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.

Part 1 - Introduction

 

Today’s article provides an introduction to stored procedures in Snowflake, which can be used to create modular code with complex business logic by combining SQL statements with procedural logic. The article discusses the benefits of stored procedures, including error handling and dynamically creating SQL statements, and provides examples of how to create and use stored procedures in Snowflake.

 

Part 2 - What are Snowflake Stored Procedures?

 

Snowflake stored procedures can be thought of as a function that enables users to create modular code with complex business logic by combining multiple SQL statements with procedural logic. They are used for data migration and validation while handling exceptions. Benefits of stored procedures include procedural logic such as branching and looping, error handling, dynamically creating SQL statements to execute, and executing code with the privileges of the stored procedure creator. A Stored Procedure is created with a CREATE PROCEDURE command and is executed with a CALL command. A Stored Procedure uses JavaScript for logic and control and SQL is used to call the JavaScript API.

 

Part 3 - Benefits of Stored Procedures include:

 

• Procedural logic such as branching and looping which straight SQL does not support.
• Error handling.
• Dynamically creating SQL statements to execute.
• Executing code with the privileges of the stored procedures creator. This allows stored procedure owners to delegate power to perform operations to users who otherwise could not.

A Stored Procedure is created with a CREATE PROCEDURE command and is executed with a CALL command. The result is returned as a single value. A Stored Procedure uses JavaScript for logic and control and SQL is used to call the JavaScript API.

 

Part 4 - Stored Procedure Examples:

 

Let’s say we want to insert a row using a stored procedure.

First, let’s create a database and table to use.

USE DATABASE DEMO_DB;
CREATE OR REPLACE TABLE Employee(emp_id INT, emp_name varchar,emp_address varchar);
create sequence if not exists emp_id;

Now that we have that setup, let’s create our first stored procedure.

CREATE OR REPLACE PROCEDURE employee_insert(name varchar, address varchar)
RETURNS VARCHAR
LANGUAGE JAVASCRIPT
AS
$$
var command = "INSERT INTO Employee (emp_id, emp_name, emp_address) VALUES (emp_id.nextval, '"+NAME+"','"+ADDRESS+"')";
var cmd_dict = {sqlText: command};
var stmt = snowflake.createStatement(cmd_dict);
var rs = stmt.execute();
return 'success';
$$;

In the first section, we define the SPs name, parameters, return value, and language. Then between the $$, we write the actual code. Now that everything is setup, we can use the SP with the CALL command like this:

CALL employee_insert('Name','Location');

Let’s give this a try and see what happens!

 

Awesome! That worked!

Make sure to use a database that exists in your console if you’re replicating this.

However, a simple insert isn’t particularly useful for a Stored Procedure. We’d be better off just using pure SQL. Let’s look at a more realistic example that better demonstrates the power of Stored Procedures.

 

Taking it Further

 

Let’s imagine the following scenario. You receive an employee’s data in JSON. You want to store it on the table but you have to ensure the following first:

• Employee name cannot be empty or NULL
• Employee name must be unique
• Employee address cannot be NULL

It is possible to do all of this using a single stored procedure. However, since we have two distinct tasks — validation and insert — it’s better practice to break them up into 2 separate procedures. We’ll then call the validation procedure from inside the insert procedure. We’ll be utilizing Snowflake’s variant type to return a json object. Let’s take a look at our validate procedure.

 

Validate Procedure

 

create or replace procedure employee_validate_json(INPUT VARCHAR)
RETURNS VARIANT NOT NULL
LANGUAGE JAVASCRIPT
AS
$$
var json_row = {};
var error_count = 0;
try {
    var employee_data = JSON.parse(INPUT);
    var employee_id = employee_data.employee_id;
    var employee_name = employee_data.employee_name;
    var employee_address = employee_data.employee_address;
    if (!employee_name) {
        json_row["employee_name"] = "employee name cannot be empty or null.";
        error_count = 1;
    } else {
        var command = "select count(*) from Employee where emp_name='"+ employee_name + "'";
        var stmt = snowflake.createStatement({sqlText: command});
        var res = stmt.execute();
        res.next();
        row_count = res.getColumnValue(1);
// check for duplicate
    if (row_count > 0) {
        json_row["employee_name"] = "employee name already exists in table.";
        error_count = 1;
    }
}
if (employee_address == null || employee_address == undefined) {
    json_row["employee_address"] = "employee address should not be NULL.";
    error_count = 1;
}
json_row["is_error"] = error_count;
} catch (err) {
    json_row["Exception"] = "Exception: " + err;
    json_row["is_error"] = 1;
}
return json_row;
$$;

The stored procedure will either return is_error = 0 if there is nothing wrong with our JSON or return is_error = 1 with the appropriate error message.

Successful run:

call EMPLOYEE_VALIDATE_JSON('{
"employee_name": "Lucas", "employee_address": "Los Angeles"
}');

Result:
{ "is_error": 0 }

Error run:

call EMPLOYEE_VALIDATE_JSON('{
"employee_name": "", "employee_address": "Los Angeles"
}');

Result:
{"employee_name": "employee name cannot be empty or null.", "is_error": 1 }

Now that we have our validation working. Let’s dive into our insert procedure!

 

Insert Procedure

 

Our insert procedure is going to call our validate procedure and check for any errors. If it finds any, it will return them. If not, it will attempt to insert the data into the table. Also returning a json upon completion.

create or replace procedure employee_insert_json(INPUT VARCHAR)
RETURNS VARIANT NOT NULL
LANGUAGE JAVASCRIPT
AS
$$
var json_row = {};
var message = {};
var detail = {};
var result = '';
var error_count = 0;
try {
    var employee_data = JSON.parse(INPUT);
    var employee_name = employee_data.employee_name;
    var employee_address = employee_data.employee_address;
    var command_validate = "call employee_validate_json('" + INPUT + "')";
    var cmd_dict_validate = {sqlText: command_validate};
    var stmt_validate = snowflake.createStatement(cmd_dict_validate);
    var rs_validate = stmt_validate.execute();
    rs_validate.next();
    var validate_result = rs_validate.getColumnValue(1);
if (validate_result.is_error > 0) {
    return validate_result;
} else {
    var command = "INSERT INTO employee(emp_id,emp_name,emp_address) VALUES(emp_id.nextval, '" + employee_name + "','" + employee_address + "')";
    var cmd_dict = {sqlText: command};
    var stmt = snowflake.createStatement(cmd_dict);
    var rs = stmt.execute();
    json_row["message"] = "successfully inserted employee";
}
} catch (err) {
    json_row["exception"] = err;
    error_count = 1;
}
json_row["is_error"] = error_count;
return json_row;
$$;

Let’s take a look at some sample runs.

call EMPLOYEE_INSERT_JSON('{
"employee_name": "Lucas", "employee_address": "Los Angeles"
}');

Result:
{ "is_error": 0, "message": "successfully inserted employee" }

Nice! That worked but let’s see what happens if we try to run the same command again.

call EMPLOYEE_INSERT_JSON('{
"employee_name": "Lucas", "employee_address": "Los Angeles"
}');

Result:
{"employee_name": "employee name already exists in table.", "is_error": 1 }

When we try it again our validate procedure finds that the employee name already exists!

 

Part 5 - Try Snoptimizer today!

 

 

Snoptimizer quickly and automatically optimizes your Snowflake account for security, cost, and performance. It eliminates headaches and concerns about security risks and cost overruns across your Snowflake account.

Try Snoptimizer today. Sign up and schedule a personal demo with us!

 

Part 6 - Conclusion:

 

Stored procedures are a great way to streamline your Snowflake tasks. They can also be used to grant higher-level access to lower-level users in a defined manner. I hope this tutorial has helped you create or transfer your stored procedures to Snowflake.

If you're interested in trying out Snoptimizer, feel free to schedule a personal demo with us today.

Overview:

 

This overview describes how to get started using Snowflake. It covers logging into Snowflake through the web interface, command line interface, or other methods. It then provides an overview of the main tabs in the web interface: databases, shares, warehouses, worksheets, and history. The databases tab allows you to manage databases. The warehouse tab allows you to configure computational resources. The worksheet tab allows you to write and run SQL queries. The history tab shows the details of past queries. The overview also describes additional features like the help menu and user preferences.

Snowflake separates its computation engine from storage. This allows it to have the additional advantage of adaptive optimization. This means that Snowflake can automatically scale your cloud usage up or down based on your current needs. In other words, Snowflake saves a little (uses fewer resources) on every data operation, saving you long-term money.

If you’d like to check out Snowflake’s capabilities yourself, you can sign up for a complimentary account with $400 worth of credit here.

 

Setup Requirements:

 

• Snowflake Account
  • Sign up for free at https://snowflakesolutions.net/ or contact Snowflake directly.

  You can connect to Snowflake through:

  • A browser (easiest to start) • The command-line interface (CLI) • Programming libraries for your backend stack

  I recommend beginning with the browser. Then transition to the CLI or a library, depending on your backend.

• Browser-based web interface
  • Minimum Version
  • Chrome: 47
  • Safari: 9
  • Firefox: 45
  • Opera: 36
  • Edge: 12
• SnowSQL, Snowflake CLI
  • Redhat-compatible Linux operating systems
  • macOS (64-bit)
  • Windows (64-bit)
• Client using JDBC or ODBC
  • Linux
  • MacOS
  • Windows
    • 64-bit for JDBC Driver
    • 32-bit or 64-bit for ODBC driver
• Any 3rd party partner

 

Logging into Snowflake:

 

Account Name

All access to Snowflake is through your account name. You’ll need it to sign in and it’s part of the URL for browser access.

https://account_name.snowflakecomputing.com

The full account name may include the region and the cloud platform hosted by your account. Ex. account-name.us-east-2.aws

 

Logging into the Web Interface:

 

Go to the hostname provided by Snowflake for your account. The format should be:

https://account_name.snowflakecomputing.com

You should see the following screen.

 

 

Enter your credentials and Login.

 

Logging in Using SnowSQL

 

SnowSQL is the command line client for connecting to Snowflake and executing SQL queries and DDL and DML operations. To connect follow this quick guide.

 

Logging in Using Other Methods

 

In addition to the web interface and SnowSQL, Snowflake supports other methods for connecting.

• 3rd-party clients services that support JDBC or ODBC
• Developer applications that connect through drivers for Python, Node.js, Spark, etc.

These methods require additional installation and configuration. Check out this for details.

 

Web Interface

 

Snowflake web interface allows you to create/manage virtual warehouses, databases, and data objects. Use this interface to load data into tables, execute ad hoc queries, perform DML/DDL operations, and view past queries. You can also manage administrative tasks such as changing passwords and managing users. Check out Managing Your Snowflake Account for more information.

On the top of the UI, you’ll see the following tabs.

 

Databases

 

Databases show the databases you have access to. You can create, clone, drop, or transfer ownership of databases.

Tasks you perform on this page include:

• Create, clone, or drop the database
• Transfer ownership of the database

Click the name of a database to view and perform tasks on it:

 

Shares

 

Shares show data shared with your organization that you can use and data you are sharing with others.

This is a new page added to Snowflake. It allows you to consume shared data with your organization and provide data to others. Don’t worry about it for now.

 

Warehouses

 

Warehouses show the computational resources you can spin up to perform analytics. You can create, drop, suspend, resume, configure, or transfer warehouses.

Warehouses are Snowflake’s computational engines. You will define them by size (computational power) and spin them up to perform data analytics.

This page shows information about the virtual warehouses to create or can access. Tasks you can perform on this page include:

• Create or drop a warehouse
• Suspend or resume a warehouse
• Configure a warehouse
• Transfer ownership of a warehouse

 

Worksheet

 

 

Worksheets are where you can write and run SQL queries and DDL/DML operations. You can run queries, load SQL scripts, open multiple worksheets, save/reopen worksheets, resize warehouses, and export query results.

 

Tasks you can perform include:

• Run ad hoc queries and other DDL/DML operations in a worksheet, or load SQL script files.
• Open concurrent worksheets, each with its separate session.
• Save and reopen worksheets.
• Log out of Snowflake or switch roles within a worksheet, as well as refresh your browser, without losing your work:
  • If you log out of Snowflake, any active queries stop running.
  • If you’re in the middle of running queries when you refresh, they will resume running when the refresh is completed.
• Resize the current warehouse to increase or decrease the compute resources utilized for executing your queries and DML statements.
• Export the result for a selected statement (if the result is still available).

For more information, check out Using Worksheets for Queries.

 

History

 

History shows the details of queries run in the last 14 days. You can filter, scroll through, abort ongoing queries, view query details/results (for 24 hours), and change displayed columns.

The page displays a historical listing of queries, including queries executed from SnowSQL or other SQL clients.

Tasks you can perform include:

• Filter queries displayed on the page.
• Scroll through the list of displayed queries. The list includes (up to) 100 queries. At the bottom of the list, if more queries are available, you can continue searching.
• Abort a query that has not been completed yet.
• View the details for a query, including the result of the query. Query results are available for 24 hours. This limit is not adjustable.
• Change the displayed columns, such as status, SQL text, ID, warehouse, and start and end time, by clicking any of the column headers.

 

Help Menu and User Preferences

 

On the top right, there is a drop-down menu from the help button. It supports the following actions:

• View the Snowflake Documentation
• Visit the Support Portal
• Download the Snowflake clients by opening a dialog box where you can:
  • Download the Snowflake CLI client (SnowSQL) and ODBC driver.
  • View download info for the Snowflake JDBC driver, Python components, Node.js driver, and Snowflake Connector for Spark.
• Show the help panel with context-sensitive help for the current page.

To the right of the help button is the user preferences. You can then change your password or security role for the session (if you have multiple roles assigned to you). For more information about security roles and how they influence the objects, you can see them in the interface, and for the tasks, you can perform, see Access Control in Snowflake.

You can also use this dropdown to:

• Set your email address for notifications (if you are an account administrator).
• Close your current session and exit the Snowflake web interface.

 

Conclusion:

 

This article summarized how to use Snowflake's web interface to manage a cloud data warehouse. The interface has tabs for databases, data shares, virtual warehouses, SQL worksheets, and query history.

The "Databases" tab lists databases and allows creating, deleting, or cloning them. The "Shares" tab shows data shares controlling database and table access. The "Warehouses" tab manages warehouses required to run queries. Users can start, stop, resize, or unload warehouses. The "Worksheets" tab provides interfaces to run SQL queries and view results. Users can run ad-hoc queries or save common queries. The "Query History" tab lists query details like the user, warehouse, and time. This helps monitor usage, troubleshoot issues, and ensure compliance.

In summary, Snowflake's web interface offers a centralized portal to manage a cloud data warehouse and run SQL queries for business insights.

Introduction - Snowsql CLI Client

 

SnowSQL is a command-line client for Snowflake, a cloud-based data warehousing and analytics platform. It allows you to execute SQL queries and perform all DDL and DML operations. It can be downloaded from the Snowflake Repository and has all the same capabilities as the Snowflake UI. It provides an easy way to access Snowflake right from your command line.

 

In this article we'll discuss how to install and configure SnowSQL.

 

Steps to Activate SnowSQL CLI Client:

 

Step 1 – Download and Install SnowSQL CLI

 

1. Login into Snowflake and click on Help in the top right corner

2. Click on Downloads -> Snowflake Repository

3. Select Cli Client (snowsql) and click Snowflake Repository in the Downloads Dialog box.

 

This will lead you to a web index page.

4. Select the operating system where you want to install SnowSQL and click download.

5. Click on bootstrap -> 1.2 (or the newest version) -> Pick your OS (Darwin is Mac) -> Download the latest version

6. Run the installer.

 

Step 2 – Running SnowSQL CLI

 

2.1. Check SnowSQL is installed properly

Run: snowsql -v

Output: Version: 1.2.5 (or latest)

Good! Now that snowsql has been installed, let’s set up our environment to work.

 

2.3 Login to your account

snowsql -a account_name -u username

Account name can be found in the first part of your URL when logged into Snowflake (everything before snowflakecoputing.com, for instance, sample_username.sample_region.azure)

 

Setup the database context

 

// create a warehouse

CREATE WAREHOUSE yourname_WH AUTO_SUSPEND = 60 AUTO_RESUME=TRUE;

USE WAREHOUSE yourname_WH;

// select your desired database

USE DATABASE SNOWFLAKE_SAMPLE_DATA;

// select the data schema

USE SCHEMA TPCDS_SF100TCL;

 

 

(Note: Lukes-MacBook-Pro and lmunro are specific to my console. Yours will be different unless you somehow stole my laptop in which case please give it back.)

Awesome! Now we’re ready to perform whatever data analytics you desire.

However, it can be quite tedious to type in your account, username, password, warehouse, DB, and schema every time you log in. You can edit the snowSQL config file to perform these automatically.

 

Step 3 – Edit Config File

 

3.1 Locate the hidden snowsql folder

• • Linux/Mac OS: ~/.snowsql/
  • Windows: your-user-folder.snowsql

 

3.2 Open the file named config and add the following

[connections.configuration-name]

accountname = your_account_name

username = your_username

password = your_password

dbname = SNOWFLAKE_SAMPLE_DATA

warehousename = yourname_WH

schemaname = TPCDS_SF100TCL

• Save and exit
• Connect using the following command

snowsql -c configuration-name

 

Step 4 – Modify Display Prompt

 

SnowSQl prompt automatically displays the current user, warehouse, database, and schema. The tokens can be seen in the image above. This prompt can be a bit lengthy but you can edit the prompt with the following command:

!set prompt_format=>>

To auto-change the prompt format, add the following to the configuration file.

[options]

auto_completion gives you possible existing options, very helpful!

auto_completion = True

prompt_format=>>

 

Q&A’s Section:

 

1. What does the -c mean?

 

Whenever you run a program in the terminal you can specify arguments with a dash (-). The -c parameter tells the program snowsql to look in ~/.snowsql/config for a connection named lmunro_config. It then uses those credentials and other configurations to quickly log you in and set up your environment. Note: -c is an abbreviation. You can also use –connection.

 

2. Are there any other parameters that I should know about?

 

Yes! There are a bunch of parameters that can make your life easier. You can log in and set up your environment all in one line, like this:

snowsql -a **.east-us-2.azure -u lmunro -d SNOWFLAKE_SAMPLE_DATA -s TPCDS_SF100TCL -w LUCASMUNRO_WH

Don’t worry if that’s a bit overwhelming. You can (and should) use the config file so you don’t need to type it all out. If you’re interested in using these parameters or want more information check out the docs.

 

3. What is the config file?

The config file is a file that can be edited to set configuration and options for the SnowSQL CLI. It allows you to preset login credentials and database settings by adding a [connections] block and specify options by adding to the [options] block. For more information, you can check out the public documentation at https://docs.snowflake.com/en/user-guide/snowsql-config.html.

 

Conclusion

 

SnowSQL CLI is a quick way to plug into Snowflake directly from the terminal. It’s preferable to the UI if you already have a grasp of terminal operations and don’t require the UI to navigate around.

The config file in the SnowSQL folder is where you can set configuration and options for the CLI. You can preset login credentials and database settings by adding a [connections] block and specify options by adding to the [options] block. For more information check out the public documentation.

Introduction

Python has become one of the go to languages for data analytics. I’m constantly using jupyter notebooks to quickly clean, analyze, and visualize data. That’s why I was ecstatic to learn that my favorite data warehouse, Snowflake, has a simple python connector. In fact, it took me just 10 minutes to set up my environment and start running analytics on some COVID-19 data!

Allow me to walk you through it.

Overview

The Snowflake Connector for Python provides an interface to develop Python applications which connect to Snowflake. The connector supports all standard operations.

Prerequisites

If you don’t have python yet, install it here.

This tutorial requires a minimum of Python 2.7.9 or Python 3.5.0, any version above that is supported. To check what version of Python you have installed on your machine, open terminal and run the following command:

python --version

If your python version is out of date, run this command to update:

python -m pip install --upgrade pip

Install Python Connector for Snowflake

Snowflake’s Python Connector is part of the Python Package Index (PyPI) so we can install it with pip or conda.

pip install --upgrade snowflake-connector-python #Or (if your using python 3) pip3 install --upgrade snowflake-connector-python #Or (if you prefer conda) conda install -c conda-forge snowflake-connector-python

Connecting to Snowflake with Python

Now that the connector is installed, let’s connect to Snowflake. I’m using jupyter notebooks but you can use any Python IDE for this. To begin, let’s import the Snowflake package we just downloaded.

import snowflake.connector

Now that we’ve imported the library, we’ll need 2 key pieces of information to connect to snowflake.

• Snowflake account and region
• User login and password

The snowflake account and region can be found in the URL when you log into Snowflake website. For example:

http://demo-account.demo-region.snowflakecomputing.com

The format is https://ACCOUNT.ACCOUNT_REGION.snowflakecomputing.com. So our account would be demo-account.demo-region. Your user information is the same you use to login to snowflake. We can create some variables to store this information.

sfAccount = ‘demo-account.demo-region’

sfUser = ‘demo-user’

sfPassword = ‘demo-pass’

Now we have all the information needed to use the Python connector in our application. The following example attempts to establish a connection and print out the version of snowflake we have running. If the connection fails, an error message is printed out.

import snowflake.connector sfAccount = 'demo-account.demo-region' sfUser = 'demo-user' sfPass = 'demo-pass' // Connection object holds the connection and session information with the database alive conn = snowflake.connector.connect( user = sfUser, password = sfPass, account = sfAccount ) // creates a cursor object to for execute and fetch operations cs = conn.cursor() try: cs.execute("SELECT current_version()") one_row = cs.fetchone() print(one_row[0]) finally: cs.close() conn.close()

There you go! We’ve just connected to our snowflake database with python and retrieved some information. This should serve as a starting point for you to build your application. To dive deeper into what snowflake operations you can do with python check out the official documentation.

Introduction

Big Data. Internet of Things. Social Media. Everyday millions of data points are generated and moved across the internet, from the viral video you send to your friend on TikTok to critical business data to make decisions. The acceleration of data use and utility is only getting faster. That’s why security and protecting your data is of the utmost importance. That's where Secure Data Sharing comes in play.

The cloud is the solution. Snowflake’s platform offers instant access to live data in a secure format. This feature can not only streamline data sharing inside organizations but also how we share data to the outside. Many companies continue to use outdated data sharing technologies which are more costly and less secure.

How Does Secure Data Sharing Work?

Sharing involves two parties: the data provider and one or more consumers. Snowflake enables sharing of database tables, views and user-defined functions (UDFs) using a Secure Share object. A data consumer given a Secure Share has access to a read-only version of the database in their own account to operate on.

With Snowflake’s data sharing, no actual data is copied or transferred between accounts. All sharing is accomplished through the service layer and metadata store. Thanks to Snowflake’s architecture and separate compute from storage, data sharing is instant and prevents storage costs.

Sharing is done with a Secure Share object. Each Secure Share includes: privileges that grant access to the database, schema, consumer accounts and objects being shared (tables, views, UDFS). The share object represents a connection between the provider and consumer. New objects or data can be added to the object and will be available to the consumer in real time. Access to a share can be revoked at any time as well.

Two Types of Consumers

Participates in Secure Data Sharing are either data provider or consumer. A provider creates a Secure Share to export, and the consumer imports the Secure Share. However, there are two different types of consumers: reader accounts and full consumer. The difference affects who pays for the storage resources.

Full consumer accounts are existing Snowflake customers. Data can be shared directly to the existing account and the existing account pays for all computer resources incurred by querying the database.

Reader accounts are from consumers not on Snowflake’s platform. If the consumer is not a Snowflake customer, the provider can create Reader Accounts. For a Reader Account, all costs would be paid by the provider who shared the data. All costs can be tracked and invoiced back to the consumer.

Setting up a Secure Share with Reader Account

Let’s begin with an example using my personal database (BDU), which has a schema containing flight data (FAA). There are eight tables in the schema:

Click the Shares tab at the top of the user console, making sure that you toggle the view to Outbound since you are a provider creating a Secure Share that will go to an outside account. Then hit the Create button to open the menu to create a Secure Share. Please note that you need ACCOUNTADMIN privileges, or a custom role that has been granted these specific privileges, to create a Secure Share. SYSADMIN privileges will not be enough:

In the menu, insert the Secure Share Name (SHAREDEMO), and click the button to Select Tables & Secure Views to select the eight tables. Please note that you can select or deselect tables or views as needed. Next, hit the Create button to complete the process:

Now you see that Secure Share has been created and the data can be previewed. Please note that you need an available Virtual Warehouse that is usable by your current role in order to run this query. Next, click the button to Add Consumers:

As you can see, there are two Account Types: Reader and Full. Full is an existing user which will assume costs. Reader does not, the data provider pays for them. In this case, the intended consumer is not a Snowflake customer, so click the Create a Reader account link:

To create a Reader Account, insert the name (READERACCOUNT) and a comment (optional), along with credentials for the ACCOUNTADMIN user (READER1). Then click Create Account:

Going back to a worksheet and executing the command SHOW MANAGED ACCOUNTS will show all details needed:

Going back to the previous Add Consumers menu, you can now see that the new Reader Account has been added as a Consumer of the Secure Share. Clicking the blue link with the account locator (XB28199) will also take you the login for the Reader Account:

Enter the credentials for the ACCOUNTADMIN user:

Once you are in your Reader Account, you will need to do some setup. First, ensure that your role is ACCOUNTADMIN (it will default to SYSADMIN). Then click the Warehouses tab and create a warehouse to use (DEMO_WH). You will need compute resources to run queries from your provider.

Click the Shares account, making sure that you are toggled to Inbound. We see the SHAREDEMO has been shared to us by the ITSTRATEGISTS account we were using, which is the data provider in this case. Click Create Database From Secure Share:

In the menu, name your database accordingly and grant access to the desired roles. You can change the name of the database given by the provider. You can grant access to the database to multiple roles. Click Create Database:

Navigate to Worksheets. The shared database (SHARE_FAA) now has read-only tables you can query using the SYSADMIN role. As the consumer, you should be able to read from it using a reporting tool like Tableau or an ETL tool. If you’re a Full Consumer, you can query this data along with your existing data while not paying any storage costs.

Database tables are read-only. To change this, you can create another database and table by selecting from the shared tables. In this case, a table that has 148 million records took 27 seconds to move over using a medium-sized warehouse. Unfortunately, you cannot use Snowflake’s clone feature on a shared database.

Frequently Asked Questions

What is the difference between sharing data with existing Snowflake customers versus non-Snowflake customers?

Existing Snowflake customers bear the costs of any storage or compute incurred from querying or copying the shared database. For a non-Snowflake customer, the provider would create Reader Accounts and pay for all the costs incurred by those accounts. As the provider, you would be able to track account usage and bill the consumer if that was part of the set business agreement.

How fast would the data update for consumers?

Instant! The data is stored and shared on the cloud so the provider and consumer see the same data. The Secure Share is a metadata wrapper that points to the correct data that is still sitting with the provider. So any changes to the provider’s dataset would be reflected instantly from the consumer’s viewpoint.

Once the consumers can see the data in their account, what can they do with it?

They can query and run any analytics they desire such as Tableau or Sigma. If they wish to manipulate the data, they can copy that data into their own database, so they would have write access as.

Can I allow my users to see only selected tables or views?

Yes! Snowflake has a role-based security framework, so the Account Administrator can limit which roles are accessible to certain users. Roles are a collection of permissions granted on objects. Therefore, what a user can see depends on what permissions have been granted to the user’s role.

What if my consumers go overboard with how much they are using the Reader Account?

As the data provider for Reader Accounts you control usage. Set up Resource Monitors, which impose limits on the number of credits that virtual warehouses use within a specified interval or date range. When these limits are reached or are approaching, the Resource Monitor can trigger an alert and suspension of the warehouse.

Overview:

 

Here is a practical guide to integrating Databricks with Snowflake. We will get you started with the basic setup and show you how easy it is to get the two of them connected to write and read data from each other.

 

Pre-Requisites:

 

1. You must have a Snowflake Account (the good thing is that this is easy!)– You get a 30-day free trial, including $400 of free credits. Open a Free Snowflake Account Setup here

2. You need to set up at least a Databricks Community edition (The Community Edition is free) – The Databricks Snowflake connector is included in Databricks Runtime 4.2 and above.

 

Try Databricks Free

You should have some basic familiarity with Dataframes, Databricks, and Snowflake.

 

At a high level, we are doing these main steps:

Step 1: Import a notebook that already has a shell of the code you need.

Step 2: Fill in the details in the notebook for your Snowflake database.

Step 3: Execute the 3 separate parts of the notebook which will be:

Step 4: Make the connection.

Step 5: Write a data frame to snowflake.

Step 6: Read a snowflake table back into a data frame.

 

Steps:

 

Okay. Now that you have a Databricks Account setup then log in. I’m going to assume you have the Community Edition so the login is here: (if you have the regular editions then login to the appropriate area).

https://community.cloud.databricks.com/login.html

Then once you are logged in you should see a screen like this:

 

 

Go to the Workspace icon (It is the 3rd from the top on the left-hand side) Once you click on it then on the right there will be a dropdown arrow to the right of the menu item “Workspace”.  When you click there then click on Import and it should look like this:

 

 

Then Choose the URL there and put in this notebook link and click the Import Button. https://docs.databricks.com/_static/notebooks/snowflake-python.html

• This is one of my favorite parts about Databricks, they make it look easy to share Notebooks and be more organized.

Once you have imported the notebook it should look like this:

 

 

There are 3 main sections to this sample connector notebook from Databricks:

 

1.  The Snowflake connection:

 

You need to fill in all the details in blue. You should set up the data bricks secrets to start.

Then make sure you add the other appropriate details here (database, schema, warehouse):

• You will notice on the image there is in the upper right a run button. If you have worked with Jupyter Notebooks this is very similar.

 

2.  Write Data to a Snowflake table:

 

Fill in the sections in blue. Mainly just what table you want to write to. If the table is not created then it will create it for you.  If you wish to carry out further tests with your data and create a data frame, you can modify this section accordingly: spark.range(5).write.

 

3.  Read Data to a Snowflake table:

 

Fill in the sections in blue. Mainly just what table you want to read from which is the one you just created.

This is a straightforward example that demonstrates how to connect Databricks and Snowflake. The process of connecting Spark outside of Databricks is also relatively easy but requires setting up the Spark to Snowflake Connector and the JDBC Driver. Fortunately, Databricks 4.2 and higher come with these already set up, which makes the process even smoother.

Find out more about all the benefits Snowflake has to offer you and your business.

 

Conclusion:

 

In conclusion, integrating Databricks with Snowflake is a simple process that can be done in just a few steps. By following the guide above, you can easily connect the two platforms and write and read data from each other. This integration opens up many possibilities for data analysis and business intelligence, making it a valuable tool for any organization.

What is Confluent in Snowflake?

Confluent is a data streaming platform based on Apache Kafka. It allows for the integration of different data sources and the processing of large amounts of data in real-time. In this document, the instructions are provided for setting up Confluent with Snowflake, a cloud-based data warehousing and analytics platform. The purpose of this integration is to move data generated in Confluent/Kafka into Snowflake for further analysis and insights.

 

How To Set Up Confluent with Snowflake:

Here is a practical guide to getting started with setting up Confluent with Snowflake

Pre-Requisites:

1. To start, you'll need a Snowflake Account. Don't have one yet? Not to worry - you can sign up for a 30-day free trial with $400 credit at Free Snowflake Account Setup.
2. This setup requires using Docker. (I’ll have separate instructions to do this without Docker later)
3. You also need git.

Here we go – there are 3 main parts to this setup:

1. Get this docker version of confluent/Kafka up and running.
2. Create a topic on it to generate data for moving data into Snowflake.
3. Setup the Kafka to Snowflake Connector as the Destination with the right Snowflake connectivity.

Part 1 – Get the docker version of Confluent/Kafka running

Okay…The first time it will take a few minutes to download… and you will see the output eventually like this:

If you want to verify that everything is up and running then execute this command:

Part 2 – Create a topic to send Data to Snowflake. Generate data for it with the DataGen functionality.

Let’s go… execute these commands in sequence:

Create a topic:

Generate data for the topic: Let’s first configure a JSON file for the data we want to create. Use whatever editor you want and create this file u.config.

I’m using vi u.config and pasting this in there:

u.config details

Part 3 – Download the Kafka to Snowflake Connector and configure it.

So you have Confluent/Kafka up and running. You have data generated into a topic.

So now just download the magical Kafka to Snowflake connector here: https://mvnrepository.com/artifact/com.snowflake/snowflake-kafka-connector/0.3.2 I’m sure by the time I publish this the version will change but for now, assume it’s this one.

Once you have the file in the same directory we have been using for everything then copy it to the connected virtual machine where it needs to be to work.

You now need to create the configuration file to set up the Connector and the Sink associated with it that connects to the Snowflake Database.  This does assume you have already set up your RSA key.  You do have to fill in 6 of the settings below to have this setup for your specific configuration. Again, use your favorite editor.  I’m using:vi connector_snowflake.config and entering in my specific details.

connector_snowflake.config details

Almost there. Now use this configuration file to set up the sink.

Now in a few seconds or minutes if you set up everything correctly the topic should be written on the Snowflake table. Go into the database and schema you connected to and you should be able to execute something like:

Now you should see data flowing from Kafka to Snowflake. Enjoy!

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