Data lineage vs. data provenance
Digital transformation and big data have permanently changed the way businesses operate: It’s more important than ever for businesses to have a strong digital presence and to utilize existing data. Additionally, data management has been transformed by the evolution of big data, and it’s important for organizations to keep track of data quality, sources, and lineage with the implementation of international compliance laws.
There are two leading organizational data management techniques: Data provenance and data lineage. This blog post will detail the two and discuss the benefits and drawbacks of each approach.
What is data lineage?
Data lineage tracks the journey your data takes over time to provide a better understanding of where your data came from and any changes that have affected it. It can also help you locate exactly where a data point’s ultimate destination is within the pipeline. Without data lineage, it can be particularly cumbersome to track data through its transformations, turns, twists, and various business intelligence systems.
In short, data lineage provides an overview of data’s complete lifecycle management.
What is data provenance?
Data provenance refers to the historical tracing of data from its original source to its last stage. Data provenance is particularly useful when tracking data compliance and maintaining data quality, and it provides even more benefits to organizational data management by tracking:
- Input methods
- Data sources
- Factors that influence the initiation of data
In short, data provenance tracks data sources and their various transformation stages.
Let’s do a specific comparison of data provenance vs. data lineage.
Key tools
Some data provenance tools include the following:
- Cloudera
- Open Provenance Model
- Linux Provenance Modules
- Data Tracker
- Kepler
- Jupyter
- CamFlow
Key data lineage tools include the following:
- ASG Data Management
- Octopai
- Jaspersoft ETL
- Dremio
- Kylo
- CloverETL
- Apatar
- Talend Open Studio
Compliance requirements
When required, it is possible to submit data for regulatory compliance easily thanks to the sophisticated nature of data management tools.
However, data provenance tools are somewhat outshined by data lineage tools concerning the production of mandatory compliance data.
Challenges
Data lineage – Managing large volumes of data can be challenging when using data lineage. This includes unifying disparate promotional systems, tracking cross channels, and maintaining data lineage.
Data provenance – Data provenance tools often struggle to accurately track data retention, execution reproduction, and complicated large workflows.
Components
Data lineage – Data lineage components often include data nurture methods, data capture sources, and web portals. AI included are an ERP system, CRM systems, and data qualification systems.
Data provenance – Data provenance components include data input methods and tracking capture sources.
Goals
Data lineage – Data lineage aims to track data’s entire life cycle from the data origination through data exhaustion.
Data provenance – Data provenance tools track origination as well. However, these tools often segregate data into three key stages:
- Data in process
- Data in motion
- Data in rest
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