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How can you profile and catalog data to ensure data lineage and provenance during migration?

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Data migration is the process of moving data from one system or platform to another, often for reasons such as upgrading, consolidating, or modernizing. However, data migration can also pose challenges and risks for data quality, integrity, and governance. How can you profile and catalog data to ensure data lineage and provenance during migration? Data lineage and provenance are the metadata that describe the origin, history, and transformations of data, and they are essential for data trustworthiness, compliance, and auditability. In this article, we will discuss some data migration tools and techniques that can help you profile and catalog data to ensure data lineage and provenance.

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Krishna Baluswami

Experienced Data and Analytics Leader | Driving Business Success through Strategic Solutions and Thought Leadership

1 Data profiling

Data profiling is the first step in data migration, and it involves analyzing the structure, content, and quality of the source data. Data profiling can help you identify data types, formats, values, ranges, patterns, anomalies, dependencies, and relationships. Data profiling can also help you assess the complexity, completeness, and accuracy of the source data, and identify any issues or gaps that need to be addressed before migration. Data profiling tools can automate the process of scanning, sampling, and summarizing the source data, and generate reports and statistics that can inform your data migration strategy and design.

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Krishna Baluswami

Experienced Data and Analytics Leader | Driving Business Success through Strategic Solutions and Thought Leadership
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This article effectively highlights the significance of data profiling in data migration projects. However, in practical situations, I have rarely witnessed project teams or stakeholders regarding data profiling as a feasible choice; instead, they view it as a potential project delay. If we have a robust data governance team in place with active engagement, we can potentially prioritize data profiling as an initial step. Otherwise, the primary focus tends to be on expediting the data migration process to achieve quicker completion.

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2 Data cataloging

Data cataloging is the process of creating and maintaining a centralized repository of metadata that describes the data assets in your organization. Data cataloging can help you discover, document, and classify the data sources, schemas, tables, columns, fields, keys, indexes, constraints, and other attributes. Data cataloging can also help you enrich the metadata with business terms, definitions, descriptions, tags, labels, ratings, comments, and other annotations. Data cataloging tools can automate the process of extracting, ingesting, and updating the metadata from various data sources, and provide a searchable and browsable interface for data discovery and governance.

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Krishna Baluswami

Experienced Data and Analytics Leader | Driving Business Success through Strategic Solutions and Thought Leadership
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I recommend following this sequence of steps for data migration to ensure a smooth and successful process: Data Cataloging, Data Provenance, Data Lineage, Data Profiling, Data Modeling, Data Mapping, Data Migration. This sequential approach ensures a well-organized and efficient data migration, minimizing errors and enhancing the chances of a successful outcome.

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3 Data lineage

Data lineage is the metadata that tracks the flow and transformation of data from its source to its destination, and shows how the data is used, moved, changed, and affected by different processes, applications, systems, and users. Data lineage can help you understand the origin, history, and context of the data, and how it relates to the business objectives and requirements. Data lineage can also help you ensure the data quality, integrity, and consistency throughout the migration process, and verify the accuracy and completeness of the migrated data. Data lineage tools can automate the process of capturing, mapping, and visualizing the data lineage across multiple data sources, platforms, and environments.

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Aleksander Magda

Data Tech Lead @ IndieBI | Data Architecture, AI, Games
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Understanding data lineage is like tracing your family tree, but for your data. With tools like Azure Purview and Apache Atlas, you can easily see where your data comes from and how it changes over time. Why is this crucial? Because without this clarity, you're flying blind during data migration. You risk data loss, quality issues, and even compliance breaches. In simple terms: know your data's journey with the right tools, or face the potential pitfalls of misinformation.

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4 Data provenance

Data provenance is the metadata that records the source, ownership, and authority of the data, and shows who created, modified, accessed, or deleted the data, and when, where, why, and how they did so. Data provenance can help you establish the trustworthiness, validity, and reliability of the data, and how it complies with the policies, regulations, and standards. Data provenance can also help you monitor and audit the data activities and events during and after the migration process, and detect and resolve any issues or incidents. Data provenance tools can automate the process of collecting, storing, and querying the data provenance information from various data sources, platforms, and systems.

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5 Data mapping

Data mapping is the process of defining the rules and logic that govern how the data is transferred, converted, and transformed from the source to the target. Data mapping can help you align the data structures, formats, types, values, and attributes between the source and the target, and handle any differences, conflicts, or inconsistencies. Data mapping can also help you apply any data cleansing, validation, enrichment, or integration operations that are needed to improve the data quality and usability. Data mapping tools can automate the process of designing, testing, and executing the data mapping tasks, and generate the data mapping documentation and code.

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6 Data migration

Data migration is the final step in data migration, and it involves moving the data from the source to the target, according to the data mapping rules and logic. Data migration can be performed in different modes, such as full, incremental, or delta, depending on the volume, frequency, and timing of the data movement. Data migration can also be performed in different methods, such as online, offline, or hybrid, depending on the availability, performance, and security of the data sources and targets. Data migration tools can automate the process of scheduling, running, and monitoring the data migration jobs, and provide the data migration logs and reports.

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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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Krishna Baluswami

Experienced Data and Analytics Leader | Driving Business Success through Strategic Solutions and Thought Leadership
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A metadata management tool can make a substantial contribution to the success and expeditious execution of data migration projects. This tool effectively maintains crucial components such as the data catalog, data lineage, data provenance, and even aids in certain aspects of data mapping. Furthermore, when combined with data quality management, the metadata management tool can significantly accelerate the data migration process, ultimately leading to higher ROI on various migration or upgrade projects.

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How can you profile and catalog data to ensure data lineage and provenance during migration?

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1 Data profiling

2 Data cataloging

3 Data lineage

4 Data provenance

5 Data mapping

6 Data migration

7 Here’s what else to consider

Data Architecture

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How can you profile and catalog data to ensure data lineage and provenance during migration?

1

2

3

4

5

6

7

1 Data profiling

2 Data cataloging

3 Data lineage

4 Data provenance

5 Data mapping

6 Data migration

7 Here’s what else to consider

Data Architecture

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