Unified Control Plane for Data: The Future of Data Cataloging

Updated October 15th, 2024

Share this article

Enterprises need a unified control plane for data to ensure consistent governance, compliance, and interoperability across diverse, multi-cloud environments.

While data catalogs were initially designed for this, expectations have evolved, demanding broader capabilities within today’s data ecosystem.
Unlock Your Data’s Potential With Atlan – Start Product Tour

At its most basic level, a data catalog is a technical catalog that stores all the technical metadata, such as table structures and data types. A step above this is the data dictionary, which adds business context to technical metadata (through comments and tags).

As the data ecosystem continues to expand, a “catalog of catalogs” has emerged — a unified aggregator of metadata from all the native technical catalogs and data dictionaries across your data landscape.

This article explores the future of data cataloging and how a unified control plane can bring together these various catalog types. Let’s begin by understanding the concept of a unified control plane for data.

Table of contents #

  1. What is a unified control plane for data?
  2. Types of catalogs: A comparison guide on their scope, advantages, and disadvantages
  3. A lakehouse for metadata: The foundation for a unified control plane
  4. Inception of a neutral control plane for data
  5. Related reads

What is a unified control plane for data? #

A unified control plane for data is a single platform where you can search, discover, access, and govern your data assets across all the components of your data ecosystem. This provides true end-to-end visibility for data in your organization.

Also, read -> The unified control plane in action

Native data cataloging tools may claim to offer end-to-end visibility, but they usually only cover specific components. This limitation becomes evident when tracking data lineage, quality, transformation, or governance, which often involve multiple systems across various platforms.

While integrating multiple catalogs through a common protocol and metadata layer is possible, it requires significant engineering work. Instead, a purpose-built control plane can offer a simpler, more efficient solution.

Let’s put this in context by comparing the scope, advantages, and disadvantages of these different types of catalogs.

Types of catalogs: A comparison guide on their scope, advantages, and disadvantages #

Catalog type

Scope

Advantages

Disadvantages

Technical catalog

Specific components like databases and data warehouses.

Collects native metadata for search, discovery, and governance.

Typically lacks a user interface for business users; focused on engineers and technical data analysts.

Data dictionary

Same as the technical catalog, but also includes components from the consumption layer (BI tools).

Enhances technical catalogs with business context.

Embedded within BI or specific tools, covering only a single system.

Catalog of catalogs

Aggregates metadata from various tools across the data ecosystem.

Integrates multiple catalogs into a single layer.

Requires extensive product and engineering work, essentially, a dedicated team. Despite that, it won’t solve all use cases across search, discovery, and governance of data.

Control plane for data

Covers all systems, including those beyond native catalog capabilities (orchestration engines and transformation tools).

Centralizes metadata into a lakehouse for search, discovery, and governance, irrespective of where your data assets are physically hosted.

A new tool requiring adoption across the organization.

All types of data catalogs – native technical catalogs, enhanced data dictionaries, catalog of catalogs – bring some value to business. However, they aren’t often enough to cater to the whole enterprise in a way that empowers all users to leverage the data securely and efficiently.

A unified control plane for data solves this problem by creating a metadata lakehouse that integrates with diverse tools and supports users across the business. Let’s explore the concept of a lakehouse for metadata.

A lakehouse for metadata: The foundation for a unified control plane #

The original specification for the data lakehouse had metadata layers for data management as part of the design, but those were mostly meant for the data platform’s internal operations and basic search and discovery for developers.

In principle, a lakehouse for metadata is very similar to a regular data lakehouse, but focuses on metadata management. Its purpose is to support search, discovery, and governance across all data assets in an enterprise, regardless of where the data is stored.

The key capabilities of a control plane that’s based on a metadata lakehouse architecture include:

This approach enhances search, discovery, and governance, while also enabling:

  • Workflows and automation pipelines for deletion or deprecation of data assets to save costs
  • Automatic tagging, synchronization, and propagation of tags across systems

Let’s now explore the specifics of such a unified control plane for data.

Inception of a neutral control plane for data #

It’s worth repeating that modern data environments usually span across multiple cloud and data platforms, each with its own metadata implementation.

Such a setup calls for a neutral entity, an interpreter of sorts, that can manage governance, compliance, and context across the board. That’s what the unified control plane for data brings to the table.

Atlan combines the design and architecture principles of interoperability, openness, and neutrality to deliver a truly unified control plane for data – equipped to handle diverse tools and users within your data ecosystem. It prepares your organization to make the best use of data for core use cases, including AI and analytics.

As generative AI capabilities grow, having structured and unstructured data that is discoverable and trustworthy—i.e., AI-ready—is crucial. Atlan’s unified control plane ensures that your data ecosystem is prepared for this future.

Want to understand how Atlan fits into your data and AI plans? Speak to our experts

Share this article

[Website env: production]