Back
Blog Post

Entity Relationship Diagrams (ERDs) vs. Data Lineage: Similarities and Differences

Alec Bialosky
May 24, 2024

Data-driven businesses gather and store massive amounts of data on every part of their operation. As data volumes increase, data models become more complex, and finding and understanding data for timely decision-making is much more difficult.

Entity relationship diagrams (ERDs) and data lineage are two features that make data more manageable. In this post, we’ll explain what ERDs and data lineage are, how they are different, and why they are important.

ERDs and Data Lineage: What They Are

Entity relationship diagrams (ERDs) visually illustrate how tables relate to one another. With a clear overview of data relationships, users can map the best way to query and join available data.

Example of an ERD (Source: Lucidchart)

Traditionally, ERDs have been created and maintained by data architects, declaring primary and foreign key constraints of relational database tables. Primary keys uniquely identify rows within a table, while foreign keys establish relationships between tables by referencing the primary keys of other tables, ensuring data integrity and efficient querying.

However, with the increasing usage of cloud data warehouses and data lakes like Snowflake, Databricks, and BigQuery which do not require primary key / foreign key constraints, often there’s usually no ERDs or data model diagrams that exist for the data warehouse, and creating them manually is time & resource intensive. Luckily, modern tools like Select Star generate ERDs from inferring join keys and entity relationships from SQL query history.

Data lineage, on the other hand, traces the origin of data as it flows through and between systems. This context is critical to data operations on change management, data governance, and data quality. While lineage can be documented manually, modern data platforms like Select Star can track lineage automatically from parsing the metadata, including DDL or DML SQL queries.

Before we dig in to comparing the tools – and explaining why they’re best when used together – here’s a quick overview of their similarities and differences:

 Entity Relationship Diagrams (ERDs) Data Lineage
Purpose Visualize the data model of tables and columns by showing how different entities can be used together Track and visualize the flow and transformation of table and/or column within a data system or across systems
Focus The relationships between different entities (tables) in a database The origin, transformation and movement of data throughout its lifecycle and across data applications
Use Case
  • Data model design and normalization
  • Understanding overall schema and relationships for querying data
  • Root-cause analysis for data incidents or broken reports
  • Impact analysis for data model updates
  • Propagation of data quality status, or classification tags
Detail Level Provides a high-level overview of relational data models Offers detailed insights into specific data flows and transformations
Key Features
  • Displays tables, fields, and primary/foreign key relationships
  • Illustrates cardinality and dependencies
  • Upstream and downstream dependencies
  • Data usage ( i.e., transformation vs. filter) by column
Benefits
  • Overview of the data model for data analytics
  • Discovery and understanding for how to join tables together
  • Transparency and traceability of data
  • Understanding data model dependencies
Visualization Usually presented as a diagram with tables as entities and lines indicating relationships Often shown as a flowchart or a graph illustrating the path and transformations of data

Entity Relationship Diagrams (ERDs)

Entity relationship diagrams, or ERDs, are an essential part of data modeling and architecture. They show the relationships between different data assets, making it easy to understand which disparate datasets have been combined and how they are joined together.

ERD shows which datasets are joined together and which fields they are joined on

Documenting data with ERDs

In a relational database, entities correspond to tables. The columns in the table – the information the organization wants to collect – are called attributes. For example, if a college has an entity called Students, within the entity would be attributes like Student Name and Student Major. Courses would be a different entity. Within that entity would be attributes like Instructor and Prerequisites.

Relationships are how entities interact. They might be one-to-one, one-to-many, or many-to-many. Each student has multiple courses, but only one major. An instructor might teach only one course but be associated with 100 students. An entity relationship diagram puts all this information in a clear, visual format. It’s a bird’s-eye view that facilitates database design and debugging, because redundancies and design flaws are easy to spot.

ERDs also enhance internal communication. The graphic nature of an ERD eliminates the confusion that might arise between stakeholders who talk about the same topics using different vocabulary.

Data Lineage: Data Model Dependencies

Data lineage tracks data’s origin and transformations, ensuring reliability, accuracy, and quality. It’s like a personal history of each unit of data.

While table-level data lineage only tracks data’s movement between tables, column-level data lineage is a fine-grained map that provides context for each table, column, and dashboard between your data warehouse and your BI tools.

When you understand data’s origin, ownership, transformations, and dependencies, you can trace problems to their source. You can also prevent problems by identifying potential downstream impacts before implementing a change.

Data lineage creates confidence that your single source of truth is always accurate and up-to-date.

Lineage graphs visualize data flows

Data lineage exposes how data flows and transforms within the database and across platforms

A common way to present lineage is with a lineage graph - which visualizes the flow of data across your data pipelines. While this can be helpful to understand how data traverses from asset to asset, it can also be helpful to see downstream dependencies listed out in a more hierarchical view to quickly digest larger amounts of information.

Lineage hierarchies make it easier to digest large amounts of dependencies

Data lineage use cases

Data lineage makes efficient data management and governance possible. The most common use cases for column-level data lineage include:

  • Root cause analysis: When a pipeline breaks, data lineage enables teams to quickly track down the transformation that caused the problem and fix it.
  • Impact analysis: Data lineage shows the data team exactly how data is being used downstream, so they can head off pipeline issues before making a change.
  • Automated data maintenance: Column-level data lineage allows companies to automate routine maintenance of their data catalog, ensuring data remains fresh, accurate, and compliant.
  • Audits: Auditors can easily track and verify data origins and movement and can understand its context, supporting data integrity.
  • Database optimization: Column-level lineage helps teams identify rarely-used columns that can be archived or deprecated. Streamlining and removing obsolete columns optimizes storage costs and leads to faster queries.

Xometry: Saving 200 engineering hours a year with data lineage

Here’s a real-world example of how data lineage can optimize business operations. Xometry lacked visibility into how data was flowing through their AI-powered system, leading to frequent data outages, long decision-making times, and high rates of human error. All told, the company was losing millions of dollars every month to an inefficient data pipeline.

After implementing Select Star's column-level lineage tool, the Xometry team could track data as it transformed across tables. Engineers could see potential downstream impacts before carrying out changes to the pipeline. When issues did occur, they could be tracked to their root and corrected, making debugging 36X faster. With data outages significantly reduced, Xometry saves over 200 data engineering hours a year.

Comparing ERDs vs. Data Lineage

ERDs and data lineage are different tools with a common goal: to enhance the understanding, management, and effective use of data within the organization data team.

Both tools offer valuable context to enable quick, strategic decision-making. Both present information in a logical manner that simplifies troubleshooting and compliance. Both provide visibility into the structure and flow of data.

However, each tool takes its own approach to these functions, and each presents the data in its own form.

  • Understanding data structure and flow: ERDs help us understand how data is structured within a database by illustrating the relationships between entities (tables). Column-level lineage provides insights into how data flows and transforms within and across systems.
  • Improving data management: ERDs are instrumental in designing and maintaining efficient database structures, while column-level lineage is key in managing data transformations and ensuring data integrity throughout its lifecycle.
  • Supporting data governance: By providing a clear picture of relationships and flows, ERDs and column-level lineage both contribute to stronger data governance practices. They help ensure that data is handled in a way that meets compliance standards and organizational policies.
  • Facilitating communication and collaboration across the organization: ERDs and column-level lineage tools align understanding and collaboration between stakeholders, including data architects, database administrators, and business analysts.
  • Increasing operational efficiency: ERDs make it easy for data producers to see possible connections across different data sets and how to accurately join them together. Lineage reduces the time required to understand existing data flows, improving change management efficiency and reducing time spent trying to understand where data comes from.
  • Enabling informed decision-making: Both ERDs and column-level lineage contribute to more informed decision-making by providing critical information about data structures and flows, ensuring decisions are based on accurate and reliable data.

Leveraging the combined power of data lineage and ERDs

Together, ERDs and data lineage help organizations effectively manage their data. Combining them gives teams a clear understanding of data’s structure, relationships, and dependencies, improving the ability to make timely, strategic business decisions.

ERDs and Data Lineage for Data Governance

Column-level data lineage is the backbone of data governance. It supports regulatory compliance by providing the traceability to data’s origin and how it has transformed between your data warehouse and BI tools. When errors occur, data lineage makes it possible to trace the problem back to the source.

ERDs enhance governance by making the entire data model clear, even for less-technical stakeholders. This clarity fosters productive communication about data concepts such as quality and data privacy. They also act as an early warning system – making redundancies and entanglements apparent before they have a chance to cause data errors. This helps organizations streamline data management and operations with accurate data.

ERDs and Data Lineage for Data Documentation

As a company grows and data is democratized, new tables are added and data models evolve. Manually establishing column-level lineage or ERDs is resource-intensive and unsustainable due to the dynamic nature of data models keep changing. Keeping up with these changes manually becomes impossible as companies expand.

With modern data governance platforms like Select Star, data teams are finally able to automate documentation of key relationships across datasets. These insights not only help sharing knowledge and context within the data team, but also allow automating the data governance and data management workflows, such as data classification and notifications.

The graph model of ERDs and data lineage helps visualize connections and provides a comprehensive understanding of business processes and data models. This visibility is critical for understanding data, ensuring accuracy, and making informed decisions. By seeing how data flows and interacts within the organization, teams can better manage their resources, maintain data integrity, and enhance overall efficiency.

About Select Star

Select Star is an intelligent data governance platform that automatically analyzes & documents your data. From data catalog, lineage, usage analysis, and AI assistants, Select Star provides an easy-to-use data portal, where data teams can govern and manage their data with automation. Today, Select Star is used as a co-pilot of data teams for data governance, data migration, data democratization, and cost optimization initiatives.

Book a demo to see our tool in action.

Related Posts

Dashboard Sprawls and Data Product Management
Learn More
Automating Data Security & Privacy Management
Learn More
Data Governance for Analytics Engineering
Learn More
Data Lineage
Data Lineage
Data Quality
Data Quality
Data Documentation
Data Documentation
Data Engineering
Data Engineering
Data Catalog
Data Catalog
Data Science
Data Science
Data Analytics
Data Analytics
Data Mesh
Data Mesh
Company News
Company News
Case Study
Case Study
Technology Architecture
Technology Architecture
Data Governance
Data Governance
Data Discovery
Data Discovery
Business
Business
Data Lineage
Data Lineage
Data Quality
Data Quality
Data Documentation
Data Documentation
Data Engineering
Data Engineering
Data Catalog
Data Catalog
Data Science
Data Science
Data Analytics
Data Analytics
Data Mesh
Data Mesh
Company News
Company News
Case Study
Case Study
Technology Architecture
Technology Architecture
Data Governance
Data Governance
Data Discovery
Data Discovery
Business
Business
Turn your metadata into real insights