Creating a Data Dictionary via Snowflake Query: A Step-by-Step Guide

As a data analyst or engineer, you understand the importance of having a comprehensive data dictionary that provides a clear understanding of your organization’s data assets. A data dictionary is a centralized repository that documents data elements, their meanings, and relationships, making it easier to manage and utilize data effectively. Snowflake, a popular cloud-based data warehousing platform, provides an efficient way to create a data dictionary using SQL queries. In this article, we’ll delve into the process of creating a data dictionary via Snowflake query, providing you with a step-by-step guide to get started.

Why Create a Data Dictionary?

A data dictionary offers numerous benefits, including:

• Improved data governance: A data dictionary ensures that data elements are properly defined, reducing data quality issues and improving overall data governance.
• Enhanced data discovery: With a data dictionary, users can easily discover and understand available data assets, fostering data-driven decision-making.
• Increased collaboration: A centralized data dictionary facilitates collaboration among teams, reducing misunderstandings and misinterpretations of data.
• Better data management: A data dictionary helps in identifying data relationships, dependencies, and redundancies, enabling efficient data management.

Prerequisites for Creating a Data Dictionary in Snowflake

Before we dive into the process of creating a data dictionary, ensure you have:

• A Snowflake account with a valid username and password.
• A Snowflake warehouse with at least one database and schema.
• A basic understanding of SQL and Snowflake query syntax.

Gathering Information for the Data Dictionary

To create a comprehensive data dictionary, you’ll need to gather information about your Snowflake database objects, including:

• Tables: List all tables in your database, including their names, descriptions, and schema names.
• Columns: Identify all columns in each table, including their names, data types, and descriptions.
• Constraints: Document primary keys, foreign keys, and other constraints defined on tables.
• Views: Include views in your data dictionary, listing their names, descriptions, and underlying queries.
• Sequences: Record sequence objects, including their names, data types, and current values.

Creating the Data Dictionary using Snowflake Query

Now that you’ve gathered the necessary information, let’s create the data dictionary using Snowflake query. We’ll create separate tables for each object type, and then combine the data into a single table for easy reference.

Creating the TABLES Table

CREATE TABLE DATA_DICTIONARY.TABLES (
  TABLE_NAME VARCHAR(256),
  TABLE_DESCRIPTION VARCHAR(1024),
  SCHEMA_NAME VARCHAR(256),
  CREATED_AT TIMESTAMP,
  LAST_ALTERED_AT TIMESTAMP
);

INSERT INTO DATA_DICTIONARY.TABLES
SELECT TABLE_NAME, TABLE_COMMENT, SCHEMA_NAME, CREATED, LAST_ALTERED
FROM INFORMATION_SCHEMA.TABLES;

Creating the COLUMNS Table

CREATE TABLE DATA_DICTIONARY.COLUMNS (
  TABLE_NAME VARCHAR(256),
  COLUMN_NAME VARCHAR(256),
  DATA_TYPE VARCHAR(128),
  COLUMN_DESCRIPTION VARCHAR(1024),
  ORDINAL_POSITION INTEGER,
  IS_NULLABLE VARCHAR(3)
);

INSERT INTO DATA_DICTIONARY.COLUMNS
SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE, COLUMN_COMMENT, ORDINAL_POSITION, IS_NULLABLE
FROM INFORMATION_SCHEMA.COLUMNS;

Creating the CONSTRAINTS Table

CREATE TABLE DATA_DICTIONARY.CONSTRAINTS (
  TABLE_NAME VARCHAR(256),
  CONSTRAINT_NAME VARCHAR(256),
  CONSTRAINT_TYPE VARCHAR(128),
  COLUMN_NAME VARCHAR(256),
  REFERENCED_TABLE_NAME VARCHAR(256),
  REFERENCED_COLUMN_NAME VARCHAR(256)
);

INSERT INTO DATA_DICTIONARY.CONSTRAINTS
SELECT TABLE_NAME, CONSTRAINT_NAME, CONSTRAINT_TYPE, COLUMN_NAME, REFERENCED_TABLE_NAME, REFERENCED_COLUMN_NAME
FROM INFORMATION_SCHEMA.CONSTRAINTS;

Creating the VIEWS Table

CREATE TABLE DATA_DICTIONARY.VIEWS (
  VIEW_NAME VARCHAR(256),
  VIEW_DESCRIPTION VARCHAR(1024),
  SCHEMA_NAME VARCHAR(256),
  VIEW_DEFINITION VARCHAR(4000)
);

INSERT INTO DATA_DICTIONARY.VIEWS
SELECT VIEW_NAME, VIEW_COMMENT, SCHEMA_NAME, VIEW_DEFINITION
FROM INFORMATION_SCHEMA.VIEWS;

Creating the SEQUENCES Table

CREATE TABLE DATA_DICTIONARY.SEQUENCES (
  SEQUENCE_NAME VARCHAR(256),
  SEQUENCE_SCHEMA VARCHAR(256),
  DATA_TYPE VARCHAR(128),
  CURRENT_VALUE INTEGER,
  MIN_VALUE INTEGER,
  MAX_VALUE INTEGER
);

INSERT INTO DATA_DICTIONARY.SEQUENCES
SELECT SEQUENCE_NAME, SEQUENCE_SCHEMA, DATA_TYPE, CURRENT_VALUE, MIN_VALUE, MAX_VALUE
FROM INFORMATION_SCHEMA.SEQUENCES;

Combining Data into a Single Table

CREATE VIEW DATA_DICTIONARY.ALL_OBJECTS AS
SELECT 'TABLE' AS OBJECT_TYPE, TABLE_NAME, TABLE_DESCRIPTION, SCHEMA_NAME, CREATED_AT, LAST_ALTERED_AT
FROM DATA_DICTIONARY.TABLES

UNION ALL

SELECT 'COLUMN' AS OBJECT_TYPE, TABLE_NAME, COLUMN_NAME, DATA_TYPE, COLUMN_DESCRIPTION, ORDINAL_POSITION, IS_NULLABLE
FROM DATA_DICTIONARY.COLUMNS

UNION ALL

SELECT 'CONSTRAINT' AS OBJECT_TYPE, TABLE_NAME, CONSTRAINT_NAME, CONSTRAINT_TYPE, COLUMN_NAME, REFERENCED_TABLE_NAME, REFERENCED_COLUMN_NAME
FROM DATA_DICTIONARY.CONSTRAINTS

UNION ALL

SELECT 'VIEW' AS OBJECT_TYPE, VIEW_NAME, VIEW_DESCRIPTION, SCHEMA_NAME, VIEW_DEFINITION
FROM DATA_DICTIONARY.VIEWS

UNION ALL

SELECT 'SEQUENCE' AS OBJECT_TYPE, SEQUENCE_NAME, SEQUENCE_SCHEMA, DATA_TYPE, CURRENT_VALUE, MIN_VALUE, MAX_VALUE
FROM DATA_DICTIONARY.SEQUENCES;

Querying the Data Dictionary

Now that we have a comprehensive data dictionary, let’s explore some ways to query the data.

Querying All Objects

SELECT * FROM DATA_DICTIONARY.ALL_OBJECTS;

Querying Specific Tables

SELECT * FROM DATA_DICTIONARY.ALL_OBJECTS
WHERE OBJECT_TYPE = 'TABLE'
AND TABLE_NAME = 'MY_TABLE';

Querying Column Information

SELECT * FROM DATA_DICTIONARY.ALL_OBJECTS
WHERE OBJECT_TYPE = 'COLUMN'
AND TABLE_NAME = 'MY_TABLE'
AND COLUMN_NAME = 'MY_COLUMN';

Conclusion

Creating a data dictionary via Snowflake query provides a centralized repository of your organization’s data assets. By following this step-by-step guide, you’ve successfully created a comprehensive data dictionary, enabling improved data governance, discovery, and management. Remember to regularly update your data dictionary to reflect changes in your Snowflake database.

Best Practices for Maintaining a Data Dictionary
Regularly update the data dictionary to reflect changes in your Snowflake database.
Establish a naming convention for tables, columns, and other objects to ensure consistency.
Document data lineage and data quality issues to improve data governance.
Use Snowflake’s built-in features, such as data masking and row-level security, to enhance data security.

By following these best practices, you’ll ensure your data dictionary remains a valuable resource for your organization, supporting data-driven decision-making and strategic growth.

Further Reading

For more information on Snowflake and data dictionary best practices, check out the following resources:

• Snowflake Documentation: https://docs.snowflake.com/en/
• Data Dictionary Best Practices: https://www.dataversity.net/data-dictionary-best-practices/

Frequently Asked Questions

Get the scoop on creating a data dictionary via Snowflake query!