Enterprise Data Catalog: Tutorial & Best Practices

As organizations generate and store ever-increasing amounts of data, the challenge shifts from storage to effective data management and use. A data catalog acts as the single source of truth, allowing everyone—from technical teams to business users—to create, publish, document, discover, access, and report on data, regardless of its location within the company. A well-implemented data catalog unlocks the full potential of your data assets and can help answer critical questions about data provenance, quality, and usability.

This article explores data catalog functionality, types, implementation, and integration. We also share best practices to help you maximize the value of your data catalog.

Summary of key data catalog concepts

Components of a data catalog

Key considerations when evaluating or developing data catalogs are given below.

Metadata management

Metadata is data about data and is extremely relevant for understanding a dataset's technical, operational, business, and social context. The data producer is responsible for enriching a data source with extensive metadata, and the catalog owner is responsible for enforcing the population of key metadata fields. The diagram below shares key metadata types and underlying attributes that should be captured for a dataset.

Data lineage and pipeline lineage

Data lineage tracks the data flow along its journey through the data pipeline. Most importantly, it provides data provenance information that can be used to understand data sources and whether they have particular security, privacy, or handling requirements. It shows where the data came from and how it changed; however, it doesn’t include the operational aspects of the data pipeline, such as tracking delayed or failed jobs.

To achieve pipeline lineage, which has the power to identify the root cause of data pipeline problems, one must complement data lineage with pipeline observability. That way, you can monitor the entire data journey across the pipeline stages and correlate the information with operational information to pinpoint the root cause where issues that impact data quality and reliability arise. Pantomath’s blog on data lineage challenges offers valuable insights here.

Data governance, security, and privacy

Data governance, security, and privacy are essential data catalog features for responsible data access and use. With large sensitive data volumes, implementing controls to protect data integrity and ensure regulatory compliance is crucial. Important features include

• Role-based or attribute-based access control that limits access to authorized users.
• Audit trails that record who accessed or modified data and when.

These capabilities ensure transparency, prevent misuse, and maintain compliance with privacy standards, thereby building trust and usability in the data catalog. See the example below for various permissions a catalog user can have on a source registered to the catalog.

It is also critical to distinguish between security "in the data catalog" and security "of the data catalog." Security "in the data catalog" focuses on the mechanisms within the catalog, such as access control and audit trails. Security "of the data catalog" refers to the broader infrastructure ensuring the catalog's integrity and protection. For cloud-based data catalogs, the cloud service provider typically handles infrastructure-level security. For on-premise catalogs, the IT or data engineering teams manage these responsibilities. Both layers of security are crucial considerations while implementing a data catalog.

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Enterprise search (Natural language search)

Incorporating natural language search into a data catalog can significantly enhance its usability. Users can intuitively query the catalog in plain language. GenAI can leverage well-defined metadata, comments, and tags on catalog objects to decipher relationships, patterns, and the underlying semantic context. Context-aware search enables both technical and non-technical teams to quickly discover relevant data. It reduces the complexity of finding the right assets in large, distributed data environments and improves overall data accessibility across the enterprise.

Data profiling, classification, quality, and observability

Data profiling involves analyzing a dataset's structure, content, and relationships. It provides insights into data types and distributions and potential issues like missing or inconsistent values. The process helps identify sensitive data, such as PII (personally identifiable information), and categorizes datasets into tiers like bronze (raw), silver (cleansed), and gold (curated).

Data classification builds on profiling by assigning labels or tags to data based on its characteristics, purpose, or regulatory requirements. It enables better governance and accessibility.

Data quality ensures that data meets the required accuracy, completeness, and consistency standards, preventing downstream data pipeline issues.

Data observability tracks key metrics like data volume, freshness, completeness, and lateness. It provides continuous insights into the health and reliability of data as it moves through various systems. Achieving observability within a data catalog requires monitoring these metrics and setting up alerts for any anomalies. For a deep dive into data observability metrics, refer to this blog by Pantomath.

Together, these capabilities allow the data catalog to offer a comprehensive view of data readiness so users can confidently consume data for analytics and decision-making.

Data product marketplace

In a decentralized architecture like a data mesh, you can use the data catalog to register curated datasets and make them discoverable as data products. Data owners and domain teams can manage and share their datasets, allowing other teams to effectively discover and utilize the same assets.

By integrating the data catalog into the data mesh architecture, organizations can maintain data governance while promoting data scalability and reusability across the enterprise. Illustrated below is the typical use case of a data product catalog in a data mesh design -

API & integration support

A modern data catalog must be extensible through APIs and connectors for integration with various data sources and platforms. This capability ensures that the catalog can reach the entire enterprise, supporting traditional and modern data environments. Organizations can easily onboard new data sources to ensure the catalog remains up-to-date and serves a broad range of data use cases.

A Python example of how an organization could extend the catalog to include a new data source via an API is shown below. You can adjust this example based on your catalog’s API specifications.

import requests

# API endpoint for the data catalog
API_URL = "https://example.com/api/catalog/datasets"

# New dataset metadata
new_dataset = {
    "name": "sales_data_2024",
    "description": "Sales data for Q1 2024",
    "owner": "sales_department",
    "tags": ["sales", "2024", "financial"],
    "schema": {
        "columns": [
            {"name": "order_id", "type": "integer"},
            {"name": "customer_name", "type": "string"},
            {"name": "amount", "type": "float"},
            {"name": "date", "type": "date"}
        ]
    },
    "source": {
        "type": "database",
        "connection": {
            "host": "db.example.com",
            "port": 5432,
            "database": "sales_db",
            "user": "username",
            "password": "password"
        }
    }
}

# Make the POST request to add the dataset to the catalog
response = requests.post(API_URL, json=new_dataset)

# Check the response status
if response.status_code == 201:
    print("Dataset successfully added to the catalog.")
else:
    print(f"Failed to add dataset. Status Code: {response.status_code}")

Collaboration, social & usage features

Collaboration features enhance the usability and flexibility of a data catalog. Intuitive UI/UX, commenting functionality, and the ability to share datasets within teams enable users to interact with and provide feedback on data assets. Social features like rating or tagging datasets help surface valuable insights, while suggestions or recommendations improve data discovery. These collaborative tools foster a data-driven culture, making the catalog a dynamic space for both technical and non-technical users to engage effectively with data.

Types of data catalogs

Understanding the nuances of each catalog type will help you make an informed decision that aligns with your data management goals and operational needs. Refer to the table below to understand which catalog types offer which key features. While there is significant overlap in functionality across these categories, it's crucial to identify your organization's most critical requirements when selecting the appropriate type.

Enterprise catalogs

Enterprise data catalogs provide a comprehensive view of all organizational data, targeting strategic decision-making and long-term insights. They focus on centralized data discovery, metadata management, and broad data access across departments. Typical metrics include data coverage, data quality scores, and user engagement. These catalogs help break down data silos and support cross-functional decision-making.

Examples include Alation, Collibra, Atlan, Attacama, and Informatica Enterprise Data Catalog.

Platform-specific catalogs

These catalogs are tailored for specific cloud platforms or data tools, optimizing integration and performance within those environments. They ensure connectivity and management of data stored in cloud-native formats, offering deep integration with services like data lakes or warehouses. Metrics often focus on performance within the specific platform and ease of integration.

Cloud-specific ones include AWS Glue Catalog, Azure Purview, and Google Cloud Data Catalog. Tool-specific ones include Databricks Unity Catalog and Snowflake Polaris Catalog.

Data governance catalogs

Governance-focused catalogs are built to manage sensitive data classification and access control. They ensure that organizations comply with regulations and internal policies. Key metrics include compliance scores, access audit trails, and data sensitivity levels. They are critical in maintaining security and ensuring data is handled appropriately across the enterprise.

Examples include Alation, Atlan, IBM Knowledge Catalog, Collibra, and Ataccama.

Data observability catalogs

These catalogs are designed to monitor the health and performance of data assets, focusing on metrics like data volume, freshness, completeness, and latency. They provide insights into key usage patterns, the criticality of data assets, and operational performance for data reliability and quality across complex pipelines.

One example is Pantomath, which enhances data observability by providing automated cataloging of data assets and real-time data health monitoring. Its capabilities include comprehensive lineage tracking with pipeline observability so teams understand and rectify data flows and dependencies effortlessly.

Operational catalogs

These catalogs focus on supporting real-time data access in daily business operations. They are designed to manage live data streams and provide quick insights into transactional or operational systems. Key features include real-time indexing, low-latency data retrieval, and metrics like uptime and query performance. Their value proposition lies in enabling efficient operations and decision-making in fast-paced environments.

Explore the features of Confluent Stream Catalog and other Apache Atlas-based implementations to learn more about the live stream functionality.

Design & implementation considerations

Key considerations when planning, designing, and implementing a data catalog are given below.

Data strategy alignment

Ensure the data catalog aligns with your organization’s broader data strategy and business objectives. It should support data governance initiatives and drive data-driven goals. For instance, if the organization plans to adopt a decentralized data mesh, the catalog must facilitate treating each data asset as a product.

Pre-implementation planning

Clearly define objectives, engage relevant stakeholders, and assess the organization’s data landscape. Early involvement of business stakeholders and identification of key data sources and data consumers lays a strong foundation for catalog implementation.

Scope and platform selection

Carefully determine the scope of the data catalog and select a platform that meets current requirements and offers flexibility for future scalability and integration with other tools. This ensures long-term value and adaptability to evolving needs.

Integration with existing infrastructure

The data catalog must integrate with existing databases, data lakes, and analytics tools. This involves ensuring compatibility, security, and ease of use through APIs and connectors that support various data sources across the enterprise.

Metadata and lineage extraction and automation

Automating metadata and lineage extraction ensures an accurate and reliable catalog population. Keeping metadata up-to-date across all data sources maintains the catalog’s relevance and usability over time. Investing in automating this process can really accelerate catalog development. Below, we share an example of how such tasks are automated in Pantomath.

Using existing data dictionaries, glossaries, and tribal knowledge

Incorporating existing data dictionaries, glossaries, and tribal knowledge helps enrich the catalog’s metadata. It ensures consistency and adds valuable business context that aligns with operational and analytical use cases.

Scalability and cross-platform support

Design the data catalog to scale with the organization's growth, supporting multiple platforms and ensuring adaptability to emerging technologies. Cross-platform support enables data discovery and management as new systems are introduced. This also includes support for unstructured data and building governance on top of files.

User training and change management

Effective user training and change management strategies are essential for successfully adopting the data catalog. Teams that understand how to use the catalog maximize its utility and foster a data-driven culture.

Ongoing maintenance and governance

Establish processes for ongoing maintenance, governance, and regular audits. This will ensure that the catalog remains secure, relevant, and aligned with evolving organizational goals, promoting long-term sustainability.

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Common pitfalls and best practices when implementing a data catalog

Overlooking the importance of open standards/APIs

Ensure your data catalog supports open standards and APIs for data sharing and integration across diverse systems. This promotes interoperability and holistic adoption, enabling your catalog to serve as a centralized hub for all data assets. For example, for Iceberg tables, multiple catalogs are now leveraging the Iceberg REST Catalog API, and for Delta tables, Unity Catalog is built on the OpenAPI specification.

Neglecting user-centric design

Prioritize an intuitive user experience (UX) and user interface (UI) to drive adoption. A user-friendly design ensures that both technical and non-technical users can easily navigate the catalog. It improves accessibility and overall engagement across the organization.

Failing to measure adoption and success

Regularly track usage metrics and define clear success criteria to evaluate the catalog's impact. Establish a feedback loop for continuous improvement to ensure the catalog evolves to meet user needs and organizational objectives.

Underestimating the value of automation

Leverage automation for key tasks like metadata extraction, data quality checks, and monitoring. This minimizes manual effort, enhances efficiency, and ensures the catalog remains up-to-date with minimal intervention. It frees up time for higher-value activities.

Ignoring the potential of AI capabilities

To enhance catalog functionality, incorporate AI-driven features such as natural language search, automated recommendations, and predictive analytics. These capabilities make the catalog smarter and more user-friendly, improving both discovery and decision-making.

Lack of flexibility for future growth

Design the data catalog to be extensible and adaptable to new data sources, emerging technologies, and evolving business needs. This flexibility ensures the catalog remains relevant as the organization grows and changes, protecting your investment in the long term.

Inadequate data quality and governance controls

Implement strong data quality measures and governance frameworks to ensure your data assets' reliability, security, and compliance. Automating data quality and governance checks prevent data issues from disrupting business processes or violating regulations.

Skipping comprehensive user training

Provide thorough training and ongoing support to ensure that all users, from data engineers to business analysts, are equipped to fully leverage the catalog’s capabilities. Continuous education ensures that users maximize the value of the tool, driving long-term success.

Ignoring the need for continuous improvement

Regularly update, audit, and refine the data catalog to keep it aligned with changing organizational goals and evolving data strategies. Continuous improvement is critical to maintaining the catalog’s relevance and effectiveness over time.

Involving people and process changes, not just technology

Successful data catalog implementation is not just about technology but requires aligning people and processes. Engage stakeholders across the organization and ensure workflows are designed to support the adoption and utilization of the data catalog, making it a core part of the data-driven culture.

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Conclusion

A well-implemented data catalog is essential for effectively managing, discovering, and leveraging data. Beyond organization, it plays a critical role in data governance and access control. With the right strategy, a data catalog can unlock the full potential of data assets for decision-making and business growth.