Data lineage refers to the tracking of data from its origin through its various transformations and movements across different stages of processing, analysis, and consumption within an organization's data architecture. It essentially traces the journey of data, providing insights into its source, how it has been manipulated or transformed, and where it has been used.
In essence, data lineage offers a detailed narrative of a data asset's journey from its inception to its current state. It sheds light on critical questions such as
a. Where the data originated from,
b. how it has been modified or enriched along the way, and
c. Which downstream processes or systems consume it.
d. Who owns what
e. What is the quality of the data?
In this article, we'll delve into the fundamental concepts underlying data lineage. We'll explore different types of lineage, including forward and backward lineage in the subsequent posts, and delve into how organizations can harness this information to enhance the quality, reliability, and trustworthiness of their data assets. By understanding data lineage, both data teams and business stakeholders can make informed decisions, ensure data governance compliance, and optimize data-driven processes for better outcomes.
Data lineage is like a roadmap that traces the journey of data as it moves from its sources (upstream producers) to its destinations (downstream consumers), with every intermediate step accounted for along the way. This comprehensive understanding of data lineage empowers organizations to make informed decisions, optimize data management practices, and ensure that data serves its intended purpose effectively across the entire data lifecycle.
From where can we get data lineage metadata?
Data lineage metadata can be obtained from various sources within an organization's data ecosystem. Here are some common sources:
1. ETL tools: ETL (Extract-Transform-Load) tools provide built-in functionality to capture and store data lineage metadata. These tools automatically track data movement and transformations as part of their processing pipelines. Ex: tool like Apache Airflow, Talend, Apache NiFi provide built-in support to extract data lineage metadata.
Ex: https://www.astronomer.io/blog/3-ways-to-extract-data-lineage-from-airflow/
2. Data Catalogs and metadata management systems: Data catalogs and metadata management systems serve as centralized repositories for storing and managing metadata across the organization. These platforms often include features for capturing and visualizing data lineage. Examples of data catalog tools with data lineage capabilities include Collibra, Alation, Apache Atlas, and IBM InfoSphere Information Governance Catalog.
3. Database and Data Warehouse Platforms: Some database and data warehouse platforms offer native support for capturing data lineage metadata. They may record information about data sources, tables, columns, and dependencies within their metadata repositories. For example, platforms like Amazon Redshift, Snowflake, and Teradata provide features for tracking data lineage within their environments.
4. From Documents: In some cases, data lineage metadata may be documented manually by data stewards, analysts, or developers. Documentation tools, such as wikis, spreadsheets, or specialized data lineage documentation software, can be used to record information about data flows, transformations, and dependencies.
5. Many other applications like APIs, logging tools, Data governance applications etc., can provide data lineage metadata.
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