Dealing with Snowflake Unstructured Data Made Easy

Structured data is easier to analyze as it can be stored in standard relational database systems. However, businesses that solely rely on structured data are less likely to outshine their competitors that utilize both structured and unstructured data. Why? Structured data is limited in functionality when compared to unstructured data. More importantly, unstructured data accounts for up to 90 percent of stored information. 

By now, you should have a rough idea of the valuable nature of structured data. With this in mind, this post is designed to give you a comprehensive tutorial on dealing with Snowflake unstructured data. Have a read below.

What is Snowflake?

In simple terms, Snowflake is a Software as a Service (Saas) platform that offers an all-in-one platform for Data Warehousing, Data Lakes, Data Engineering, Application Development, and much much more. The platform was developed in 2012 and is built upon the Amazon Web Service, Microsoft Azure, and Google Cloud infrastructure. One of the platform’s most significant benefits is that you do not need to install any hardware, making it suitable for companies that do not want to set up physical Data Warehouses on site. Instead, everything is handled off-site over the Cloud.

The architecture of Snowflake separates its “Compute” and “Storage” units, thereby scaling differently. This allows the customers to use and pay for both services independently. It means organizations that have high storage demands but less need for CPU cycles, or vice versa, do not have to pay for an integrated bundle that requires payment for both, making it very attractive to companies.

What are the Top Features of Snowflake?

• Cloud Provider Agnostic: Snowflake is a Cloud-agnostic solution meaning it can work on all three major Cloud service providers: AWS, Azure, and Google Cloud. The benefit? You can easily fit Snowflake into your current Cloud service plan. 
• Scalability: Snowflake offers users multi-cluster Cloud technology. This implies that users can scale up resources whenever they have extensive data loads. 
• Minimal Administration: With Snowflake, users can set up the software with minimal administrative need from the IT team. This is facilitated by state-of-the-art auto-scaling features that alter warehouse size depending on the need. 
• Top-tier Security Features: It has many security features, such as two-factor authentication, tri-secret-secure, etc.

What is Unstructured Data?

From the name itself, you can infer what unstructured data is. In simple terms, this is the data that lacks any model or schema. It may be textual or non-textual, and it can either be generated by humans or machines. Due to the lack of a comprehensive model, this data cannot be stored in conventional Relational Database Systems. This has led to the need for platforms to analyze this data and draw insights.

Why is unstructured data important? Nearly 90% of all data collected by organizations is unstructured, and its volumes are growing by the day. This is the sole reason why companies cannot rely on structured data despite its benefits, such as ease of analysis and storage. 

With unstructured data, companies stand a better chance of gaining credible insights from collected information and making critical and beneficial financial decisions. With all this information in mind, what are some common examples of unstructured data? Read on to find out.

Examples of Unstructured Data

Below are some examples of human-generated unstructured data. It is worth noting that this data cannot be parsed by traditional analytic software.

• Media: This includes digital photos, video clips, and photos. 
• Mobile Communication Media: This may include phone recordings, chats, and text messages. 
• Emails: Emails sent between users fall under this category. 
• Text: This encompasses word files from MS Word, Spreadsheets, Presentations, and Log Files. 
• Social Media and Websites: This comprises information from Social Media Platforms such as Facebook and Instagram.

Dealing with Snowflake Unstructured Data

Before we get to unstructured data, let us first see how standard Databases handle this information. Most Relational Databases use two varied techniques while dealing with unstructured data, as outlined below. 

• Method 1: Some Relational Databases will flatten unstructured data into structured columns, optimizing performance in the process. 
• Method 2: Other Relational Databases will provide a different data type to store the unstructured data. This technique offers slower performance since all the data needs to be parsed during query time. 

Snowflake unstructured data uses a technique similar to Method 2 listed above. Accordingly, the platform provides the variant data type that deals with unstructured data. It is worth noting the maximum size for this data type is 16MB. The platform also uses keys to create columns for the unstructured data. One of the most significant benefits of this approach is query optimization using the variant data type.

Snowflake Unstructured Data Storage

Storing Snowflake’s unstructured data is a task that falls upon the Data Architecture Administrator. Accordingly, these individuals can either use internal or external storage devices. Accessing the files is a no-hassle affair. You need only use the GET REST API. Below is the URL format for the same:

https://<account>.snowflakecomputing.com/api/files/<db_name>/<schema_name>/<stage_name>/<file_path>

As a Cloud-agnostic Data Warehouse solution, Snowflake makes securing unstructured data pretty easy. You need only utilize easy-to-use commands to secure the files. For instance, you can grant access to the files using the GRANT and REVOKE statements. You should use the following commands to grant access to the files:

grant read on @unst_info to role data_manager;

grant read on @unst_info to role security_manager;

You can also access Snowflake’s unstructured data using URLs. Accordingly, there are three different URL types you can use as shown below:

• Pre-signed URLs: These URLs are already verified and users can simply use them to access the files they need. 
• Stage file URLs: For these URLs, users need to be verified on Snowflake. Furthermore, they need read privileges for the files they want to access. 
• Scoped URLs: Scoped URLs are used to grant users temporary access to unstructured Snowflake files. 

How about cases when users need to process unstructured data? Here, the platform supports external functions in Cloud platforms such as AWS, Azure, and Google Cloud. Using such commands, users can extract text from unstructured images, use Machine Labels to identify trademarks from images, and process pdf files. 

There it is. Using this technique, Snowflake’s unstructured data is now easy to handle. It is worth noting that this is a relatively new feature being introduced in 2020. Due to the sheer magnitude of unstructured data generated daily, Snowflake made a wise decision to introduce native support for this data type. 

Conclusion

Snowflake is a virtual Data Warehouse that has become an industry-leading Cloud-Based SaaS Data Platform. Snowflake is always seeking ways to improve and enhance its data offerings thereby, making it a Data Warehouse of choice.

In this post, you got a thorough introduction to Snowflake and its features. More importantly, you learned what unstructured data is and its benefits for business. Next up, you learned how to deal with unstructured data in detail.

With all this information at your fingertips, you can now harness the benefits of unstructured data using Snowflake. However, Snowflake supports many more Data Sources and allows loading data from other traditional and Cloud-based applications, SaaSs, CRMs, BI tools, etc. You may require a Data Integration tool like Hevo Data to extract and load data from multiple sources into Snowflake.

Share your experience of working with Snowflake unstructured data in the comments section below.