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Why is data lineage important?

Data lineage provides a full overview of how your data flows through the systems of your environment via a detailed map of all direct and indirect dependencies between data entities within the environment. This gives you a greater understanding of the source, structure, and evolution of your data.

Why is data lineage important?

Growing data complexity makes it increasingly difficult to maintain full visibility and control over data environments. As a result, incidents can occur without any insight into what caused them, negatively impacting your business and user experiences.

Data lineage provides a full overview of how your data flows throughout the systems of your environment. This gives you a greater understanding of the source, structure, and evolution of your data for a complete view of how and where the data is moving and what the impacts could be, helping you prevent incidents before they occur.

What are some examples of data lineage?

Examples of the types, sources, and processes of data lineage include:

Pattern-Based Data Lineage: This technique reads metadata about tables and columns and uses the information to create links representing possible data flows from common patterns or similarities. Pattern-based lineage is the best approach for identifying manual data flows happening outside the system—like copying data to a flash drive, modifying it on another computer, or storing it on a different part of the system.

Design Lineage: This technique looks directly into the code that processes and transforms data records. This is “code” in the broadest sense—SQL script, a PL/SQL stored procedure, a macro in an Excel spreadsheet, or the mapping between a field in a report and a database column or table. Parsing and reverse engineering this code is much tougher than parsing log files, and it requires specialized scanners for all supported technologies. This diversity gives design lineage the advantage as the best approach for eliminating data blind spots.

Run-Time Lineage: This technique relies on run-time information extracted for the data environment. Run-time lineage is valuable for incident resolution because it provides accurate information about the flow of a specific data element. However, it only captures information about recently executed data flows and can be lacking in transformation details because not everything can be logged, especially if you’re dealing with more complex algorithms.

Manual Data Lineage: The process of manual data lineage analysis starts at the top with your people and documenting the knowledge in their heads. Then, it’s inputting that information into spreadsheets or other straightforward mapping mechanisms so the lineage can be defined. Manual data lineage analysis is done using code as a source, which is analyzed manually.

Self-Contained Data Lineage: This approach uses logs as a source and a tool that fully controls every data movement for full insight into the whole data processing workflow. It is limited to the controlling platform, meaning anything that happens outside the controlled environment is invisible, which can be limiting.

External Automated Data Lineage: This approach can use data, logs, or code as its primary source, and it is versatile enough to combine sources and approaches. This versatility allows it to be adjusted based on level of understanding and user needs. It is fully automated, plus it has external analysis designed to handle the diversity of your data environment. It doesn’t require that all the data processing be done on one platform or by one tool, and it’s not reliant on one source.

What is the purpose of data lineage?

Data lineage helps you tame data complexity and gives you a full overview of how your data moves across systems, where it originates from, how it transforms along the way, and how it’s interconnected. Such an overview will help you boost your data governance efforts, increase overall trust in data, achieve full regulatory compliance, accelerate root cause and impact analyses, roll out our frequent bug-free releases, painlessly migrate to the cloud, and more.

Why is it important to track data flow?

Data flow is the movement of data throughout your environment—its transfer between data sets, systems, and/or applications. Having full visibility into how your data flows through the systems of your environment not only gives you a greater understanding of the sources, structure, and evolution of your data but also helps you harness data complexity, see through data blind spots, and prevent incidents before they occur. This is the insight data lineage can provide.

How do you build a data lineage solution for databases?

Building a data lineage solution for databases encompasses mapping the various data elements—the files, tables, views, columns, and reports within databases—and the processes and algorithms they go through. As an automated data lineage platform, MANTA can connect to a database, scan the metadata, and read all the programming code and logic stored in it. Using this information, MANTA creates a detailed visualization of the data lineage that can be pushed to any third-party metadata management solution or viewed in MANTA’s native visualization.

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