Data engineering teams work with data stored across databases, data warehouses, cloud platforms, APIs, and data lakes. As these environments grow, finding the right information becomes increasingly difficult. Engineers may know that a dataset exists but struggle to understand what it contains, where it comes from, how it should be used, or whether it can be trusted.
This is where a knowledge catalog becomes useful.
A knowledge catalog helps organisations connect data with the information needed to understand and use it effectively. Instead of simply listing available datasets, it can connect technical metadata, business definitions, relationships, ownership, lineage, and other contextual information to make enterprise data easier to discover and interpret.
In this guide, you will learn what a knowledge catalog is in data engineering, how it works, how it differs from a traditional data catalog, and how organisations can use it to improve data discovery, governance, and analytics.
What Is a Knowledge Catalog in Data Engineering?
A knowledge catalog in data engineering is a centralised system that organises information about an organisation’s data assets and connects them to meaningful business and technical context.
It helps users understand not only what data exists but also what the data means, how different datasets relate to one another, where the data originated, and how it can support business decisions.
For example, an organisation might store customer information in PostgreSQL, sales transactions in a cloud data warehouse, and marketing campaign data in a separate analytics platform.
A basic catalog might list these three data assets and provide their descriptions.
A knowledge catalog can go further by connecting the customer table to sales transactions, explaining the meaning of customer lifetime value, identifying the team responsible for customer data, and showing which dashboards depend on the underlying tables.
These connections turn isolated data assets into a more understandable information environment.
A knowledge catalog may include:
- Technical metadata: Table names, column types, schemas, and database locations.
- Business metadata: Definitions of terms such as revenue, active customer, and churn.
- Data lineage: Information about where data originates and how it changes through pipelines.
- Relationships: Connections between datasets, business concepts, reports, and other assets.
- Ownership and governance: Information about responsible teams, access policies, and data classifications.
- Usage context: Documentation about how datasets are used in reporting, machine learning, and operational workflows.
The exact capabilities depend on the platform. Some knowledge catalogs combine a traditional data catalog with a business glossary and knowledge graph, while others incorporate semantic models and AI-powered search.
Why Is a Knowledge Catalog Important in Data Engineering?
Data engineering involves more than moving information from one system to another. Engineers must also ensure that data is understandable, reliable, discoverable, and suitable for its intended purpose.
A knowledge catalog addresses several common challenges.
1. Finding the Right Data Faster
Large organisations may have thousands of tables and datasets. Without an effective discovery system, engineers often depend on colleagues to identify the correct source.
A knowledge catalog allows users to search using technical names, business terms, descriptions, and relationships.
For example, an analyst searching for customer retention data might discover a dataset named customer_activity_daily, even if the table name does not contain the phrase “customer retention”.
The catalog can connect the dataset to the relevant business concept and explain how it should be used.
2. Improving Data Understanding
A table name alone rarely explains the meaning of its contents.
Consider a column named status. Depending on the system, it could represent an order status, payment status, subscription status, or customer account status.
A knowledge catalog provides additional context through descriptions, business definitions, relationships, and ownership information.
This reduces misunderstandings and helps teams use datasets correctly.
3. Supporting Data Governance
Organisations need to know who owns their data, which information is sensitive, and how different datasets should be accessed.
A knowledge catalog can document data classifications, ownership, stewardship responsibilities, and governance policies.
However, documenting an access policy does not automatically enforce it. Access restrictions must also be implemented through the relevant databases, identity systems, and data platforms.
4. Reducing Duplicate Data Work
Different teams sometimes build separate pipelines or datasets to solve the same problem because they cannot find existing solutions.
By making data assets and their purposes easier to discover, a knowledge catalog helps teams identify reusable datasets, transformations, and definitions.
This can reduce unnecessary duplication and improve consistency across analytics projects.
5. Making Data More Useful for AI
AI systems need context to interpret enterprise information accurately.
A language model may encounter a column named ARR without knowing whether it means annual recurring revenue or something else in a particular organisation.
A knowledge catalog can connect the abbreviation to its approved definition, relevant datasets, business rules, and ownership.
This context can support AI assistants, natural-language analytics, and retrieval-augmented generation (RAG) systems. It does not guarantee that AI-generated answers will be correct, but it gives them better information to work with.
How Does a Knowledge Catalog Work?
A knowledge catalog typically combines metadata collection, information processing, relationship mapping, and search or discovery capabilities.
The implementation varies by platform, but the workflow commonly follows these steps.
Step 1: Discover Data Sources
The catalog connects to the organisation’s data environment.
Sources might include:
- Relational databases such as PostgreSQL and MySQL.
- Cloud data warehouses.
- Data lakes and object storage.
- ETL and ELT pipelines.
- Business intelligence platforms.
- Machine learning platforms.
- Data transformation tools.
Connectors or APIs collect available metadata from these systems.
The catalog should only access information permitted by its configured credentials and permissions.
Step 2: Collect Metadata
After connecting to the sources, the system collects information describing available assets.
For example, it might discover a table containing the following columns:
| Column | Data type | Description |
|---|---|---|
| customer_id | INTEGER | Unique customer identifier |
| order_date | DATE | Date the order was placed |
| order_value | DECIMAL | Monetary value of the order |
| order_status | VARCHAR | Current order status |
The column names and data types are technical metadata. The descriptions add context that helps users understand the table.
Step 3: Add Business Meaning
Technical metadata does not always explain how an organisation defines its business metrics.
A company might define monthly revenue as the total value of completed transactions during a calendar month, excluding cancelled orders and refunds.
A knowledge catalog can connect that definition to the relevant tables, columns, filters, and transformation logic.
This helps teams use consistent definitions when creating reports or building analytical models.
Step 4: Map Relationships
The catalog identifies or records connections between data assets and business concepts.
For example:
Customers → Orders → Payments → Revenue Dashboard
The relationships might be established using database schemas, pipeline metadata, documentation, manually curated links, or automated inference.
Some platforms represent these connections using a knowledge graph, which stores entities and the relationships between them.
Step 5: Enable Search and Discovery
Once metadata and relationships are organised, users can search for datasets, business terms, owners, or reports.
Depending on the platform, discovery may include keyword search, filters, natural-language search, recommendations, or graph navigation.
A data analyst looking for revenue data could discover the approved revenue definition, the underlying fact table, the transformation pipeline, and the dashboard that uses the metric.
Step 6: Maintain the Catalog
A knowledge catalog must remain aligned with the systems it describes.
When a table changes, a pipeline is replaced, or a business definition is updated, the catalog should reflect the change.
Automated metadata refreshes, lineage updates, ownership reviews, and documentation workflows help prevent information from becoming outdated.
Knowledge Catalog vs Data Catalog
The terms knowledge catalog and data catalog are sometimes used interchangeably, depending on the vendor and implementation. However, they can describe different levels of capability.
A traditional data catalog primarily helps users find and understand data assets through metadata. A knowledge catalog places additional emphasis on connecting those assets to business meaning and relationships.
| Feature | Traditional Data Catalog | Knowledge Catalog |
|---|---|---|
| Data asset discovery | Core capability | Core capability |
| Schema and column metadata | Common | Common |
| Business definitions | Often supported | Often central |
| Data lineage | Common in advanced platforms | Often integrated |
| Relationships between concepts | May be limited or separately managed | Often emphasised |
| Knowledge graph | Optional | May be a core component |
| AI-assisted discovery | Platform-dependent | Often a design objective |
| Business context | Varies by implementation | Emphasised |
These differences are not universal product rules. Many modern data catalogs already provide business glossaries, lineage, knowledge graphs, and AI-powered search.
When evaluating tools, compare their actual features rather than relying on the product label alone.
Knowledge Catalog vs Metadata Catalog vs Knowledge Graph
These three concepts are related, but they are not identical.
Metadata Catalog
A metadata catalog organises descriptive information about data assets.
It might tell you that a table called fact_orders exists in a particular warehouse, contains specific columns, and was last updated at a certain time.
Knowledge Catalog
A knowledge catalog combines data discovery with broader context. It may connect the table to business definitions, data owners, transformation logic, dashboards, and related concepts.
Knowledge Graph
A knowledge graph represents entities and their relationships in a structured form.
For example, a knowledge graph might represent the relationships between a customer, an order, a product, and a sales region.
A knowledge catalog can use a knowledge graph to make these relationships searchable and navigable. However, a knowledge catalog does not necessarily require a graph database or a formally implemented knowledge graph.
The main distinction is that a metadata catalog describes assets, a knowledge catalog helps users understand those assets in context, and a knowledge graph is one possible way to represent connected information.
Practical Example: Building a Simple Knowledge Catalog With Python
You do not need an enterprise platform to understand the basic idea behind a knowledge catalog.
The following Python example creates a small metadata registry that connects a dataset to its business definition, owner, and related assets.
import pandas as pd
catalog = [
{
"asset_name": "customers",
"asset_type": "table",
"description": "Stores customer account information",
"business_term": "Customer",
"owner": "Customer Data Team",
"related_assets": "orders"
},
{
"asset_name": "orders",
"asset_type": "table",
"description": "Stores customer order transactions",
"business_term": "Sales Order",
"owner": "Sales Analytics Team",
"related_assets": "customers, payments"
},
{
"asset_name": "payments",
"asset_type": "table",
"description": "Stores payment transaction details",
"business_term": "Payment",
"owner": "Finance Data Team",
"related_assets": "orders"
}
]
catalog_df = pd.DataFrame(catalog)
print(catalog_df)This code creates a simple table containing descriptive information about three data assets.
You can search the catalog by asset name or business description:
search_term = "customer"
results = catalog_df[
catalog_df["description"].str.contains(
search_term,
case=False,
na=False
)
| catalog_df["business_term"].str.contains(
search_term,
case=False,
na=False
)
]
print(results[
["asset_name", "description", "owner"]
])The search returns assets whose descriptions or business terms contain the word “customer”.
This is a basic illustration, not a complete production catalog. A real implementation would typically include persistent storage, access controls, metadata ingestion, lineage tracking, validation, and automated updates.
For a more advanced version, you could store relationships in a graph structure, connect to a database to extract schemas, and build a search interface that lets users explore datasets and their dependencies.
Popular Tools Used to Build Knowledge Catalogs
Organisations can build knowledge catalog capabilities using specialised platforms or by combining existing data infrastructure tools.
Examples include:
1. DataHub
DataHub is an open-source metadata platform that supports data discovery, metadata management, lineage, and governance-related workflows. It can serve as a foundation for a catalog that connects technical assets with organisational context.
2. OpenMetadata
OpenMetadata is an open-source platform for metadata management, data discovery, lineage, collaboration, and governance capabilities. It can help teams create a connected view of their data environment.
3. Atlan
Atlan is a data catalog and metadata platform focused on data discovery, collaboration, governance, and contextual information about data assets.
4. Collibra
Collibra provides data intelligence and governance capabilities, including cataloguing, business context, stewardship, and policy management.
5. Microsoft Purview
Microsoft Purview provides data governance and catalog capabilities across supported data environments. Its features can help organisations discover, classify, and understand their data assets.
These platforms differ in architecture, pricing, integrations, and available capabilities. Not every product implements a feature explicitly called a “knowledge catalog.” Evaluate each platform against your requirements for metadata discovery, business semantics, lineage, graph relationships, AI integration, and governance.
Best Practices for Implementing a Knowledge Catalog
A knowledge catalog is only useful when its information is accurate, accessible to the right people, and maintained over time.
Start With High-Value Data Assets
Do not attempt to catalogue every dataset immediately.
Begin with critical assets used for financial reporting, customer analytics, operational dashboards, or machine learning. Expand once the initial workflow demonstrates value.
Assign Clear Ownership
Every important dataset should have a responsible owner or steward.
Ownership helps teams determine who can explain a dataset, resolve definition conflicts, and approve changes to business terminology.
Standardise Business Definitions
Create consistent definitions for important metrics such as revenue, customer acquisition cost, active users, and churn.
Link these definitions to the data assets and transformation logic used to calculate them.
Automate Metadata Collection
Manual documentation can become difficult to maintain as data environments grow.
Where possible, automate schema discovery, lineage collection, metadata refreshes, and change detection. Keep human review for business definitions and relationships that require judgement.
Integrate Governance Early
Record classifications, approved usage, ownership, and relevant access requirements from the beginning.
Remember that catalog documentation supports governance but does not replace technical access controls or compliance processes.
Measure Adoption and Quality
Track whether people can find the datasets they need and whether the catalog improves data workflows.
Useful measures include search success, metadata completeness, ownership coverage, lineage coverage, duplicate asset creation, and the time required to locate trusted data.
Challenges and Limitations of Knowledge Catalogs
Despite their benefits, knowledge catalogs introduce several implementation challenges.
Incomplete metadata: Important datasets may lack descriptions, owners, or reliable lineage information.
Outdated documentation: Business definitions and technical structures can change faster than catalog updates.
Integration complexity: Connecting multiple databases, cloud platforms, and analytics tools may require significant configuration.
Conflicting business definitions: Different departments may calculate the same metric differently. Resolving these disagreements requires business decisions, not just better technology.
Security concerns: Catalogs can expose sensitive information about data assets and their structure if access is not configured properly.
Low adoption: A catalog will deliver limited value if engineers and analysts do not use it or contribute to maintaining it.
The most effective implementations combine appropriate technology with clear ownership, governance processes, and ongoing maintenance.
A knowledge catalog in data engineering helps organisations connect technical data assets with the business context needed to understand and use them.
By combining metadata, business definitions, lineage, relationships, and ownership information, it can make data discovery more efficient, improve consistency across analytics teams, and provide useful context for AI-powered data applications.
The key is to treat the catalog as a maintained part of the data architecture rather than a one-time documentation project. Start with important datasets, establish reliable definitions, automate metadata collection where possible, and keep information aligned with the systems it describes.
As data environments become more complex, the ability to find not only the right data but also the right meaning behind it becomes increasingly important.
Frequently Asked Questions
1. What is the main purpose of a knowledge catalog?
The main purpose is to help people discover, understand, and use data by connecting technical metadata with business definitions, ownership, lineage, and relationships between data assets.
2. What is the difference between a knowledge catalog and a data catalog?
A data catalog primarily organises and describes data assets. A knowledge catalog generally emphasises additional business context and relationships that help users understand how those assets fit together. The distinction varies across platforms.
3. Is a knowledge graph required for a knowledge catalog?
No. A knowledge graph can represent relationships between datasets, business concepts, and other entities, but a knowledge catalog can also use relational databases, metadata repositories, semantic models, or other structures.
4. How does a knowledge catalog support artificial intelligence?
It provides contextual information that AI systems can use to interpret business terms, discover relevant datasets, and connect questions to approved data sources. Its effectiveness depends on metadata quality, system integration, permissions, and the design of the AI application.
5. Which tools can be used to build a knowledge catalog?
Tools such as DataHub, OpenMetadata, Atlan, Collibra, and Microsoft Purview offer different combinations of cataloguing, metadata management, lineage, governance, and discovery capabilities. The best choice depends on an organisation’s infrastructure, budget, and requirements.