Xenonstack Recommends

Understanding Data Catalog for Snowflake

Acknowledging Data Management
          Best Practices with DataOps


Introduction to Data Catalog for Snowflake

Organizations are investing in their data and analytics capabilities, they want their projects to complete rapidly and perfectly. Enterprises are trying to understand all the data within and external to Snowflake in the enterprise. Data Catalog for snowflakes helps to observe their implementations and real-time analysis so that they can get immediate value. Snowflake is the cloud data warehouse that provides the storage to store and analyze all your enterprise's data in one location. It provisions data storage repositories to ingest structured data for reporting and data analysis. Snowflake's capability of accepting mountains of unrefined data from numerous sources in various formats also makes it an attractive Data Lake solution to many IT decision-makers.
Snowflake developed a strategy to win both the data warehouse and big data battles by building on the achievements of the data warehouse, the flexibility of systems. Source: Snowflake's Vision For The Data Warehouse

What is a Data Catalog?

A data catalog is an organized record of data assets that uses metadata to help organizations manage their data. These assets can include structured data in tables and unstructured data in documents, web pages, email, mobile data, images, audio, video, and reports. The various features of the data catalog are:
  1. Serverless: It is a fully managed and scalable metadata management service that needs no infrastructure.
  2. Metadata as a Service: It is a metadata management service for classifying data assets via custom APIs and the UI, thus providing a unified view of data.
  3. Central Catalog: It provides a versatile and powerful cataloging system for capturing technical metadata and business metadata in a structured format.
  4. Search and Discovery: It provides a simple and easy-to-use user interface with powerful search capabilities to quickly and easily find data assets.
  5. Schematized Metadata: It Supports schematized tags (e.g., Enum, Bool, DateTime) and provides rich and organized business metadata to organizations.
  6. Cloud DLP Integration: Discovers and classifies sensitive data, provides intelligence, and simplifies the process of governing data.

What are the Benefits of the Data Catalog?

Listed below are the main benefits of the Data Catalog.
  • A Better Understanding of Data: It provides a better understanding of data through improved and clear content. Analysts can better understand data with detailed descriptions and comments from other data citizens.
  • Increased Speed and Efficiency: Employees can access data with enhanced speed and efficiency.
  • Reduced Risk: Analysts can quickly review annotations and metadata with the help of a data catalog to spot null fields or incorrect values that can impact analysis, enhancing security and reducing risks.
  • Improved Data Analysis: Better the data, easier is the process to analyze it.

Read more about GCP Data Catalog – A Complete Guide to Metadata Management Service

What are the Functions of Data Catalog?

There are several key functions of the Data Catalog, some of them are listed below:
  • Dataset Searching

Data Catalog includes vigorous search capabilities such as search by facets, keywords, and business terms. Nontechnical users can take the benefit of natural language search capabilities. Ranking search results by relevance and frequency of use is particularly useful and beneficial.
  • Dataset Evaluation

Choosing the right datasets depends on evaluating their suitability for an analysis use case without downloading or acquiring data first. Important evaluation features include capabilities to preview a dataset, view all associated metadata, check user ratings, view user reviews and curator annotations, and view data quality information.
  • Data Access

The way from search to evaluation and then to data access should be a seamless user experience. The catalog should know the access protocols and should be capable of providing access directly. Its functions provide access protections for security, privacy, and compliance-sensitive data. A robust data catalog provides many other capabilities, including support for data curation and collaborative data management, data usage tracking, intelligent dataset recommendations, and various data governance features.
Read more about Snowflake Cloud Data Warehouse

Data Catalog and the Snowflake Data Exchange

Snowflake Data Exchange is an analytic data warehouse provided as SaaS ( Software-as-a-Service). It facilitates a data warehouse that is faster, efficient, and much easier and flexible to use than any other traditional data warehouse offerings. Unlike the other data warehouses, Snowflake's data warehouse is not built on an existing database or big data software platform such as Hadoop. Instead, it uses a new SQL database engine with a unique architecture designed for the cloud. It is similar to other data warehouses, but it provides various additional functionalities and capabilities. The Snowflake Data Exchange is a marketplace that allows Snowflake customers to access data from providers and discover, access, and generate insights. Snowflake Data Exchange is straightforward to use for its customers. Customers can easily connect to Data Exchange from their respective Snowflake accounts. They can instantly browse a data catalog they want to and can securely access data. To join with existing Snowflake data sets. This platform improves data exchange control, speed, and security and makes data integration and querying simple without the need to transfer data via API or extract data to cloud storage. By easily connecting with the Data Exchange from their Snowflake account, customers can instantly browse a data catalog. To find and securely access data to join existing Snowflake data sets.


In today's world, much data is generated from various applications. It is challenging and difficult to manage such a large amount of data. Data catalogs help us overcome these challenges. Active data curation (storing data in a shared database) is a core reason for data catalogs' success and a critical practice for modern data management.

Related blogs and Articles

Modern Data Warehouse Services, Architecture and Best Practices

Enterprise Data Management

Modern Data Warehouse Services, Architecture and Best Practices

What is Modern Data Warehouse? Modern Data warehouse comprised of multiple programs impervious to User. Polyglot persistence encourages the most suitable data storage technology based on data. This "best-fit engineering" aligns multi-structure data into data lakes and considers NoSQL solutions for JSON formats. Pursuing a polyglot persistence dat strategy benefits from virtualization and takes...