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Data is everywhere around you. In a real sense, it influences every aspect of your business. It could feel like there isn’t enough time to examine the specifics of how well it is serving your business when you’re preoccupied with decisions on how to handle your data.
Observe this. Your organization is using data 24 hours a day. So understanding where it came from, how it got there, and how it’s moving through the company is crucial to understanding its worth.
Data lineage becomes important in this situation. It is simpler to comprehend how data was formed, where it came from, and where it is going when we can track the origins, migrations, and changes of the data.
In this post, we will be closely look at Data Lineage, how it works, its use cases, techniques, and much more.
What is Data Lineage?
Data lineage serves as a kind of digital passport. It is the most comprehensive account of a data trip, detailing all of its stops, detours, and modifications from its origin to its eventual destination.
In essence, data lineage describes the origin, modification, and use of a piece of data across many systems and platforms. It functions as a detective’s tool by giving users information about how data was produced, where it originated from, and how it was utilized. This information enables users to recognize and resolve any potential problems.
Data lineage is a priceless resource for companies that depend on data to run their operations because it allows users to respond to crucial questions like who, what, when, and where.
Data lineage is, to put it simply, the ultimate data trail that guarantees data accuracy, completeness, and consistency while offering a clear and succinct perspective of a data’s full path.
How does Data Lineage work?
Data lineage is the road map that enables us to follow a piece of data from its starting point to its endpoint. Consider a data point as a traveler, and its passport to be its data lineage to better understand how it functions.
Data sources, data transformation, data storage, and data output make up the passport’s four primary components.
The many systems, applications, and platforms from which the data originates are represented by data sources, which serve as the beginning points for the data’s journey. Data transformation is the subsequent stage, and data lineage charts the data’s progression from these sources to it.
Data transformation refers to the shaping, modifying, and manipulating of data to meet user needs. It functions as a rest stop during the data’s trip, preparing it for the next leg.
The data is then stored before going to its final location. It could be kept on cloud servers, databases, or some other kind of storage device. Data lineage keeps track of where the data is stored, as well as how it is protected, backed up, and recovered.
The final step is data output, which is where the data is sent to be used. Reports, infographics, or any other type of data product might be used to present it. Data lineage keeps track of the output and guarantees the consistency, accuracy, and completeness of the data.
Data lineage basically works by recording each stage of the data’s journey, from its inception to its output, and making sure that it stays reliable, consistent, and correct all the way through. Data lineage helps organizations to make educated decisions, fix problems, and adhere to legal obligations by giving a full view of a data’s existence.
In order to understand the data assets and how they move through the data pipeline, metadata is a crucial part of the data lineage process.
You can see how data is converted and utilized within the organization using data lineage tools, which leverage metadata to provide a visual depiction of the data flow. This enables users to assess the data’s potential helping them make better-informed decisions.
Types of Data Lineage
There are three basic forms of data lineage: forward data lineage, backward data lineage, and bi-directional data lineage.
Forward Data Lineage
As with a one-way street, forward data lineage involves tracking a piece of data from its starting point to its ending point. Beginning from the data source, it follows the data as it passes through several transformations and storage systems to reach its output.
Understanding the processing and transformation of data as well as any problems that may have arisen along the way are facilitated by having a data lineage of this kind. Every step leads to the next; it’s like following a trail of breadcrumbs.
Backward Data Lineage
Backward data lineage is similar to a voyage in reverse where we trace the data’s output back to its source. The process begins at the data’s final location and moves backward through a variety of storage and transformation techniques until it reaches the data source.
Identification of the data’s original source, comprehension of its transformation, and verification of its correctness and completeness are all possible with the help of this kind of data lineage. It works like a detective’s tool, allowing us to follow the path of the data backward.
Bi-directional Data Lineage
A two-way street, bi-directional data lineage combines the advantages of forward and backward data lineage. It provides a comprehensive view of the route of the data by tracking it from its source to its destination as well as from that location to its starting point.
In order to determine the data’s original source, comprehend how it was altered, and guarantee its quality, consistency, and completeness all along the way, it is helpful to track the data’s lineage. With real-time information on its location and status, it’s like having a GPS tracker for data.
Implementation of Data Lineage
Implementing data lineage in an organization frequently involves the following phases.
Define the data sources
The systems and databases that hold the data you wish to track should all be identified. To do this, you must first identify the various data sources, including files, APIs, and cloud services.
Collect the metadata
The next stage is to acquire details about the data, including its location, format, and organization. Understanding the features of the data and how it is utilized is made possible by this metadata.
Identify data flaws
It is simpler to understand how data is updated and used within the organization if the flow of data is mapped out from its source to its destination, including any transformations or processing that take place along the route.
Track data access
To maintain data security and compliance, track, and record who accesses the data.
Store and visualize the lineage
Utilise visualization tools to present the lineage for simple comprehension and analysis. Store the gathered metadata and data flow information in a single repository.
Implement an automated solution
You can verify data lineage is being gathered and monitored through automation, which will also assist to cut down on mistakes and boost productivity.
Review & Update
Make that the lineage records are correct and current on a regular basis, and update it as appropriate.
The implementation process may need to be modified or added to phases depending on the unique requirements and limits of each organization.
Data Lineage Techniques
Pattern-based Lineage
With this method, lineage is performed without having to interact with the programming that generated or transformed the data. Metadata assessment for tables, columns, and business reports are all part of it. It explores lineage by looking for trends using this metadata.
For instance, it is quite likely that a column in two datasets with the same name and identical data values represents the same data at different phases of its existence. A data lineage chart is then used to connect those two columns.
Pattern-based lineage has the significant benefit of being technology independent because it just checks data, not data processing methods. Any database technology, including Oracle, MySQL, and Spark, can implement it in the same way. The drawback is that this approach isn’t always precise.
When the data processing logic is concealed in the computer code and not readily obvious in human-readable metadata, it can occasionally overlook relationships between datasets.
Lineage by Data Tagging
This method is predicated on the notion that a transformation engine tags or otherwise markers data. It traces the tag from beginning to end in order to find lineage. This approach can only be successful if you have a reliable transformation tool that manages all data transfer and you are familiar with the tagging structure the tool employs.
Even if such a tool were to exist, no data that was created or altered without it could be subjected to lineage via data tagging. It is limited in this regard to performing data lineage on closed data systems.
Self-Contained Lineage
Some businesses have a data environment that includes metadata storage, processing logic, and master data management (MDM). These settings frequently include a data lake where all data is kept throughout its entire lifespan.
Lineage can be naturally provided by this kind of self-contained system without the requirement for additional resources. However, just as with the data tagging method, lineage won’t be aware of anything that occurs outside of this regulated environment.
Data Lineage by Parsing
The most sophisticated type of lineage is one that reads data-processing logic automatically. For thorough, end-to-end tracing, this method reverse engineers the data transformation logic.
Since this solution must comprehend all of the programming languages and tools used to convert and transport the data, its deployment is complicated. This might use extract-transform-load (ETL) logic, SQL- and Java-based solutions, old data formats, XML-based solutions, and other techniques.
Data Lineage Use Cases
Data modeling
Companies must establish the underlying data structures that support them in order to visualize the many data items and the connections between them inside a company. These connections are modeled using data lineage, which also shows the many dependencies present in the data ecosystem.
Since data changes over time, new data sources constantly appear, requiring new data integrations, etc. Because of this, firms’ general data models for managing their data must likewise change to reflect the environment.
Compliance
Data lineage offers a compliance method for auditing, enhancing risk management, and making sure data is kept and handled in accordance with data governance policies and laws.
Impact Analysis
The effects of certain business changes, such as any downstream reporting, can be seen using data lineage tools. Data lineage, for instance, might assist executives in determining how many dashboards a name change would affect and, consequently, how many people access that reporting.
Data migration
Organizations employ data migration to comprehend where the data is located and how long it has been there before shifting it to a new storage system or implementing new software.
Data lineage helps teams prepare for system upgrades or migrations by giving them an overview of how the data has moved throughout the organization. This speeds up the transfer to the new storage environment overall.
Additionally, it gives teams the chance to declutter the data system by archiving or eliminating outdated or useless data. By doing so, the data system will perform better overall and need less management of data.
Challenges of Implementing Data Lineage
- Data Security: Data security is a primary concern while building data lineage. To follow a data journey from its starting point to its final destination, access to sensitive data must be granted, and this data must be protected against unauthorized access and breaches.
- Lack of Standardization: One of the primary barriers to embracing data lineage is the lack of standards. Since many platforms, apps, and systems employ unique methods for tracking and recording data provenance, it can be difficult to piece together a cohesive picture of a data journey.
- Data Silos: Data silos are another issue that arises while implementing data lineage. When data is spread across several applications and systems, it could be challenging to track its journey from one to another. This might lead to inaccurate or incomplete data lineage.
Conclusion
In conclusion, data lineage is an essential part of every data-driven enterprise. It offers a comprehensive perspective of a data’s path from its starting point to its ending point, guaranteeing its accuracy, completeness, and consistency.
Future data lineage automation and standardization are expected to increase, making implementation and maintenance for organizations easier. In the end, the significance of data lineage cannot be emphasized.
It gives companies the tools they need to make wise choices, run their operations more efficiently, and achieve success.
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