Snowflake is a Cloud Data Warehouse solution used for the storage of data. It provides its users with many benefits including Security and Scalability. Due to this, organizations are moving their data from traditional Data Storage to Snowflake. Organizations are also moving their data from big data platforms like Hadoop and Teradata into Snowflake. A single Snowflake account may have up to 10 databases, each containing thousands of Views, Tables, and Columns. Multiple users from different departments within the organization will be running queries and executing jobs to meet different business needs.
This means that proper management of the data and queries run by the users is necessary. We need to know the relationship between different tables and views, the most important columns for a table, the frequently accessed columns of a table, and more. An enterprise-wide Snowflake Data Catalog is the best approach to this. It will make it easy for the organization to manage its data stored in Snowflake. In this article, we will be discussing the Snowflake Data Catalog in detail.
What is Snowflake Data Catalog?
A Data Catalog refers to an organized record of data assets that uses Metadata to facilitate Data Management in an organization. Such data assets include Structured data stored in tables and Unstructured data stored in Web Pages, Documents, Emails, Videos, Audio, Mobile data, and Reports.
A Snowflake Data Catalog helps organizations to answer the following data questions:
- Which organization is using which type of data?
- How are the views and tables related to each other?
- When was the data updated last?
- What are the most important columns in a table?
Benefits of Snowflake Data Catalog
Below are the benefits offered by the Snowflake Data Catalog:
1. Search and Discovery
The Snowflake Data Catalog has powerful search capabilities to help users easily and quickly find data assets. Users can search for the relevant information and get it with much ease.
2. Low Data Integration Costs
A direct and governed access to ready-to-query data virtually eliminates the traditional ETL data ingestion and transformation steps and costs.
3. Faster Access to Fresh Data
The Snowflake Data Catalog eliminates the hassle of copying state data and moving it to Snowflake via the Snowflake secure data sharing technology. It facilitates access to Live, Shared, Governed data sets. Users also get updates made to the data in real-time.
4. Data Quality Monitoring
The Snowflake Data Catalog comes with advanced quality check features to check for duplicates, formatting issues, missing values, and more in the organization data. This is good for quality data in an organization.
5. Data Lineage
With the Snowflake Data Catalog, it is easy to track the data journey like the Data Origin, Transformations, and Destination. This helps in tracking the changes that have been made to data to facilitate impact and root cause analysis.
Top Snowflake Data Catalogs
Given below are the best Data Catalogs for Snowflake:
1. Dataedo
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This is an on-premises Data Catalog and Metadata management tool. It uses a business glossary, data dictionary, and ERDs (Entity Relationship Diagrams) to help you document, catalog, and understand your Snowflake data. Dataedo reads your Data Schema and helps to describe each data element with ease.
Dataedo has 3 pricing plans with the cheapest plan costing $49/month/user.
2. Alation Data Catalog
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This is another Snowflake Data Catalog with data intelligence features like Data Governance, Data Search & Discovery, Digital Transformation, and Analytics. It also has a very powerful Behavioral Analysis Engine, open interfaces, and inbuilt collaboration capabilities. It combines human insight and machine learning capabilities to handle challenges related to data management.
To know more about its pricing, you can talk to their sales specialists via its live chat feature.
3. Lumada Data Catalog
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This is another Snowflake Data Catalog that combines AI, patented fingerprinting technology, and Machine Learning to automate data discovery, classification, and maintenance. It facilitates easy access to data and enhances collaboration, helping the organization to use its data more intelligently.
You can request a demo before committing yourself to using it.
4. Tree Schema
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This is another option for a Snowflake Data Catalog. It comes with all essential Data Catalog features including Data Lineage, Rich-Text Documentation, Tagging your Assets, Assigning Technical owners and Data Stewards to your datasets, and more. You can point to this Data Catalog and populate your catalog fully in less than 5 minutes. The Tree Schema Data Catalog can support modern sources of data such as Kafka, S3, and DynamoDB.
This Data Catalog has a free plan that supports up to 5 users, 1 data source and 3 other subscription-based plans.
5. Atlan
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Atlan is a modern Snowflake Data Catalog that runs natively in the cloud. It is easy to use and comes with a simple user interface that makes it useable to diverse individuals including data stewards, engineers, and business owners. It helps them to understand, discover insights, and trust their data. Atlan uses a bots ecosystem and machine learning to automate stewardship tasks like automatic data profiling, glossary tagging, and data quality alerts.
Atlan runs on an Open API architecture and uses a pay-as-you-go pricing model, meaning that it can be used by teams of all sizes.
6. Stemma
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This is a fully-managed Snowflake Data Catalog powered by Amundsen. Stemma bridges the gap between the data producers and consumers, helping them to trust their data. It also provides richer Metadata and enterprise management. With Stemma, organizations can easily find trustworthy data. It automatically documents data usage patterns, giving organization users an up-to-date view of their data usage.
You can contact the Stemma team for a free demo of how their product works.
Conclusion
A Snowflake Data Catalog will make it easy for organization users to find and understand the data stored in Snowflake. The catalog is also good for data quality monitoring as it checks for inconsistencies in the data, for example, data duplicates, missing values, and more. There are different options when looking for a Snowflake data catalog. The best option for you will depend on the feature that you are looking for.
As your business begins to grow, data is generated at an exponential rate across all of your company’s SaaS applications, Databases, and other sources. To meet this growing storage and computing needs of data, you would require to invest a portion of your Engineering Bandwidth to Integrate data from all sources, Clean & Transform it, and finally load it to a Cloud Data Warehouse such as Snowflake for further Business Analytics. All of these challenges can be efficiently handled by a Cloud-Based ETL tool such as Hevo Data.
Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations such as Snowflake, with a few clicks. Hevo Data with its strong integration with 150+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!
Share your experience of learning about Snowflake Data Catalog in the comments below!
Nicholas Samuel is a technical writing specialist with a passion for data, having more than 14+ years of experience in the field. With his skills in data analysis, data visualization, and business intelligence, he has delivered over 200 blogs. In his early years as a systems software developer at Airtel Kenya, he developed applications, using Java, Android platform, and web applications with PHP. He also performed Oracle database backups, recovery operations, and performance tuning. Nicholas was also involved in projects that demanded in-depth knowledge of Unix system administration, specifically with HP-UX servers. Through his writing, he intends to share the hands-on experience he gained to make the lives of data practitioners better.
