Introduction:

Big data refers to extremely large datasets that can be analyzed to identify patterns, trends, and associations. The analysis of big data provides insights into various fields, including business, science, and government. However, the challenge with big data is not just analyzing it, but also storing, managing, and sharing it. This is where technologies like Snowflake come into play, as they offer a secure platform for storing and sharing large amounts of data.

 

Part 1: What is Data Sharing?

Let’s begin with data, data can derive from software that is used by enterprises within their business. For example, how many people are viewing a website, or what kind of people are most interested in a certain brand? On a lower level, data sharing is simply when data resources are shared with many users or applications and at the same time assuring that there is data fidelity to all of those participating.

Now how is this relevant today? Currently, data sources are continuous which in turn means that there have to be data volumes for all the data sources. The main focus, of data sharing, has become how to move these increasing volumes of data and how to ensure that the data is accurate and secure. The cloud comes into play as it is expanding what data sharing is capable of. Now that there is the modern cloud, data sharing can allow people to share live data within their business and outside of it, get rid of data silos, create access to specific data sets, and more. However, this would require a platform that can put data sharing into motion and ensure that it works to its potential and this is where Snowflake comes into the picture.

 

Snowflake and Data Sharing

Snowflake allows for data collaboration while at the same time lowering costs. It gives organizations the ability to securely share data and access live data. Not only is it secured and governed access to shared data but you can also publish data sets. As you can see the possibilities seem endless, but that’s only a brief preview of the capabilities of data sharing within Snowflake so let’s take a deeper look at the many parts that play a role in data sharing in Snowflake and how they come together in data sharing.

 

Part 2: What are Data Providers and Consumers?

A data provider is an account in Snowflake that creates shares that can be accessed by other accounts in Snowflake. When a database is shared, Snowflake supports it through grants that allow access control to objects within the database. There are no restrictions on the number of shares that can be created or accounts that can be added to a share.

A data consumer is an account that creates a database from a Share that is made accessible by another data provider. When you add a shared database to your account, you can access and query the objects within it. There are no limitations on how many Shares you can consume from data providers, but you can only create one database for each Share.

 

What is a Share?

In Snowflake, Shares are objects that contain all the necessary information for sharing a database. Shares include permissions that provide access to the databases and schema containing the object to be shared, as well as access to specific objects within the database. Additionally, consumer accounts are shared with the database and objects.

When a database is created from a Share, the objects shared within it become available to any users within the consumer account. These Shares can be customized, are secure, and are fully controlled by the provider account. This allows objects added to a Share to be accessed in real-time by consumers, and the provider account can also rescind access to a Share or any of its objects.

 

Part 3: How does Secure Data Sharing Function work in Snowflake?

When securely sharing data, the data is not copied or transferred between accounts, as one might assume. Rather, sharing is accomplished through Snowflake’s layer and metadata store. As a result, shared data does not occupy storage space within a consumer account, and therefore does not contribute to monthly data storage costs. However, charges will be incurred for the compute resources required to query the shared data.

Going back to what was previously mentioned, because the data itself is not copied or exchanged it makes secure data sharing an easy and fast setup for providers and it also makes shared data quickly available to consumers. But let’s take a closer look at how data sharing works for both the provider and the consumer:

 

Provider:

We will create a share of a database within your account. You can then grant access to objects within the database. This will enable you to share data from multiple databases, as long as those databases are under the same account. Finally, you can add one or more accounts to the share, including any accounts that you may have within Snowflake.

 

Consumer:

We will set up a read-only database from Share. You can customize access to the database by using the same access control that is provided for objects.

The structure of Snowflake allows providers to share data with many consumers, even those within their organization. Consumers can access shared data from many providers.

 

What Information is shared with Providers?

Snowflake providers have access to certain information about consumers who access their data. This includes the consumer's Snowflake account and organization names. Providers can also view statistical data about data consumption, such as the date of consumption and the number of queries generated by the consumer account on a provider's Share.

In addition, providers can see any information that a consumer provides at the time of data request submissions, such as the consumer's business email and company name.

 

Can I share with Third Parties?

Sharing data is only possible between Snowflake accounts. However, if you're a provider within Snowflake, you may want to share data with a consumer outside of Snowflake. Luckily, Snowflake has created reader accounts to facilitate this process.

Reader accounts enable data to be shared with consumers who are not Snowflake customers without the need for them to become one. These accounts are owned by the provider account that created them. While the provider account uses Shares to share databases with reader accounts, the reader account can only receive data from the provider account that created it.

Users with a reader account can query shared data, but they are unable to perform DML tasks that are available in a full account.

Having introduced data sharing and its workings within Snowflake, let's explore other features that come with Snowflake's data sharing.

 

Part 4: Products that use Secure Data Sharing in Snowflake

Snowflake offers additional products that enable data sharing between providers and consumers. These products include Direct Share, Snowflake Data Marketplace, and Data Exchange.

 

Direct Share:

Direct Share is a simple method of sharing data that enables account-to-account data sharing while utilizing Snowflake's Secure Data Sharing. As the provider (account on Snowflake), you can grant access to your data to other companies, allowing them to view your data within their Snowflake account without the need to move or copy any data.

 

Snowflake Data Marketplace:

All accounts in Snowflake can access the Snowflake Data Marketplace, provided they are in non-VPS regions on supported cloud platforms. The Data Marketplace uses Snowflake's Securing Data Sharing to facilitate connections between providers and consumers, similar to the Direct Share product.

You have the option to access third-party data and import the datasets into your Snowflake account without the need for transformation. This allows you to easily combine it with your existing data. The Data Marketplace provides a central location to obtain data from multiple sellers, simplifying the process of data sourcing.

Additionally, becoming a provider and publishing data within the Data Marketplace is a great way to monetize your data and reach a wider audience.

 

Data Exchange:

Data Exchange enables secure collaboration around data between invited groups, allowing providers to share data with consumers, as well as with your entire organization, including customers, partners, or even just within your unit. It also provides you with the ability to control who has access to your data, and who can publish, consume, or simply view it. Specifically, you can invite others and determine whether they are authorized to provide or consume data. Data Exchange is available for all Snowflake accounts hosted on non-VPS regions and supported cloud platforms.

These three products in Snowflake that use secure data sharing are useful for both provider and consumer accounts (and more) within Snowflake. Now that we have seen how data sharing works and what other features use data sharing in Snowflake, let's take a look at how to use the data that was shared with you or your data that is shared with others and more.

 

Working with Shared Data:

Once you have a grasp of the fundamentals of direct share, Snowflake Marketplace, and data exchange, there are additional concepts and tools available for you to explore.

Within Snowflake, those with an ACCOUNTADMIN role can utilize the Shared Data page on the new web interface to manage and create shares. As we delve further, please note that "inbound" refers to data that has been shared with you, while "outbound" refers to data shared from your account.

 

Data Shared with You:

Provider accounts can share inbound shares with your account using Direct Share, Data Exchange, or the Snowflake Marketplace. Inbound shares allow you to view data shared by providers, including who provided the share and how the data was shared. You can also create a database from a share.

To access your inbound shares, go to the "Share With Me" tab within the Snowflake web interface. Here you will find:

• Direct shares that are shared with you. These shares are placed into two groups: 1. Direct shares that are ready to be used and 2. Direct shares that have been imported into a database can be queried.
• Listings for data exchange that you have access to. The data is shown under the name of the initial data exchange. If you have more than one data exchange, each data exchange will be shown within separate sections.
• Listings for the Snowflake Marketplace data that have been moved into a database and can be queried. However, it does not show shares that are ready to be used. You can find the data listing in the Marketplace menu.

 

Data You Shared:

Your account allows you to share data with consumers through outbound shares. You can share data directly, through data exchange, or via the Snowflake Marketplace (as previously mentioned for inbound shares).

With outbound shares, you can:

• View the shares you have created or have access to, including information such as the database for the share, consumer accounts that can access the share, the date when the share was created, and the objects that are being shared.
• Create and edit both a share and its data listing.
• Remove access to the share for individual consumer accounts.

Returning to the web interface, the "Shared by My Account" tab displays outbound shares from Snowflake Marketplace, data exchange, and direct shares.

When considering shares, icons are located beside each share to indicate the sharing mechanisms like direct sharing, data exchange, or Snowflake Marketplace.

Lastly, there are filters available when viewing your shared data:

• Type: This is presented as the "Ally Types" drop-down and allows you to differentiate direct shares from listings.
• Consumer: This is presented as the "Shared With" drop-down and allows you to select a specific consumer or data exchange (where the data has been shared).

 

Data that is Shared

When sharing data, there are many ways you can do this:

1. Use direct share to directly share data with consumers
2. In the Snowflake Marketplace, post a listing
3. In data exchange, post a listing

Furthermore, when you are in the web interface and you want to share data, you will use the “Share Data” drop-down and choose from the list that provides all the platforms where you can share data.

 

Requesting Data

In the web interface, you can view inbound and outbound requests in the "Requests" tab. However, this tab does not display data requests from the Snowflake Data Marketplace.

Let's take a moment to review what inbound and outbound requests mean.

Inbound requests are made by consumers who are seeking access to your data. You can organize these requests by status and review them accordingly. Outbound requests, on the other hand, are requests made by you to obtain data listings from other providers. Just like inbound requests, you can sort them by status. Keep in mind that the requests you make may be rejected, but you can always resubmit them.

 

Managing Exchanges

In certain roles, such as the Data Exchange Admin role or if you have Provider Profile Level Privileges, you can create and organize provider profiles within the “Manage Exchanges” tab. However, if your organization does not have a data exchange, the “Manage Exchanges” tab will not be visible.

Regarding the provider profile, with this role, you can perform the following tasks within a data exchange:

• Create, update, and delete a profile
• Update contact email
• Manage profile editors

Now that we have reviewed data sharing, you should be able to understand all its components and the different functions it offers!

To keep up with new features, regularly visit our website for more information and tips.

 

Conclusion:

This article provides a deep dive into data sharing and how it works within the Snowflake ecosystem. It covers the basics of data sharing, the role of data providers and consumers, and how to secure data-sharing functions. Additionally, it explores Snowflake's products that use secure data sharing, such as Direct Share, Snowflake Data Marketplace, and Data Exchange. The article also explains how to work with shared data, including managing inbound and outbound requests and managing exchanges.

Introduction:

 

In today’s article, we’ll explore together Snowflake's Search Optimization Service, a feature that can improve the performance of point lookup for certain queries by creating search access paths. The service is available on the Enterprise Edition or higher for Snowflake and is best for business users who rely on quick access to data for critical business decisions.

The article also covers how to turn on the service, its benefits, and its cost. We also introduce Snoptimizer™, our service that scans for all the Snowflake anti-patterns and optimizes your account to help you run cost-effectively.

 

History of Snowflake & Search Optimization:

 

Snowflake initially made a name for itself as the easiest data warehouse to use back in 2014. Since then it has transformed itself and its core technology into a full Snowflake Data Cloud. While the Snowflake Data Cloud Account at first comes with many amazing features by default, there are many areas where you can optimize Snowflake for your specific needs and use cases. As Snowflake has grown over the years, it has added a ton of functionality including paid services such as SnowPipe, Materialized Views, Auto Clustering, Search Optimization Service, and others.

Alternatively, it can be useful for data scientists who want to continuously explore specific subsets of data. Essentially it is a maintenance service that runs in the background of Snowflake and creates search access paths. These paths make it easier to load and populate data quickly, as well as update stale data.

 

Turning on the Feature:

 

To turn on such a feature, you must first ensure you are using an account that has access to add it to a table. Having access means you have the following privileges: ownership & add search optimization. Once that requirement is met, it’s as simple as typing the following into your console:

 

ALTER TABLE [IF EXISTS] <table_name> ADD SEARCH OPTIMIZATION;

To ensure it is turned on, show your tables and check to see that ‘Search Optimization’ says ON. A few notes to add is that you will see an increase in credit consumption while the service runs and starts to build the search access paths. You can get an estimate of the cost for specific tables before committing by running the following command:

 

SYSTEM$ESTIMATE_SEARCH_OPTIMIZATION_COSTS(‘<table_name>’)

Being strategic with the tables you introduce to the search optimization service will help greatly with reducing those costs. The service fits best for tables that aren’t queried by columns and tables that aren’t clustered.

If you add the service and decide to move it later on, you can easily do so with the correct privileges by running the following command:

 

ALTER TABLE [IF EXISTS] <table_name> DROP SEARCH OPTIMIZATION;

This is just one solution to make your life easier and queries faster, however, there are many more out there that are more cost-friendly and do not require you to look thoroughly through your tables. One of the prime examples is Snoptimizer™, our service that scans for all the Snowflake anti-patterns and optimizes your account to help you run cost-effectively. It checks your resource monitors, auto-suspend settings, cloud service consumption, and warehouse computing among other things to fix your account and ensure you are fully optimized.

 

Conclusion:

 

Snowflake's Search Optimization Service is a powerful feature that can significantly improve the speed and efficiency of certain queries. While it comes with a cost, it can be a valuable investment for business users who rely on quick access to data for critical decision-making. However, it's important to be strategic about which tables you introduce to the service to minimize costs. Additionally, there are alternative solutions available, such as Snoptimizer™, that can optimize your account and help you run cost-effectively. With the right approach, Snowflake's Search Optimization Service can be a powerful tool in your data optimization arsenal.

Introduction:

 

This Snowflake's Summit was a roller coaster of emotions, but more often than not, we were thrilled with all the new announcements. With over 61+ sessions, we got to see some of Snowflake’s amazing new features, tons of use cases, and first-hand looks at how to use their new tools with step-by-step labs. Most of us are too busy to watch two days’ worth of webinars, but that’s where we come in – providing you with your weekly dose of Snowflake Solutions! We decided to help out by highlighting the most important announcements, as well as the sessions we thought were worth the watch!

This time around Snowflake announced that they have five main areas of innovation: data programmability, global data governance, platform optimization, connected industries, and powered by Snowflake. While magical upgrades and new tools mean more flexibility for users, the reality is that most of these new features are still in private preview, so we, the public, won’t see them in action for some time. Regardless, we’ll still go through the top areas of innovation:

 

Platform optimization:

One of the most significant improvements this year is the enhanced storage economics, resulting in reduced storage costs due to improved data compression. As a result, many will begin to see savings on storage for new data. Additionally, Snowflake has developed new usage dashboards, enabling users to better monitor and comprehend their usage and costs across the platform. While it appears that Snowflake is making progress in the direction of cost optimization, the subject has been challenging so far, and there are not enough safeguards in place to prevent warehouse sizes (and bills) from skyrocketing. If you're interested in discovering the various ways your company can inadvertently lose money on Snowflake, as well as strategies for avoiding them, we invite you to register for our upcoming Cost Optimization webinar.

 

Global Data Governance:

Moving forward, we will discuss the six new data governance capabilities that have been added to the Snowflake platform. We will focus on the three that are most exciting.

 

1. Classification:

Automatically detects personally identifiable information.

• Why is this cool? We can apply specific security controls to protect their data!

 

2. Row access policies:

Dynamically restrict the rows of data in the query based on the username, role, or other custom attributes.

• Why is this cool? We no longer need multiple secure views and can eliminate the need for maintaining data silos. That’s a win in our book.

 

3. Access History:

A new view that shows used and unused tables to produce reports.

• Why is this cool? You can see what’s bringing value and optimize storage costs based on what is frequently accessed or completely abandoned data. Who doesn’t love to save money?

 

Connected Industries:

Following we have two upcoming features that we thought were worth mentioning since they will be game-changers! These two features are Discover & Transact and Try Before You Buy, both of which will ease collaboration and data procurement between connected industries.

 

1. Discover and Transact:

Directly within the Snowflake Data Marketplace, a consumer can now discover data and purchase with a usage-based pricing model.

• This is truly cool because of the self-service aspect! By providing this feature, we can significantly reduce the cost of selling and delivering data to our valuable clients.

 

2. Try Before You Buy:

Now consumers can access sample data to make sure they’re getting all they need before signing that check.

• Why is this interesting? Everyone loves a free sample!

 

Data Programmability:

 

Probably the most important updates are under the data programmability umbrella. So, if you’re still with me, hang on a little longer, this is about to get interesting!

Some innovations are ready to be used now in public preview, so let’s check them out:

1. SQL API: This new API enables customers to automate administrative tasks without having to manage infrastructure, there’s no need to maintain an external API management hub!
2. Schema Detection: Now supports Parquet, Orc, Arvo, and hopefully more file formats in the future.

 

Exciting things to look forward to soon:

1. Serverless Tasks: Snowflake will determine and schedule the right amount of computer resources needed for your tasks.
2. Snowpark and Java UDFs: Snowpark is going to be the Snowflake developer’s new playground. It allows developers to bring their preferred languages directly into the platform. Java UDFS will also enable data engineers and developers to bring their custom code to Snowflake. This enables better performance on both sides!
3. Unstructured Data Support: Soon, we will be able to treat unstructured data the same as structured data, with the ability to store, govern, process, and share.
4. Machine Learning with Amazon SageMaker: A tool that will automatically build and insert the best machine-learning models into Snowflake!

 

Conclusion:

 

In summary, Snowflake's 2022 Summit exhibited several noteworthy novel features and updates, particularly in the domains of platform optimization, global data governance, and data programmability. Although a significant number of these features are still in private preview, they provide a glimpse into Snowflake's future direction and potential.

Keep an eye out for more updates and guidance from IT Strategists on how to leverage Snowflake's tools and solutions to their fullest potential. Be sure to check out our blog for more news and information.

Introduction:

 

The Snowflake Data Marketplace is a cloud data exchange platform where organizations can discover, access, and share live, ready-to-query data sets in a secure, governed, and compliant manner.

Long gone are the days when consumers have to copy data, use APIs, or wait days, weeks, and sometimes even months to gain access to datasets. With Snowflake Data Marketplace, analysts around the world are getting the information they need to make important decisions for their businesses in a blink of an eye and the palm of their hands.

 

 

What is it and how does it work?

 

The Snowflake Data Marketplace provides access to live, query-ready data sets. It uses Snowflake's Secure Data Sharing to connect data providers and consumers. Currently, it offers access to 229 data sets. As a consumer, you can discover and access various third-party data sets. They will be directly available in your Snowflake account to query. There's no need to transform the data or spend time joining it with your own data. If you use multiple vendors for data, the Data Marketplace gives you a single source to access everything.

 

Why is this Marketplace important?

 

Companies can now securely and instantly share and access live, regulated data in real-time without copying or moving it. In the past, getting access to such data could take days, weeks, months, or even years. With the Data Marketplace, gaining access only takes a couple of minutes. Already over 2000 businesses have requested free access to key data sets in our marketplace. This is a gold mine for anyone who wants data-driven, on-demand access to live and ready-to-query data, and the best part is that it's available globally, across clouds.

There are significant benefits for both providers and consumers. However, three key points enable companies to unlock their true potential using the Data Marketplace:

 

Source Data Faster and More Easily

• As we said previously, using Snowflake Data Marketplace as a consumer allows users to avoid the risk and hassle of having to copy and migrate stale data. Instead, securely access live and governed shared data sets, and receive automatic updates in real-time.

Monetize Your Data

• As a data provider on Snowflake's Data Marketplace, you can create new revenue streams by offering your governed data assets to thousands of potential Snowflake data consumers.

 

Reduce Analytics Costs

• Using this service eliminates the costs and effort of traditional data integration. Both consumers and providers can access shared data directly, securely, and compliantly from your Snowflake account. This streamlines data ingestion, pipelines, and transformation.

 

Learn more about Snowflake Data Marketplace

 

For more information on Snowflake’s Data Marketplace, visit the official website here.

If you're curious to see Snowflake's official Marketplace demo, check it out here.

 

Action Items after reading this article:

 

• Visit the Snowflake Data Marketplace official website.
• Check out Snowflake's official Marketplace demo.
• Learn more about accessing live, query-ready data sets through Snowflake's Data Marketplace.
• Find, try out, and purchase the data and applications you require to power innovative business solutions.
• Discover how data providers can create new revenue streams by offering governed data assets.
• Understand how the Data Marketplace reduces analytics costs by streamlining data ingestion, pipelines, and transformation.

What’s the benefit of learning to create a database on Snowflake?

 

It is important to learn to create databases with Snowflake because:

• Snowflake is a popular data warehouse built for the cloud. Having experience with Snowflake will be valuable for data engineers and data scientists.
• Snowflake has many advantages over traditional data warehouses like fast performance, scalability, and low cost. Being able to build databases on Snowflake will allow you to leverage these benefits.
• Snowflake uses SQL, a common query language, so the skills you learn will be transferable to other systems.
• Snowflake has many features for modeling and managing data that you can utilize by knowing how to create databases and schemas.
• There are many resources and a large community to help you learn Snowflake.

 

Creating Databases with the User Interface:

 

To complete this tutorial, you will need a Snowflake account and the URL to log in to the web interface.

Log in to your Snowflake environment and select the Databases tab at the top left of the screen. It should look similar to the image below, except you won't have the same databases. Snowflake provides sample data.

 

 

Let’s create a new database. Click the Create button and fill out the information in the pop-up window.

 

 

When naming the database, there are restrictions -- no spaces and the name cannot start with a number. You can read the full set of restrictions in Snowflake’s documentation.

Select Finish and you’ll see your new database appear in the table. Click on your database and you’ll see any tables, views, or schemas that exist. Since the database has just been created, none of these objects exists yet.

 

Creating Schemas:

 

A snowflake schema is a logical grouping of database objects (tables, views, etc.). Each schema belongs to a single database and can have its security configuration.

From inside our selected database, select the Schemas tab. Snowflake will create a public and information schema by default. The PUBLIC schema can be used to create any other objects. The INFORMATION_SCHEMA contains all the metadata for the database.

 

 

Click the Create button and provide and name and comment. The Managed Access option determines if the security of the objects within the schema is managed by the schema owner or the owner of the object.

 

Creating Databases and Schemas with Code:

 

All operations done with the UI can also be done with SQL code on the tab of the worksheet. To see what code corresponds to the operations we are doing, click on the Show SQL button.

 

 

Using code streamlines our Snowflake work once we understand how it functions. This can reduce time and automate tasks.

To run any SQL code in Snowflake, choose Worksheets from the main menu. This lists all databases on the left for reference and provides a space to enter your code. On the right, we see our current role, database, warehouse, and schema.

Let’s enter the code to replicate the UI process from before. When you click Run, only the line where your cursor is executes. To run multiple lines, highlight them and click Run. If you’ve already created your database, run DROP DATABASE DEMO_DB for this to work.

 

 

And with those steps, you should be able to create a database. If you want to learn new techniques, keep checking our blog regularly for more helpful tips.

 

Conclusion:

 

To conclude, we have walked through the steps to create your first database and schema in Snowflake using both the user interface and SQL code. You now have the foundation to build tables, load data, and query your Snowflake data warehouse.

Introduction:

 

In today’s article we’ll go over Snowflake's support for semi-structured data in the form of JSON, Avro, ORC, Parquet, and XML. It covers the process of loading JSON data into a Snowflake table, including creating a database object, schema, table, file format, and stage. It also provides an example of querying semi-structured data using Snowflake's SQL SELECT statements.

 

What is Snowflake’s semi-structured data?

 

One of Snowflake's unique feature is its native support for semi-structured data. Snowflake supports semi-structured data in the form of JSON, Avro, ORC, Parquet, and XML. Semi-structured data is data that does not conform to a specific schema, such as JSON data. Snowflake can load JSON data directly into table columns with type VARIANT, a universal type that can be used to store values of any type. Data can be queried using SQL SELECT statements that reference JSON elements by their paths.

One of Snowflake’s unique features is its native support for semi-structured data. Snowflake supports semi-structured data in the form of JSON, Avro, ORC, Parquet, and XML. JSON is the most widely used and industry standard due to its data format and ease of use.

 

 

JSON data can be loaded directly into the table columns with type VARIANT, a universal type that can be used to store values of any type. Data can be queried using SQL SELECT statements that reference JSON elements by their paths.

Let’s take a look at our JSON data. Here our some JSON data properties:

• Data in JSON is a name-value pair.
• Data is separated by a comma.
• Curly braces hold objects.
• Square brackets hold an array.

{
"ID": 1,
"color": "black",
"category": "hue",
"type": "primary",
"code": {
"rgb": "255,255,255",
"hex": "#000"
}
},{
"ID": 2,
"color": "white",
"category": "value",
"code": {
"rgb": "0,0,0",
"hex": "#FFF"
}
},{
"ID": 3,
"color": "red",
"category": "hue",
"type": "primary",
"code": {
"rgb": "255,0,0",
"hex": "#FF0"
}
}

Database Object

We have created a new database object to load and process semi-structured data as shown below. You can use the existing one if you have already created it earlier.

CREATE DATABASE IF NOT EXISTS TEST_DATABASE;

 

Schema

Create a new schema under TEST_DATABASE object to have ease of access. This step is optional if you already have access to the existing schema. In such a case you can use the existing schema.

CREATE DATABASE IF NOT EXISTS TEST_DATABASE;

Table

In order to create JSON data, we need an object to hold the data and it should be capable enough to hold the semi-structured data.

In snowflake, to process the semi-structured data, we have the following data types:

• Variant
• Array
• Object

We’ll be using the variant object to load data into a Snowflake table.

CREATE TABLE IF NOT EXISTS COLORS
(
TEST_DATA VARIANT
);

Object CHRISTMAS_REC is created with one column TEST_DATA that holds the object of JSON data.

 

File Format

To load the JSON object into a Snowflake table, file format is one of the mandatory objects in snowflake:

CREATE FILE FORMAT JSON_FILE_FORMAT
TYPE = 'JSON'
COMPRESSION = 'AUTO'
ENABLE_OCTAL = FALSE
ALLOW_DUPLICATE = FALSE
STRIP_OUTER_ARRAY = TRUE
STRIP_NULL_VALUES = FALSE   IGNORE_UTF8_ERRORS = FALSE;

The above file format is specific to JSON. The STRIP_OUTER_ARRAY array option removes the outer set of square brackets [ ] when loading the data, separating the initial array into multiple lines. If we did not strip the outer array, our entire dataset would be loaded into a single row in the destination table.

 

Stage

In order to copy the data to a Snowflake table, we need data files in the cloud environment. Snowflake provides two types of stages:

• Snowflake Internal stage.
• External stages (AWS, Azure, GCP).

If you do not have any cloud platform, Snowflake provides space to store data into its cloud environment called – “Snowflake Internal stage”.

In this article, we have used a Snowflake internal stage and created a dedicated stage for semi-structured load.

CREATE STAGE IF NOT EXISTS JSON_STAGE FILE_FORMAT = JSON_FILE_FORMAT;

You can use below command to list files in stages:

LIST @JSON_STAGE;

PUT & COPY Command

 

PUT command fetches data from local storage to snowflake internal stages. You can run this command from the Snowflake CLI client. I’ll be using the Snowflake UI to do it under the database tab.

PUT file://<file_path>/sample.json @COLORS/ui1591821970011   COPY INTO "TEST_DATABASE"."TEST_SCHEMA"."COLORS" FROM @/ui1591821970011 FILE_FORMAT = '"TEST_DATABASE"."TEST_SCHEMA"."JSON_FILE_FORMAT"' ON_ERROR = 'ABORT_STATEMENT';

You can accomplish the same thing by using Snowflake UI under the database tab. Click on your database and then find your way to the table. Click on load data above it.

 

 

Check that the data was properly loaded (SELECT from COLORS).

 

Querying Semi-Structured Data

Snowflake is extremely powerful when it comes to querying semi-structured data. The command works a lot like JavaScript, except we use : notation to retrieve the category for each row. By using :: notation, we define the end data type of the values being retrieved.

SELECT
test_data:ID::INTEGER as ID,
test_data:color::STRING as color,
test_data:category::STRING as category,
test_data:type::STRING as type,
test_data:code.rgb::STRING as code_rgb,
test_data:code.hex::STRING as code_hex
FROM
colors;

 

Conclusion:

 

The process of loading data into a database can be a cumbersome task but with Snowflake, this can be done easily. Snowflake functionality makes it possible to process semi-structured data. Check out the docs to learn more about semi-structured data.