The value of data in the worldwide economy is rising rapidly. Forecasts put the value of the worldwide data monetization industry at $6.1 billion by 2025, up from an expected $2.3 billion in 2020. This is a CAGR (Compound Annual Growth Rate) of 21.7%.
Enterprises can use Snowflake Data Sharing as they continue to utilize the Snowflake Data Cloud as their data storage platform.
By pooling resources, both producers and consumers can more easily distribute and use data.
In this article, we’ll examine Snowflake sharing data, the Snowflake data marketplace, and others.
First, data sharing lets you send specific objects to a different Snowflake account or a reader account. Sharing information eliminates the need to duplicate or move data between storage locations.
What makes this such a huge win for businesses, anyway? Data movement across databases and other systems for the purpose of blending data is a typical practice while developing data pipelines as well as data products.
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Let’s imagine you want to build a machine learning model using both internal data from your online transaction processor (OLTP) database along some external data. In the past, businesses would send data off to a data lake, then bring in data from elsewhere and analyze it using a technology like Apache Spark.
What if, instead of manually loading data from other sources, you simply copied it into Snowflake, and everyone in your company could benefit from it? This eliminates the burden of maintaining consistent data copies, which reduces the need for space, computing resources, and upkeep.
Picture this: other businesses are able to benefit from the valuable data your organization has produced. What if, for instance, your company had access to private information that allowed it to accurately predict how long it would take to ship a product, while you wanted to offer that data to your clientele?
Snowflake facilitates this type of data sharing.
You can choose from several data-sharing mechanisms provided by Snowflake. Depending on your needs, you can choose to either restrict access to your data to a certain group of people or make it publicly available.
Direct sharing, data exchange, and the Snowflake Data Marketplace are all options for sharing data in Snowflake. Direct shares are the bare bones of data sharing; they only communicate directly between two accounts. The goal of both data exchanges as well as the Snowflake Data Marketplace, is to make your data available to a wider audience.
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While the Snowflake Data Marketplace is accessible to all Snowflake accounts outside of VPS areas, data exchanges remain the central center for safe collaboration between invited users.
If you’re looking to either make money off of your data or trade it with other businesses, you’ll need to decide how you’ll be doing it. Like:
You can choose to share data with another Snowflake account or with reader accounts when sharing information through a share. The Snowflake account that receives the shared data is accountable for maintaining the compute units (virtual warehouses) used for querying the data.
An alternative to creating a Snowflake account for the final customer is creating a reader account. In order to do this, you can use the data supplier’s own compute objects.
Snowflake offers secure and materialized views while securing UDFs for safe data sharing. These objects give readers access to data without revealing the underlying SQL used to get readable results.
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By design, secure [materialized] views don’t take advantage of optimizations that could inadvertently reveal business logic, and they also give consumers little access to the underlying data.
The Snowflake Data Marketplace brings together data suppliers and buyers via Snowflake Secure Data Sharing.
Users can browse and purchase various third-party datasets in the Snowflake Data Marketplace. Now there is a centralized hub where businesses can go to find the data they need to fuel their machine learning and analytics models.
You can choose between a Standard Listing or a Personalised Listing when publishing data to a Snowflake Data Marketplace.
Consumers can quickly find public data sets using Standard Data Listings, which often give generic, compiled, or non-customer-specific information.
Consumers can inquire for unique data sets using Personalised Data Listings. Providing a broad standard data listing as well as letting data consumers request more individualized data as needed can be profitable strategies for data producers.
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Let’s walk through the process of generating a data share, a secure view, as well as sharing it both immediately and with the Snowflake training in Hyderabad once we’ve covered the different kinds of data sharing and how you can benefit from them.
In this scenario, we wish to deliver data sharing to consumers that includes both customer as well as marketing data. Providing consumers with this data will need the development of:
You can control who has access to your personal information and avoid problems with data synchronization when you use Snowflake as a data provider.
Data stays in Snowflake, which makes it simple to link shared datasets with your own, reducing the amount of data transformations that you require to conduct as a data consumer.
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You can add information to your data share, including a title as well as description, as well as usage examples, when you distribute it through listings. Access to usage data, seamless replication to other regions, and the option to charge for or make selected datasets publicly available on the Snowflake Marketplace are just some of the perks for providers, on top of the advantages for end users.
With Listings, you can easily collaborate with users in other Snowflake regions and across clouds without having to replicate your data manually. Listings allow you to add more context to the data you publish, see how your customers are using it, and, for listings you make available to the public on the Snowflake Marketplace, get a sense of how interested people are in what you have to offer.
Direct sharing is an alternative to using a listing to distribute information. At Snowflake, we use Secured Data Sharing for all of our file-sharing needs. No matter what you decide, Reader Accounts will allow you to share with others who lack Snowflake accounts.
Data Exchanges are useful for managing groups of accounts and figuring out which users can add, remove, and modify entries within a certain group.
You can choose to make a listing exclusive to certain accounts, or you can make it available to everyone on the Snowflake Marketplace.
You can begin sharing and using listing-related data after agreeing to the supplier and consumer conditions. Check out the Listings FAQ for additional details.
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If you want to share information with another account in the same thing Snowflake area, use a Direct Share. A direct share eliminates the need to duplicate or transfer data.
If you want to share information with a small number of accounts but don’t want to create listings that you’ve only offered to those accounts, a data exchange is a good alternative.
After having a data exchange built and set up for your account, you may begin inviting members and defining their roles inside the exchange as either consumers, providers, or both.
All Snowflake accounts on all authorized cloud providers outside of VPS areas can use the Data Exchange without any problems.
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You should now have a better grasp of how data sharing operates in Snowflake and the groundwork to begin sharing (and selling) your data. If your company needs assistance formulating a strategy for implementing data sharing, or if you simply have some questions that need answering, get an expert for the best assistance.
Author Bio:
Vinod Kasipuri is a seasoned expert in data analytics, holding a master’s degree in the field. With a passion for sharing knowledge, he leverages his extensive expertise to craft enlightening articles. Vinod’s insightful writings empower readers to delve into the world of data analytics, demystifying complex concepts and offering valuable insights. Through his articles, he invites users to embark on a journey of discovery, equipping them with the skills and knowledge to excel in the realm of data analysis. Reach Vinod at LinkedIn.
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