While making data-driven decisions, businesses are often bothered by questions like – Where is the data coming from and where is it going? Here is exactly where data lineage comes into play. It helps businesses decipher all the relevant information around their data and offers clarity on how data flows across their organization.
In this blog, we shall cover all the crucial aspects related to data lineage:
- Meaning of data lineage
- Benefits of data lineage
- Data lineage techniques
- Data lineage best practices
What is data lineage?
Data lineage is the process of comprehending, recording, and visualizing data as it flows from data sources to end-users. Simply put, it helps you gain a granular view of how data flows from source to destination.
With data lineage, businesses can receive continuous updates on how the data is moving through the organization, how is it getting transformed, where is it being stored, and various other metadata.
Organizations can leverage the power of data lineage to conduct system migrations, track errors in data processes, bring data discovery and metadata closer, and reduce risks.
In a nutshell, data lineage is all you need to facilitate data accuracy and consistency. One thing that you must note is that metadata management is crucial for presenting data lineage across on-premises and the cloud. When you manage metadata effectively, it enables you to view data flow via different systems. This makes it easier to identify data concerning a particular report or ETL process.
Benefits of data lineage
Data lineage is like a boon for IT teams. It helps them gain a 360-degree view of the journey of data. Data lineage has powerful impacts on several business areas. Let’s have a look at some of them:
1. Enables effective data governance
With data lineage, businesses can easily map and verify how data is accessed and transformed. This further helps in maintaining regulatory compliance, data quality, data privacy, and other important aspects related to data governance.
2. Facilitates successful data migration
Data lineage uses data graphs to present the relation between various data objects and data flows. It makes it easier for data architects to move data to new systems as data lineage helps them gain a fair idea of the location and lifecycle of data sources.
3. Boosts business productivity
Data lineage tracks how all the sensitive data flows throughout the organization and helps businesses quickly identify and fix data gaps. This further helps in enhancing business products and services.
4. Generates accurate analytics
When it comes to IT operations, data lineage presents the impact of data changes on downstream analytics. It helps you identify the business risks associated with the changes and adopt a proactive approach to change management.
Data lineage techniques
Here’s a list of a few standard techniques that businesses can utilize to conduct data lineage:
1. Pattern-based lineage
This technique leverages patterns to perform data lineage. Pattern-based lineage does not deal with the code that is used to transform data. It uses metadata for tables, columns, and business reports. This technique only monitors data and not the data processing algorithms.
2. Lineage by parsing
It is one of the most advanced data lineage techniques. It tends to automatically read the logic that is used to process data. Lineage by parsing has a complex deployment. You need to comprehend different tools and programming languages like ETL logic, SQL, Java solutions, and more.
3. Lineage by data tagging
In this technique, every data that moves or transforms gets tagged by a transformation engine. Lineage by data tagging requires a consistent transformation tool to control all the data movement. To produce a lineage presentation, all tags are read from start to finish.
4. Self-contained lineage
This technique can inherently present lineage and does not require any external tools. There are certain data environments that comprise data lakes. The data lake can store all kinds of data in all stages of its lifecycle.
Data lineage best practices
Let’s have a quick look at some of the best practices that you should follow while implementing data lineage for your business:
1. Leverage automation for data lineage extraction
Avoid capturing lineage manually in static tools. Use intelligent data lineage tools to automate processes. We recommend using Informatica’s Enterprise Data Catalog to gain insights into single-step transformations. Thanks to its AI and ML capabilities, Informatica EDC can automatically stitch lineage together from various enterprise sources.
Our tool is built using Apache Spline, Python, and Informatica EDC which extracts data lineage from Spark job execution and visualizes the same on Informatica EDC.
Here’s a graphical representation to show how LumenData’s tool helps you extract data lineage:
2. Add the metadata source to the data lineage
Relational database management systems, ETL software, custom applications, and BI tools tend to create their data. This metadata should be included in your lineage as it helps in comprehending the data flow.
3. Choose progressive extraction
Make sure to extract data lineage and metadata in the same way as data flows through your system. Progressive extraction makes it easier to map out the connections and dependencies among systems.
4. Ensure to validate end-to-end data lineage
Make sure to validate data lineage progressively. Start from high-level connections between systems. Next, move to connected data sets followed by data elements. Lastly, validate the transformation documentation.
Enable data processing optimizations with data lineage
We hope our blog helped you understand the critical concepts surrounding data lineage. With AI-powered data lineage capabilities, it becomes easier to comprehend beyond data flow relationships. Data lineage helps you avoid errors in business intelligence reports and data sources.
Get in touch with our team to learn more about using data lineage tools effectively.
Senior Data Scientist
Senior Data Scientist