applications, or services, and have different security and privacy requirements. These factors make it difficult to trace the data's end-to-end journey and ensure its integrity and reliability.

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3 Data tracing methods

Data tracing can be performed using different methods, based on the type, complexity, and volume of the data, as well as the tools and frameworks available. Manual data tracing involves manually inspecting the data sources, destinations, and intermediate steps; however, this method is time-consuming and not scalable for large or dynamic data sets. Alternatively, automated data tracing involves using software tools or platforms that can capture, monitor, and visualize the data flow and transformation; this method is fast and accurate but requires more investment. A hybrid approach may also be used to take advantage of both manual and automated methods, although this requires more coordination.

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4 Data tracing tools

Data engineers have access to different tools for data tracing, each with its own set of features, functionality, and compatibility. A data catalog is a centralized repository that stores and organizes the metadata and lineage of the data assets in an organization. It assists data engineers in discovering, understanding, and documenting the data sources, destinations, and transformations, as well as searching, browsing, and accessing the data. A data pipeline is a set of processes that move and transform data from its source to its destination. It helps automate, orchestrate, and monitor the data flow and transformation, as well as handle errors, failures, and retries. A data quality tool measures, evaluates, and improves the quality of the data. It assists in validating, verifying, and reconciling the data to identify any issues or anomalies. Lastly, a data tracing tool specifically tracks and traces the data's movement and transformation across different systems and stages. It assists in visualizing, analyzing, and auditing the data to generate reports, alerts, and dashboards.

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5 Data tracing best practices

Data tracing can be made more effective and efficient if data engineers adhere to some best practices. This includes defining the scope and objectives of the data tracing, establishing standards and conventions for the data tracing, utilizing appropriate methods and tools based on the type, complexity, and volume of the data, testing and validating the data tracing, as well as communicating and collaborating with stakeholders. Furthermore, it is important to share the data tracing results and insights with the relevant parties.

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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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What is data tracing and how do you test it?

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1 Data tracing vs data lineage

2 Data tracing challenges

3 Data tracing methods

4 Data tracing tools

5 Data tracing best practices

6 Here’s what else to consider

Data Engineering

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What is data tracing and how do you test it?

1

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6

1 Data tracing vs data lineage

2 Data tracing challenges

3 Data tracing methods

4 Data tracing tools

5 Data tracing best practices

6 Here’s what else to consider

Data Engineering

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