The first topic we discuss MLOps is the data we use. Data in its many forms holds the key to the success of any data science, machine learning, or automation project.
Data defines a machine learning pipeline’s beginning, goal, and execution. Any changes or limitations on the acquisition or even the potential of the data are directly correlated to how your model will behave while training and even post-deployment. This creates a massive demand for understanding where this data is coming from, its legal and ethical acquiring, where it ends up, whether it is being used correctly, and more.
This creates a need for systems and tools to track this “lineage” of data and ensure its perfect use in every sphere it touches. So let us explore the need for data lineage and why you or your company needs to know the road traveled by a data segment in your business and technical pipeline.
What is Data Lineage?
Data lineage is a map of where and why your data has traveled in the multiple steps and pipelines throughout the overall business pipeline of your company or venture. This tracking aims to clarify any unethical use of user’s/company’s data and ensure only the required eyes get to see the data. This step is crucial for using your metadata throughout the cloud and helps avoid any issues.
Data Lineage is extremely useful in keeping a record of changes to the overall structure of the data and why it was introduced in the first place. This information helps tackle many data-related issues instantly, like error resolution, process changes, and hardware migration in the case of inferior systems. Let us look at some spheres of your pipeline where data lineage can have a significant impact:
1. Strategic Influence: Most departments in a typical startup or company rely heavily on data and knowing the origin and alterations done to the metadata to conserve strategic interests that affect consumer strategies.
2. Dynamic Data: Thanks to current world situations, data has been highly active due to various natural and artificial changes and crises. Data Lineage ensures the best utilization of the old data in addition to the new data.
3. Data Migration: Whenever there is a need to migrate existing data banks to, say, better storage or hardware.
What are the techniques of Data Lineage?
The significance of Data Lineage comes with the need to develop techniques to help us achieve them. The upcoming methods have been used and thought over since the data boom. So let us take a look at them:
1. Pattern-Based Lineage: This technique establishes a lineage for the data without touching or utilizing coding to transform, edit or omit the database. It mainly involves looking at different data objectively and finding patterns among them. Once these relationships are established, we can further map them on data lineage graphs.
Despite its code-agnostic nature, the method also brings up some disadvantages that could have been avoided by simply including SQL scripts. Pattern recognition depends on the human’s ability to recognize it and may not work well if the pattern generated is heavily machine oriented.
2. Data Tagging: This technique works on creating tags during the transformation step. The tracking ID helps track the data sample anywhere during the lineage cycle. However, this method works well only if we follow a uniform transformation pattern for the plethora of data.
Because of this very reason, this lineage technique cannot be blindly applied to any data collection system and asks for a closed data system in which we can control the input.
3. Lineage by parsing: This is the most sophisticated kind of lineage, which relies on automatically understanding data-processing logic. This technique reverse-engineers data transformation logic to enable end-to-end tracing.
The solution is very complex as it requires a comprehensive knowledge of various programming languages that may have been used to perform transformations.
Best Data Lineage Tools to Consider
Now that we have explored the importance of data lineage and why it is essential to keep track of the family of our data, let us see why you would go for an external tool for the task rather than an in-house build. Some of the conveniences offered by by-products can be:
1. Enhanced Data Governance
2. Easier Migration of Metadata
3. Detailed Impact Analysis
Some of the tools out there which can help you integrate this cycle into your Machine Learning Pipeline are:
Keboola: It is a web-based data platform available as a service and can generate robust and unique analytics with both structured and unstructured data.
All the metadata collection processes can be automated with the tool’s help. Further conveyance of the data throughout the pipeline can be done generally without thinking twice about lineage itself.
Atlan: Atlan is a cloud-based data management service that can streamline all the data in your space for data cataloging, tagging, and parsing.
One of the service’s key features is a granular governance structure that promotes team collaboration and communication.
Collibra: It is a data intelligence platform working on banking on the privacy part of data governance, ensuring the data inventories created in data banks can be captured to their full potential, promoting proper lineage tracking among pipelines.
The interactive lineage diagram the service provides helps the interpretation of data, even for the layman in the company.
Kylo: It is a versatile open-sourced data lake management system that allows its user to ingest quickly, cleanse, validate and profile data in the service dashboard.
The rich metadata store provided with the service helps autofill a bunch of details in the fields with minuscule querying that is easy to understand.
Conclusion
In this blog, we looked at the different features, techniques, and tools that can be used to integrate data lineage in your machine learning pipeline, enabling outstanding control over your metadata and eventually translating to better communication and interworking among teams and companies.
Are you interested in learning more about data in your pipeline? How about checking out Our publication, The Essential Guide to Data Exploration in Machine Learning.
Written By
Aryan Kargwal
Data Evangelist