Importance of Data Lineage
What Is Data Lineage?
- Data lineage, in simpler terms, can be called the history of the piece of data. Data lineage is the process of tracking the data right from the moment it is created to the most recent action taken on it.
- Data lineage gives you a map of the entire lifecycle of your system migrations.
- It tracks the complete data flow from end to end, the various transformations along the way, the users and systems that worked on the datasets, and more.
- Data lineage tools offer a forensic look at the data's journey from its origin to its current state, why each data operation happened, who approved it, and every detail about each data transformation.
- Data lineage answers the hows and whys of data operations throughout the flow of data migration.
- This is where data lineage differs from data provenance.
- Data provenance helps visualize the data lifecycle via flow diagrams and other forms of graphical mapping.
- Data provenance provides a complete list of the origin, systems, and processes relevant to a piece of data.
- While data provenance does give all the information you need, it is data lineage that helps make better sense of this information.
Importance of Data Lineage
Data engineers should be very well aware of auditing and regulatory compliance requirements when dealing with data integration. With data lineage, it would be easier to perform proper data auditing.
- Data lineage ensures that your data comes from verified sources and can be trusted.
- Not just the source, it also helps you track every bit of the transformation process your enterprise data undergoes, so you always have accurate information when making your decisions.
- What could be a time-consuming process of data verification and validation can be made more effective and optimized using data lineage.
Here are some of the crucial benefits of data lineage:
Helps with compliance
adherence.
End-to-end lineage is vital to establish the credibility of your data usage. For instance, your tax auditing will be considered incomplete if it does not include data lineage information.
Similarly, you would also need to report on the origin and data transformations to adhere to the privacy regulations when dealing with sensitive data like that of healthcare. Data lineage is a great way to prove that data has not been breached at any point in time.
Performs better impact analysis.
Data lineage can help data scientists spot data issues easily, providing a better picture of the various stages in the data's lifecycle.
This helps you quickly identify the issue's root cause and resolve it through reliable data analytics.
Enables data governance.
Data lineage makes it a lot easier to implement and install a data governance platform and carry out the ultimate goals of your data governance programs.
It is also important to remember that the GDPR requires companies to focus on data lineage.
Improves data quality assurance and trust.
With data lineage, companies will be able to prove the legitimacy of their data. It helps improve your quality control processes, identify sources of error, and ensure the data handling is always tracked and monitored so that all unauthorized access is curtailed.
It provides a way to show the relevant stakeholders that data has never been tampered with and thus improves the trust standing of your company. It also helps you make better decisions, leading to effortless business intelligence.
Analyzes data updates and changes.
Data may not stay the same throughout its lifecycle. All the various changes and updates in data sources, transformation techniques, formats, and more can be analyzed and improved by using data lineage.
Data Lineage Techniques
- Pattern-based lineage
- This is a technology-agnostic technique that can be used across various databases.
- This method analyzes data and leaves out the data processing algorithms. Hence, it is only sometimes considered to be accurate.
- Any data connection not presented in the metadata gets lost, and thus this method may not be applicable in cases where coding is heavily used.
2. Data tagging
- This technique uses marker tags tracked through the data processes.
- But it is practically only viable in closed data systems and is thus only applicable for data transformations using a consistent data transformation tool.
3. Self-contained systems
- These types of lineage systems are basically an established environment where all information regarding data in all stages of its lifecycle is stored.
- Anything that happens outside of this environment will not be tracked by the data lineage.
4. Parsing
- The most commonly used, most complex, and most advanced technique of data lineage is parsing.
- In this technique, data transformation logic is used to reverse engineer and figure out the data updates that could have happened so far.
5. Coarse and fine-grained lineage
- Based on the output of data lineage, a data lineage system can be considered coarse-grained or fine-grained.
- Coarse-grained data outputs show the connections between databases, tables, and data pimples.
- Fine-grained provides a deeper picture of data connections and data flows.
6. Active vs. lazy data lineage collection
- The data lineage system can be classified as active or lazy, depending on how data collection works.
- The lazy collection involves only coarse grain lineage during run time. To capture fine-grained data, you would need to run a replay.
- As for active lineage collection, you can collect both fine grain and coarse grain lineage at run time.
7. Querying techniques
- Queries can be used to run both backward and forward tracing to find either inputs or outputs.
- Forward tracing queries help evaluate outputs based on the inputs they are derived from.
- Similarly, backward tracing queries help evaluate inputs based on the outputs that are derived from them.
How to Use Data Lineage for Your Data Systems
If you want your data lineage solution to be complete, it needs to have access to every data process and should be able to keep track of the various entities like the ETL logic, tables, views, databases, and more.
To simplify it, store the metadata from each data lifecycle stage and use it for data lineage analysis. This involves setting up the necessary operations and tasks at the crucial data stages, such as data ingestion, data processing, query history, and data lake operations.
Follow these steps to facilitate data lineage:
- Identifying the critical data elements in a data warehouse that are necessary for the business to function smoothly
- Tracking the source and origin of each of the specified data elements
- Prepare a mapping of the various sources, data elements, and their interrelationships for your systems and the entire data landscape.
- You should pay attention to metadata management by using automation
- Make sure your data lineage storages can be scaled up or down depending on the requirements and in alignment with your data system
How to Choose a Data Lineage Tool?
Here are some factors business users should focus on when choosing a data lineage tool:
- Look into the data governance capabilities. This needs much consideration as you must carefully evaluate the tool with real-world use cases.
- Examine its data logging features. This includes generating detailed data catalogs and metadata management.
- Check for cloud-based data auditing. Check the deployment methods, support for cloud-based applications, and integration with third-party applications