Metadata Management Tools: Types, Features & Benefits

Managing complex and massive data sets is tricky but metadata management tools can help teams keep their data in shape. Metadata management has become critical in data strategies created by organizations that treat data as an important asset.

In this article, we dive into metadata management and give you an overview of tools teams use to maximize the value of their metadata.

What is Metadata Management?

Metadata management is the systematic process of gathering, organizing, and using metadata to enhance the value and usability of an organization’s data assets. It’s critical to guarantee that data is more than just a passive resource, but rather a strategic asset that supports informed decision-making, operational efficiency, and compliance.

Metadata management works like a “librarian” by cataloging, storing, and providing information about your data. It offers useful context for data, making it easier for users to find, access, and comprehend data assets. This accessibility improves data-driven decision-making and fosters teamwork.

Metadata contains critical information regarding data lineage, ownership, quality, and sensitivity. This is critical for maintaining data integrity, ensuring regulatory compliance, and protecting sensitive data, including customer data and financial records. Metadata management tools can create a visual map of data provenance, demonstrating how data moves from source to destination. This is useful for tracking the impact of changes, auditing procedures, and guaranteeing data accuracy.

Along with data quality tools, metadata management tools are a key component in any modern data strategy.

Metadata Management Tools: Overview

A metadata management tool is a software solution that lets teams collect, organize, store, and manage metadata.

Metadata management systems help organizations understand and use data by offering a centralized system for cataloging, documenting, and evaluating data lineage, linkages, and usage patterns.

Metadata management software usually has features that enable users to:

• Discover and catalogue data assets.
• Track the data lineage and do impact analysis.
• Standardize and define data sets.
• Enhance collaboration and information sharing.
• Ensure data governance and compliance (often via the implementation of Data governance frameworks).

6 Best Metadata Management Tools

Google Cloud Data Catalog

One common metadata management solution is Google Cloud Data Catalog, a Google Cloud Platform service that enables customers to store, manage, and catalog metadata for data assets and business processes.

The application allows you to manage and use massive data sets, save metadata, and easily retrieve data from numerous sources. Cloud Data Catalog provides various capabilities, including data security and compliance, real-time query and search, and API access.

DataHub

Based on its feature set, we could classify DataHub, an event-driven data catalog, as a metadata platform similar to OpenMetadata. The company behind this solution is LinkedIn, which developed and used it internally before making it open-source in 2020. Since then, the adoption and community surrounding it have grown significantly.

Acryl is now the main developer and maintainer of DataHub. They also offer a DataHub SaaS service in their product portfolio. Nonetheless, Acryl is fully committed to the open-source approach. This means that the majority (if not all) of the features are and will remain in the open-source release.

Because DataHub is event-based, every metadata-related interaction with the user interface, such as metadata ingestion, triggers an event in a Kafka topic. The backend service detects the event and updates the database. This capability can be delegated to two independent services: the Metadata Change Event (MCE) and the Metadata Audit Event (MAE).

Metadata ingestion takes place in either a dedicated container or a frontend container that has been set up and started. Alternatively, you can use the Python SDK to consume metadata programmatically.

You may quickly deploy DataHub using DataHub’s helm charts and the suggested default configuration (MySQL as the database, a dedicated Kafka instance, Elasticsearch for search and graph index, and MCE and MAE in the backend).

Apache Atlas

Apache Atlas is an open-source metadata management platform that offers users a wide range of functionality for defining, storing, and managing data. It supports data tagging, entity type definitions, search, and fine-grained access control.

The tool simplifies the process of integrating data and applications, enabling users to create data governance programs and workflows using flexible entity models.

Amundsen

Amundsen was created to help users in answering questions about data availability, trustworthiness, ownership, usage, and reusability. Amundsen’s major features include simple metadata ingestion, search, discovery, lineage, and visualization. The Amundsen project is now being managed by the Linux Foundation’s AI & Data department.

Amundsen’s architecture is made up of several services, including the metadata service, search service, frontend service, and data builder. These services rely on technologies like Neo4j and Elasticsearch, so you’ll have to learn how to use them to troubleshoot potential issues.

Alation

Alation offers a solid data platform that supports multiple metadata management applications, including search and discovery, data governance initiatives, and digital transformation. It provides teams with machine learning capabilities, an AI-powered data catalog, Active Directory integration, and ad hoc reporting.

Alation is known for its ease of use and AI capabilities. It’s a great option for companies trying to boost data cooperation and discovery, as it lets teams speed up self-service, simplify cloud migration, and easily manage data governance.

Atlan

Atlan provides active and passive metadata management solutions, helping teams to create a single source of truth for all of their data assets and interact across the current data stack thanks to extensive connections with technologies such as Slack, BI, and data science tools.

Teams get handy search, catalog, and browse data assets. They can easily give data assets a profile, such as automated data quality profiling, wikis, etc. Collaboration is made simple via sharing links, using chat plugins, and integration with business intelligence systems.

Atlan’s core platform is built on top-tier open-source projects and is API-driven by default for all actions. What distinguishes Atlan are its collaboration features and real-time data insights, which have proven extremely useful in dynamic and data-driven businesses.

All the tools on our list serve a similar range of use cases but come with different features. If you’re looking not only for metadata management but also a solid data catalog solution, check out our list of top data catalog tools.

Types of Metadata Management Tools

A metadata management tool is a software system that collects, stores, and manages metadata about organizational data assets. Metadata management technologies often offer a broad range of usability. They include features like:

• Metadata Repositories and Catalogs – catalogs contain metadata in a structured and organized format, allowing users to search, explore, and find material within the metadata repository. Choosing the correct data catalog solutions can significantly improve metadata management since they centralize metadata, making it accessible and organized across several data systems.
• Data Integration Solutions – These interfaces allow the metadata management tool to connect to a variety of data sources in your organization’s tech stack, including databases, data warehouses, and data lakes.
• Business Glossary Tools – It encapsulates the business context and terminology related to data assets.
• Data Lineage Tools – This component depicts the relationships between various datasets within the company, allowing users to understand the data lifecycle.
• Data Quality Tools – This category of tools relates to establishing and enforcing data quality standards, including data lineage and meeting governance requirements through data quality monitoring or classification.
• ETL (Extract, Transform, Load) Tools with Metadata Capabilities – ETL is a data integration process that extracts, transforms, and loads data from many sources into a data warehouse or other centralized data repository.
• Master Data Management (MDM) Solutions – Master data management (MDM) is the process of producing a single master record for each person, place, or thing in an organization using data from both internal and external sources and applications. This material has been deduplicated, reconciled, and augmented, resulting in a consistent and credible source. Once established, master data promotes accurate reporting, decreases data mistakes, eliminates redundancy, and helps teams make better business decisions.

Core Components and Features of Metadata Management Tools

A metadata management tool is made up of numerous interrelated components that work together to give a complete solution for managing and analyzing your company’s data assets. Here’s a breakdown of the key components of a metadata management tool:

Data Source Connectors

These are the interfaces that allow the metadata management tool to communicate with various data sources in your company, including databases, data warehouses, data lakes, and other data storage systems. Each connection is intended to operate with a specific type or brand of data source, enabling the tool to scan, categorize, and extract metadata from it.

Centralized Metadata Repository

This is the core area for organizing, storing, and managing all acquired data. The metadata repository often employs a database or other storage technology capable of efficiently storing vast amounts of organized and unstructured data. It serves as the foundation for the other parts of the metadata management tool.

Automated Metadata Extraction and Ingestion

This component scans connected data sources, extracts pertinent metadata, and stores it in the metadata repository. The engine usually automates this process, ensuring that metadata is constantly updated as new data assets are created or current ones are modified.

Comprehensive Metadata Catalog

The catalog is a user-friendly interface for searching, browsing, and discovering data assets in the metadata repository. It exposes metadata in a structured and ordered fashion, frequently utilizing categories, tags, or hierarchical relationships to help users in navigating and finding important information.

Data Lineage and Impact Analysis Module

This component depicts the flow and linkages of data throughout the company, allowing users to understand how data is generated, converted, and consumed. It frequently incorporates graphical representations of data provenance, which facilitates impact analysis and tracing the origins of specific data items.

Business Glossary and Data Dictionary

This component contributes to the standardization and definition of datasets by providing a shared set of definitions, labels, and data quality guidelines. The data dictionary concentrates on the technical features of data pieces, whereas the business glossary captures the business context and terminology around data assets.

Collaboration and Knowledge-Sharing Features

These components let different teams and personalities inside the organization communicate and collaborate more effectively. They may contain features such as commenting, annotations, notifications, and version control to enable users to share their ideas, document data-related decisions, and build on one another’s work.

Data Governance and Compliance Features

This component assists companies in developing and enforcing data governance policies, tracking data lineage, and complying with various regulatory obligations. It may include elements such as data quality monitoring, data classification, and access control to guarantee that data is handled securely and appropriately.

Understanding the components of a metadata management tool and how they interact allows you to better visualize its functionality and possibilities. This will assist you in selecting the most appropriate solution for your organization’s requirements.

Metadata Management Tools: Feature Comparison

Benefits of Using Metadata Management Tools

There are a few advantages of metadata management to consider:

• Greater Data Visibility and Consistency – Establishes a standard definition of metadata throughout the business, ensuring that competing terminology does not cause data retrieval challenges.
• Improved Data Quality – Metadata management systems almost always use automation to uncover data errors and inconsistencies in real time.
• Reduced Risk and Informed Decision Making – Data scientists have more time to study data and extract real business value, while data teams can complete projects faster.
• Increased Efficiency – The efficiency advantages and repeatable processes of metadata management reduce redundancy and excess costs, such as storage costs.
• Enhanced Collaboration – All data users may get on the same page while using their preferred tools without disruption.
• Metadata Orchestration Across Platforms – As the modern data stack evolves to meet new needs, active metadata management enables disparate systems to communicate, making data assets interoperable.

Key Considerations Before Deploying a Metadata Management Tool

1. Define Goals and Objectives

Determine the goals you hope to achieve by implementing a metadata management technology. This could include making data more discoverable, boosting collaboration, standardizing data definitions, or assuring data governance and compliance. Clearly stating your goals helps in evaluating and selecting the appropriate tool, as well as measuring its success.

2. Ensure System Compatibility

Make sure that the metadata management tool is compatible with your existing data sources, platforms, and technologies. Determine whether it includes interfaces for your databases, data warehouses, data lakes, and other storage systems – the tool’s ability to successfully classify and manage your metadata relies on seamless integration.

3. Prioritize Scalability and Performance

Choose a metadata management platform that can expand with your company and handle larger volumes of data and users. Assess the tool’s performance, such as the speed of metadata extraction, ingestion, and search capabilities, to ensure that it matches your organization’s requirements.

4. Evaluate User Experience

The tool’s interface should be intuitive and user-friendly, allowing both expert and non-technical people to deal with metadata. Ensure that the tool includes capabilities such as search, browsing, and visualization to help users find, understand, and engage with data assets.

5. Assess Customization Needs

Look for a metadata management platform that can be tailored to your organization’s unique needs, data formats, and workflows. This could include the ability to build custom metadata fields, define distinct data categories, and interface with other tools and systems.

6. Focus on Governance and Security

Ensure that the metadata management tool adheres to your organization’s data governance policies and regulatory compliance standards. This could incorporate features such as data quality monitoring, classification, and access control.

7. Consider Vendor Support and Community

Consider the level of support and resources provided by the tool’s vendor, such as documentation, training materials, and customer service. Consider the size and activity of the tool’s user community, as a large community can be an excellent source of knowledge and best practices.

8. Understand Costs and ROI

Consider the costs of adopting, maintaining, and supporting the metadata management solution, which include software licensing, hardware, and personnel. Choose a solution that maximizes value for your organization while meeting your goals and objectives.

9. Plan for Change Management

The success of a metadata management tool is determined by how successfully it’s adopted across your organization. Create a change management strategy that involves training, communication, and continuing support to ensure that users understand the tool’s benefits and know how to use it properly.

Challenges in Metadata Management and How to Solve Them

Ensuring Consistency Across Sources

A key problem in managing metadata is the large range of sources that comprise the corporate data landscape. There’s no denying that a considerable amount of every organization’s critical data exists outside of its databases. To use metadata effectively, data managers must develop a consistent and easy-to-understand structure for anything from sophisticated, high-level ETL and BI repositories to simple flat files. Building in consistency across all the data sources is crucial for data quality.

Scaling Metadata with Data Growth

The increasing scale of metadata poses questions about where to store it, how to manage it effectively, and, most crucially, how to expand the underlying architecture to handle fast-growing metadata volume while also rapidly scaling systems.

When dealing with metadata, adding more compute resources and/or developing multiple solutions to monitor and optimize the many layers of the IT stack is ineffective for scalability and performance.

Next-generation data engines are likely to play a critical role in enabling future use cases typified by data-intensive workloads requiring unprecedented scale and performance. This is especially crucial for applications that are highly dependent on response time and latency, such as traffic optimization and smart parking.

Maintaining Accurate Data Lineage

Data lineage contributes to metadata management by giving valuable historical and operational context for your data. Implementing a robust metadata management methodology, including data lineage, is challenging but worth the effort. It helps to standardize and define data sets based on business knowledge.

Good metadata management and data lineage practices are critical for any business that wants to maximize data consumption, increase data quality, and foster a data-driven culture.

Strengthening Security and Compliance

Data governance is all about the processes, rules, and procedures that ensure an organization’s data quality, management, security, and general integrity. It includes the creation and implementation of policies and standards for managing, using, storing, and safeguarding data assets.

The basic purpose of data governance is to create a framework for properly managing and utilizing an organization’s data, ensuring that it meets business requirements, conforms with legislation, and supports organizational goals.

Establishing and enforcing data governance standards is critical to ensuring data integrity. It may be difficult to define and implement consistent metadata standards, ensure compliance, and manage data access and security.

Enhancing Accessibility for Teams

Resistance to using metadata management strategies and tools might be cultural or organizational. Some teams may regard metadata management as an additional overhead, rather than appreciating its importance in boosting data interpretation and cooperation. Appropriate and ongoing training in metadata management, including its benefits to companies, can help ensure that everyone knows the value of data and can access it easily.

How lakeFS Enhances Metadata Management Tools

Managing metadata is difficult, but teams working with data lakes have even greater issues. Since raw data is frequently fed into a data lake, many teams fail to include techniques for verifying or implementing organizational data standards. The data becomes less useful for analytics because of inadequate metadata management in data lakes.

These are the four main challenges in managing metadata in data lakes:

Challenge	Description
Data Silos	In many organizations, teams keep data in separate buckets that are divided based on the path to the data. The same goes for metadata. Keeping data in separate silos that do not match has a significant impact on the organization’s ability to use metadata to serve its most vital uses.
Immutability vs. Mutability	Data lakes are immutable, whereas data is not. It’s constantly evolving. Metadata is essential for tracking changes in data collection over time and enabling concurrency across several versions of the data.
Versioning	Because data sets change over time and businesses handle a large number of datasets, metadata is critical for managing changes to various data sets over time while also preserving consistency between all datasets ingested and produced within the data lake.
Need Abstraction Layer	Data lakes are created using object storage, which treats data as objects rather than files.

Live data systems are constantly absorbing new data as different users experiment with the same datasets. This may easily lead to many copies of the same dataset, which would be challenging to maintain.

Data version control helps teams in tackling it by extending a well-known mechanism for versioning source code to the data domain. Many data versioning technologies are open source, allowing teams to experiment with them immediately.

lakeFS is an open-source data version control management system built on Git semantics and running on top of a data lake. It allows data engineers and scientists to version control their data while creating and maintaining data pipelines and machine learning models, ensuring repeatability, collaboration, and high-quality results.

While typical techniques need data copies, lakeFS takes a different approach: it functions solely at the metadata level, allowing for fast branching, reproducibility, and atomic commits without duplicating data.

lakeFS treats metadata as the source of truth, allowing teams to version data, experiment in isolation, and communicate seamlessly – whether for machine learning, analytics, or data engineering. This metadata-driven strategy ensures that data is accessible, trackable, and controllable at scale while preserving existing storage systems.

Conclusion

Metadata management tools are essential for any organization looking to improve its data strategy with a structured approach to metadata. These tools provide a centralized way to catalog, track, and govern data, ensuring better accessibility, compliance, and decision-making. By implementing the right metadata management solution, teams can enhance collaboration, improve data quality, and maintain a clear lineage of their data assets.

When it comes to data lakes, metadata management presents unique challenges, such as handling data silos, versioning, and ensuring consistency. Systems like lakeFS help address these challenges by offering a metadata-driven approach to version control, enabling efficient data governance without duplication. As data ecosystems grow more complex, leveraging metadata management tools will be critical to maintaining a scalable, high-quality, and compliant data environment.