New: Bulk Metadata Made Simple with Export/Import

The new Export/Import module empowers data teams to manage metadata in bulk—enabling fast onboarding, easy catalog updates, and streamlined governance through simple CSV workflows. Save time, reduce errors, and maintain control with secure, auditable bulk operations.

By

Melanie Y

Updated on

August 25, 2025

Introducing Decube’s Export-Import Module: Bulk Metadata Management Made Easy

We’re excited to announce the release of Decube’s Export-Import module—a powerful new way for data governance stewards and catalog users to manage metadata at scale. This feature is designed to streamline bulk updates, onboarding, and catalog maintenance, empowering your team to work faster and more efficiently.

Why Export-Import Matters

Managing metadata across a growing data landscape can be tedious and error-prone when done manually. With the Export-Import module, you can now perform large-scale updates, onboard new assets, and clean up your catalog—all through simple, structured CSV workflows. This means less time spent on repetitive tasks and more time focused on driving data value.

What’s New: Key Capabilities

  • Export existing metadata for bulk review and editing.
  • Download blank templates to add new glossary objects or classification policies.
  • Import completed CSVs to apply updates or create new records in Decube.
  • Supported objects:
    • Catalog: datasets, dashboards, charts, data jobs
    • Glossary: glossaries, categories, terms
    • Classification Policies
  • Access control: Only users in the Owner group can perform export/import operations, ensuring governance and security.

How It Works: Step-by-Step

  1. Export: Download current metadata or blank templates from the Export/Import tab.
Export existing metadata from your Catalog and Glossary.

Edit: Make your changes or add new entries in the CSV, following the provided template structure.

Import: Upload your CSV file. Decube validates your data, flags errors, and logs every operation for full traceability.

Upload the CSV to the right operation to import all metadata updates.

Review: Use the History tab to track all export/import actions, download error reports, and ensure successful updates.

Best Practices & Governance

  • Use official templates: Always start with the provided templates from the UI to ensure your data matches Decube’s required schema and avoid import errors.
  • Validate before import: Double-check your data for accuracy and completeness before uploading. Imports are final and cannot be undone.
  • Keep backups: Save copies of your CSV files before making bulk changes, so you can easily revert or audit changes if needed.
  • Leverage access controls: Only Owners can access these features, maintaining strict control over bulk changes and reducing the risk of unauthorized updates.
  • Collaborate and assign ownership: Ensure that the right team members are responsible for preparing and reviewing import files to maintain data quality.
  • Audit and review regularly: Use the History tab to monitor all export/import actions, download error reports, and maintain a clear audit trail for compliance and transparency.

Real-World Use Cases

  • Faster onboarding: Instantly export and import catalog assets without waiting for support, so your team can get started right away.
  • Glossary management: Upload or update business terms and glossaries from Excel in bulk, keeping your data governance up to date.
  • Catalog standardization: Clean up and standardize table descriptions, tags, or owners across hundreds of assets in a single operation.

Getting Started

Ready to experience a new level of efficiency? Head to the Export/Import tab in your Decube workspace.

Download the templates, follow the documentation, and see how easy metadata management can be. Detailed explanation can be found in the documentation here.

What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.

Table of Contents

Read other blog articles

Grow with our latest insights

Sneak peek from the data world.

Thank you! Your submission has been received!
Talk to a designer

All in one place

Comprehensive and centralized solution for data governance, and observability.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
decube all in one image