Updated December 17th, 2024

The Snowflake Data Dictionary: Your Guide to Effective Data Management

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A Snowflake data dictionary serves as a centralized repository for metadata related to databases, schemas, and tables.

It enhances data management by providing clear documentation of data assets. This tool is essential for data discovery, governance, and collaboration among data teams.

Understanding its components and benefits is crucial for effective data governance.

Finding the right data is the hardest and the most time-consuming task in a data analyst’s/scientist’s typical work day. Data discovery and search have now become business-critical, influencing the success/failure of a data analytics project.

In addition to data catalog and data lineage that help make search and discovery easier and faster, data dictionary plays a crucial role in the process.

In the context of the Snowflake platform, how important is a data dictionary? How does a data dictionary help in establishing trust in snowflake data assets? Here, we will explore answers to these questions.

Table of contents #

  1. What is a data dictionary?
  2. Benefits of a data dictionary
  3. Snowflake Data Dictionary: The Information Schema
  4. Key components of a Snowflake data dictionary
  5. Challenge using Snowflake data dictionary
  6. Atlan: The Modern data dictionary for Snowflake
  7. How organizations making the most out of their data using Atlan
  8. FAQs about Snowflake Data Dictionary
  9. Snowflake data dictionary: Related reads

What is a data dictionary? #

A data dictionary holds the metadata for the database assets. It is the documentation for your database which describes the structure and content of the database objects.
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Quoting the Harvard Business Review,

Studies show that knowledge workers waste up to 50% of time hunting for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust.

It becomes extremely difficult to understand data without metadata which helps explain the source, nature, structure, quality, and usefulness of data.

A data dictionary is a collection of read-only tables and views that gets automatically created with every database. So one doesn’t have to probe source code, SQL queries, BI reports and depend on the tribal knowledge of the few to get more context about the data.

Learn more - Built-in data dictionary in PostgreSQL | Oracle Database | MySQL

Accessing PostgreSQL data dictionary with Amazon RDS

Accessing PostgreSQL data dictionary with Amazon RDS. Source: Amazon RDS.

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Top 4 benefits of a data dictionary #

Without a data dictionary for your Snowflake assets, one has to rely on siloed, undocumented tribal knowledge or spend lots of time digging through codebases, SQL queries, and logs.

Unified source of truth #


Information about databases, schemas, and tables is managed in a single source of truth — a data dictionary that is editable, shareable, auditable, and self-serve, as compared to knowledge spread across various teams, different tools, and chaotic access policies. A data dictionary makes new analysts’ onboarding and training much easier and more productive.

Data discovery #


The self-serve nature of the data dictionary helps analysts search and understand data needed for analysis and reporting. Understanding table relationships and foreign key constraints help analysts write better-performing SQL queries. It helps remove dependencies and bottlenecks and thereby enabling better governance and stewardship.

Trust data better #


Data profiling gives a quick snapshot of the nature and quality of data. The usability of data assets can be ascertained through: Data types, min-max values, frequency distribution, and unique, duplicate, and null values.

Database optimization #


A data dictionary is a rich source of technical metadata such as table relationships, query history, foreign key constraints, and database usage and performance. This helps DataOps engineers to monitor and tune databases for optimal performance and costs (storage and processing)

Snowflake Data Dictionary: The Information Schema #

Does Snowflake have a data dictionary? Yes, it does.

Snowflake has a built-in data dictionary known as and can be accessed through INFORMATION_SCHEMA. The Information Schema is always included within every database created in Snowflake.

The Snowflake data dictionary has the following two components:

1. Schema views: Metadata about all data assets/objects stored in the database.

2. Table functions: Metadata about historical information on storage, tasks, query history, and account-level usage.

Learn more: Using Snowflake Information Schema as a data dictionary

Data dictionary in Snowflake is accessed through Information Schema

The data dictionary in Snowflake is accessed through Information Schema. Source: Snowflake: The Definitive Guide - Joyce Kay Avila.

Building blocks of a Snowflake data dictionary #

  • Table schema, names, and descriptions
  • Table owners and relationships
  • Number of columns, rows, column names, and descriptions
  • Permissible values and validation rules for a column
  • Data types
  • Referential constraints — foreign keys and primary keys
  • Data profiling with descriptive statistics — missing values, min-max values, and histogram distribution
  • SQL query logs
  • Warehouse storage, load, and a metering history

Learn more: Reference documentation of Snowflake data dictionary

5 key challenges of using a Snowflake Data Dictionary #

The 5 key challenges of relying just on Snowflake’s native data dictionary include:

  1. Accessibility for non-technical users
  2. Absence of an intuitive search experience
  3. Support for data stored at non-snowflake databases
  4. Lack of custom metadata support
  5. Lack of data collaboration

Accessibility for non-technical/business users #


Managing and accessing a data dictionary in Snowflake is only possible by running SQL queries. This makes it harder for business users to use dictionaries for data discovery.

Writing SQL queries to access data dictionary in Snowflake

Writing SQL queries to access data dictionary in Snowflake. Source: Snowflake.



For accessing a data dictionary in Snowflake, one still needs to have a general idea of where a particular data asset comes from. This makes it difficult when you have thousands of tables spread over different databases.

Multiple data sources support #


Snowflake provides a data dictionary only for databases stored within the Snowflake warehouse. When you have data stored at non-Snowflake databases, you’ll need a centralized data dictionary tool to assimilate all data sources.

Lack of custom metadata support #


Snowflake data dictionary supports only metadata exposed through the API. It is not possible to add custom metadata — ETL logs, Airflow status, and data quality checks — to the data dictionary.

Lack of data collaboration #


As the volume of data on your Snowflake warehouse increases, access to the data dictionary alone would not be sufficient to find and trust data. Bringing in the human element through embedded collaboration — chats, crowdsourced data stewardship, and integrations with IT/task management tools — helps make data reliable and usable.

Atlan: A modern data dictionary for Snowflake #

The problem with traditional data dictionaries (that come built-in within databases) is that it is driven by passive metadata: Metadata that is the default, limiting, and doesn’t give you a complete picture of a data asset.

On the contrary, the crux of active metadata is its openness and interoperability. Atlan makes it possible to effortlessly move metadata across your data stack — data lakes, warehouses, BI tools, pipelines, ETL — and helps embed this rich context on data catalog, lineage, and data dictionary.

Atlan’s data dictionary helps you build a 360° data profile to understand your data better, search and discover data faster, and collaborate in and around data assets.

Now, let’s have a look at the key data dictionary capabilities of Atlan:

Auto-generated data dictionary #


Apart from Snowflake, Atlan’s bots automatically crawl and pull data from all the commonly used data sources like Bigquery, Databricks, Redshift, MySQL, and PostgreSQL. Atlan also connects with BI tools like Looker, Tableau, and Power BI and so it gives your data dictionary an end-to-end view of a data asset.

Learn more: How Atlan connects with multiple data sources

Snowflake data dicionary: Atlan — Automated data dictionary for Snowflake data warehouse

Atlan — Automated data dictionary for Snowflake data warehouse. Source: Atlan.

Search and discovery on an easy-to-use interface #


Atlan’s primary use case is a data catalog. It provides a Google-like search experience that lets anyone on your team — from a data engineer to a business user/analyst — search and filter data assets on an easy-to-use user interface.

Learn more: Atlan data catalog for data discovery and search

Snowflake data dicionary: Atlan data catalog provides Google-like search to discover and find the right data for your analysis

Atlan data catalog provides a Google-like search to discover and find the right data for your analysis. Source: Atlan.

Data dictionary enriched with custom metadata #


Atan’s APIs let you track all kinds of metadata — technical, administrative, business, and social — from your warehouse, ingestion, ETL, and business intelligence tools. Questions like, “when was the last time this data was updated?”, and “what are the transformations the data has gone through?” are answered right there in the data dictionary.

Learn more: Custom metadata support in Atlan

Everyone is on the same page with a business glossary #


Atlan’s data dictionary also has a business glossary — a centralized knowledge bank that lists all of the key business terms, concepts, KPIs, and metrics associated with a data asset.

Learn more: Build a business knowledge base with a data glossary

Snowflake data dicionary: A centralized repository that lists all of the key business terms and concepts used in the day-to-day operation of an organization

A centralized repository that lists all of the key business terms and concepts used in the day-to-day operation of an organization. Source: Atlan

End-to-end visibility with data lineage #


Atlan’s automated data lineage crawls data sources and parses SQL queries to build column-level lineage visualization. Data lineage helps understand relationships and dependencies between various data assets across the entire data life cycle.

Learn more: Visualize your data asset from the source to the BI dashboard

Snowflake data dicionary: Get end-to-end data visiblity from the data sources to BI dashboard with data lineage

Get end-to-end data visibility from the data sources to the BI dashboard with data lineage. Source: Atlan.

Democratize data with embedded collaboration #


Atlan seamlessly integrates with your team’s communication and project management tools. This lets you initiate conversations around a data asset right within Atlan. Crowdsource data stewardship tasks like updating documentation/READMEs, adding owners, certifying terms, and adding linked assets.

Learn more: Democratize your data team with embedded collaboration

Snowflake data dicionary: With Atlan, search and find data right from the tools you are familiar with.

With Atlan, search and find data right from the tools you are familiar with. Source: Atlan.

A Demo of the Atlan data dictionary for Snowflake data warehouse

Atlan: A Snowflake Ready Technology partner for data discovery and governance #

If you are evaluating and looking to deploy a best-in-class data catalog and data dictionary for the Snowflake data warehouse? Do give Atlan a spin.

Atlan is the first data catalog and metadata management solution approved by the Snowflake Ready Technology Validation Program.

Quoting Tarik Dwiek, Head of Technology Alliances at Snowflake,

As Snowflake continues to help companies mobilize their data, Atlan makes it possible to find, use, document, and collaborate on this data. Our partnership with Atlan was a natural fit, given how its open approach, pay-as-you-go model, and delightful user experience align with Snowflake’s own ethos.

Atlan is more than a metadata management and data cataloging tool. Atlan is built by data engineers for solving the evolving needs of modern data teams which include faster discovery, transparent data flow, robust governance, and collaboration built on open infrastructure and an easy-to-use user interface.

The deep integration and the open API enable Atlan to solve other modern data governance use cases across DataOps, workflow management, and pipeline automation.

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

FAQs about Snowflake Data Dictionary #

1. What is the Data Dictionary in Snowflake? #


The Snowflake Data Dictionary is a centralized repository that holds metadata about databases, schemas, and tables. It provides essential information for data management and governance.

2. Does Snowflake have a data catalog? #


Yes, Snowflake includes a built-in data catalog known as the Information Schema, which serves as a data dictionary for managing metadata.

3. What is a Data Dictionary in a database? #


A data dictionary in a database is a collection of metadata that describes the structure, relationships, and constraints of the data stored within the database.

4. What is Snowflake data schema? #


The Snowflake data schema refers to the organization of data within the Snowflake platform, including the structure of databases, tables, and relationships between them.

5. How does a Snowflake data dictionary enhance data management? #


A Snowflake data dictionary enhances data management by providing a unified source of truth for metadata, facilitating data discovery, and improving collaboration among data teams.

6. What are the key components of a Snowflake data dictionary? #


Key components of a Snowflake data dictionary include schema views, table functions, metadata about data assets, and historical information on storage and usage.

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