What is Data Lineage, Best Practices and Techniques

Data Lineage

Definition of Data Lineage

Data lineage reveals the data life cycle. It aims to show all data flows, from start to end. Data lineage refers to the process of recording, understanding, and visualizing data flow from data sources to consumers. This includes all data transformations that occurred along the way, including how the data was transformed and what changed.

Data Lineage Process

Companies can use data lineage to:

  • Track errors in data processes
  • Process changes can be implemented with less risk
  • System migrations can be done with confidence
  • To create a data mapping framework, combine data discovery and a complete view of metadata to create data discovery

Data lineage allows users to verify that their data comes from trusted sources, has been correctly transformed, and is loaded at the correct location. When strategic decisions are based on accurate information, data lineage is crucial. Data verification becomes difficult, or even very expensive, if data processing are not properly tracked.

Data lineage is about validating data consistency and accuracy by allowing users the ability to search upstream or downstream from source to destination to find anomalies and correct them.

Why is Data Lineage Important?

Understanding the source of a data set is not enough to fully appreciate its significance, perform error resolution, process changes understanding, and system migrations.

Data quality is improved by knowing who made the changes, how they were updated and what was the process used. This allows data custodians and data owners to protect the integrity of data throughout its entire lifecycle.

The following areas can benefit from data lineage:

  • Businesses run smoothly: when they have good data: Data is a key component of all departments including sales, marketing, manufacturing, management, and management. Data can be gathered from field research and operational systems to optimize organizational systems and improve products and services. Data lineage provides detailed information that helps to understand the validity and meaning of these data.
  • Data in flux: data is constantly changing. Management must combine and analyze new methods of collecting and accumulating information in order to create business value. Data lineage allows for the tracking of data, making it possible to reconcile and make best use old and new data.
  • Data Migrations: IT professionals need to know the exact location and lifecycles of data sources in order to move them to new storage equipment. This information is quickly and easily available through Data Lineage, making it easier and less risky to migrate data.
  • Data Governance: The details in data lineage can be used to perform compliance audits, improve risk management, and make sure data is stored and processed according to organizational policies and regulatory standards.

Data Classification and Data Lineage

Data classification The process of classifying data into categories based on user-configured characteristics.

A key component of an information security program is data classification. This is especially important when large volumes of data are stored. It helps to understand the location of sensitive or regulated data, and provides a solid foundation for data security strategies.

Data classification can also improve productivity and decision-making, eliminate unnecessary data, and lower storage and maintenance costs.

Combining data lineage and data classification can make data classification even more powerful.

Data classification is used to locate sensitive, confidential, and business-critical data.
Data lineage tools are available for each dataset. They can be used to examine its entire lifecycle, identify integrity and security issues, as well as resolve them.

Data Lineage Techniques and Examples

These are some common methods for performing data lineage on strategic datasets.

Pattern-Based Lineage

This technique does not deal with the code that generated or transformed the data. This involves evaluating metadata for tables, columns and business reports. It uses this metadata to investigate lineage and look for patterns. If two datasets have a column that has a similar name and data values, it’s very likely that these are the same data at two different stages of its lifecycle. These two columns can then be linked in a data lineage diagram.

Pattern-based lineage has the advantage that it monitors data and not data processing algorithms. It is, therefore, technology-neutral. It can be used across any database technology, including Oracle, MySQL, and Spark.

This method isn’t always reliable. It can sometimes miss connections between data sets, particularly in some cases. If the data processing logic is not visible in the metadata, it is possible that it is hidden within the programming code.

Lineage by Data Tagging

This technique assumes that a transformation engine tags data or marks it in some way. It tracks the tag from beginning to end in order to determine lineage. This method works only if there is a consistent tool for transformation.You are in control of all data movement and the tagging structure used.

Even if such tools exist, data lineage via data tag cannot be applied to data that has not been generated or modified without the tool. It is therefore only applicable to data lineage in closed systems.

Self-Contained Lineage

Many organizations have a data environment which provides storage, processing logic and master data management (MDM), for central control of metadata. These environments often contain a data lake, which stores all data at all stages of their lifecycle.

This self-contained system can provide lineage without the need to use external tools. As with data tagging, lineage won’t be aware of any events outside the controlled environment.

Lineage by Parsing

This is the most advanced type of lineage and relies on automatically reading data processing logic. This technique reverse-engineers data transformation logic to perform extensive, end-to-end tracing.

Because it must understand all programming languages and tools required to transform and move data, this solution can be difficult to deploy. This might include extract-transform-load (ETL) logic, SQL-based solutions, JAVA solutions, legacy data formats, XML based solutions, and so on.

Data Lineage for Data Processing, Ingestion, and Querying

You must keep track of all processes that convert or process data when building a data linking system. Each stage of data transformation must be mapped. It is important to track tables, views and columns across different databases and ETL jobs.

This can be done by collecting metadata for each step and storing it in a metadata repository that can then be used to perform data lineage analysis. Automated data lineage analysis across Databases and ETL environments.

This is how lineage works across the different stages of a data pipeline.

Data Ingestion – Tracking data flow in data ingestion jobs and checking for errors or mappings between source and destination systems.
Data processing – Tracking specific operations on data and their results. The data system may read a text file and apply a filter to count values in a particular column before writing to another table. Every stage of data processing is examined separately to find errors and security/compliance violations.
Query history – allows users to track their queries and generate automated reports from databases and data storage. You can perform operations such as joins and filters to create new datasets. It is important to verify that the process data passes through data lineage when performing queries or reports. Users can optimize their queries by using lineage data.
Data lakes – Tracking user access to various objects or data fields and identifying security and governance issues. Because of the large amount of unstructured information, these issues can be difficult to enforce in large data lakes.

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