Snowpark is a programming library that allows you to develop applications that process data in Snowflake without moving data to the system where your application code runs. It provides a high-level API for interacting with Snowflake data, and it supports popular programming languages such as Python, Scala, and Java.

SnowSQL is a command-line tool that allows you to interact with Snowflake using SQL. It can be used to run SQL queries, manage Snowflake objects, and develop and test Snowpark applications.

Native Apps is a Snowflake framework that allows you to create and share data applications with other Snowflake users. Native Apps applications can be used to perform a variety of tasks, such as data analysis, machine learning, and data visualization.

Snowpark and SnowSQL can be used together to develop Native Apps applications in a number of ways. For example, you can use Snowpark to develop the business logic of your application, and then use SnowSQL to deploy and manage the application in Snowflake. You can also use Snowpark to develop a command-line interface (CLI) for your application, and then use SnowSQL to expose the CLI to other Snowflake users.

Here are some specific examples of how Snowpark and SnowSQL can be used to develop Native Apps applications:

Develop a machine learning model in Python and deploy it to Snowflake using Snowpark. This would allow you to run the model on Snowflake data without having to move the data to a separate machine learning platform.
Develop a data visualization application in JavaScript and share it with other Snowflake users using Native Apps. This would allow other Snowflake users to visualize the data in your application without having to develop their own visualization tools.
Develop a CLI for managing Snowflake objects using Snowpark and SnowSQL. This would allow you to automate tasks such as creating and deleting databases, tables, and views.
Overall, Snowpark and SnowSQL are powerful tools that can be used to develop a wide variety of Native Apps applications. Snowpark provides a high-level API for interacting with Snowflake data, and SnowSQL provides a command-line tool for developing and managing Snowpark applications.

Here are some additional benefits of using Snowpark and SnowSQL to develop Native Apps applications:

Performance: Snowpark applications are highly performant because they are executed on Snowflake's infrastructure.
Scalability: Snowpark applications can scale to process large datasets without any performance degradation.
Ease of use: Snowpark and SnowSQL are easy to use, even for developers who are not familiar with Snowflake.
If you are looking to develop data applications within Snowflake, then Snowpark and SnowSQL are two essential tools that you should consider using.

AWS is not currently planning to launch a feature that is directly equivalent to Snowflake Native Apps. However, AWS does offer a number of features that can be used to build and deploy data-driven applications, such as:

Amazon SageMaker: Amazon SageMaker is a machine learning service that makes it easy to build, train, and deploy machine learning models. Amazon SageMaker provides a number of features that can be used to build data-driven applications, such as pre-built machine learning algorithms, Jupyter notebooks, and Model Serving.

Amazon EMR: Amazon EMR is a managed Hadoop service that makes it easy to run Hadoop, Spark, and Hive clusters. Amazon EMR can be used to build data-driven applications that require high-performance data processing capabilities.

Amazon Athena: Amazon Athena is a query service that makes it easy to analyze data in Amazon S3 using standard SQL. Amazon Athena can be used to build data-driven applications that require real-time or near-real-time data analytics.

In addition to these features, AWS also offers a number of other services that can be used to build and deploy data-driven applications, such as Amazon Redshift, Amazon DynamoDB, and Amazon Kinesis.

While AWS does not currently offer a feature that is directly equivalent to Snowflake Native Apps, its existing features can be used to build and deploy a wide variety of data-driven applications.

It is also worth noting that AWS is constantly innovating and adding new features to its services. It is possible that AWS will launch a feature that is more similar to Snowflake Native Apps in the future.

Snowflake Native Apps are still in early access, and the ability to install them in your Snowflake account has been available on AWS since May 2023. Snowflake Native Apps are not yet available on Google Cloud Platform or Microsoft Azure.

Snowflake plans to make Snowflake Native Apps generally available on all three cloud platforms in the near future.

To install a Snowflake Native App in your Snowflake account, you will need to:

1. Go to the Snowflake Marketplace.

2. Find the Snowflake Native App that you want to install.

3. Click on the Install button.

4. Follow the on-screen instructions to complete the installation process.

5. Once the Snowflake Native App is installed, you can access it from the Snowflake Console.

There are a number of options for monitoring and troubleshooting issues in Snowflake Native Apps:

Snowflake Native App Framework: The Snowflake Native App Framework provides a number of features for monitoring and troubleshooting Snowflake Native Apps. For example, the framework provides logging and event-handling capabilities. You can also use the framework to collect and analyze performance metrics.

Snowflake Monitoring Tools: Snowflake offers a number of monitoring tools that can be used to monitor Snowflake Native Apps. For example, you can use the Snowflake Performance Monitor to monitor the performance of your Snowflake Native Apps in real time. You can also use the Snowflake Alerting System to set up alerts for specific events, such as when a Snowflake Native App experiences an error.

Third-Party Monitoring Tools: There are a number of third-party monitoring tools that can be used to monitor Snowflake Native Apps. For example, you can use a third-party monitoring tool to collect and analyze performance metrics, logs, and events for your Snowflake Native Apps.

Here are some specific examples of how you can use the above options to monitor and troubleshoot issues in Snowflake Native Apps:

Use the Snowflake Native App Framework logging and event handling capabilities to collect logs and events from your Snowflake Native Apps. You can then analyze these logs and events to identify any errors or performance problems.

Use the Snowflake Performance Monitor to monitor the performance of your Snowflake Native Apps in real time. This can help you to identify any performance bottlenecks or spikes in activity.
Use the Snowflake Alerting System to set up alerts for specific events, such as when a Snowflake Native App experiences an error. This can help you to quickly identify and resolve issues.

Use a third-party monitoring tool to collect and analyze performance metrics, logs, and events for your Snowflake Native Apps. This can give you a more comprehensive view of the health of your Snowflake Native Apps and help you to identify and resolve issues more quickly.

By using the above options, you can ensure that your Snowflake Native Apps are monitored and troubleshooted effectively. This can help to prevent downtime and ensure that your users have a positive experience.

In addition to the above, here are some general tips for monitoring and troubleshooting Snowflake Native Apps:

Identify the key metrics to monitor: For each of your Snowflake Native Apps, identify the key metrics that you need to monitor to ensure that the app is performing as expected. These metrics may include things like CPU usage, memory usage, database response time, and error rates.

Set up alerts: Set up alerts for key metrics so that you are notified immediately if there are any problems. This will allow you to quickly identify and resolve issues before they cause downtime or impact your users.

Regularly review logs and events: Regularly review the logs and events for your Snowflake Native Apps to identify any potential problems. This can help you to identify and resolve issues before they cause major problems.

Have a troubleshooting plan in place: Have a troubleshooting plan in place for each of your Snowflake Native Apps. This plan should outline the steps that you will take to troubleshoot and resolve common issues.
By following these tips, you can ensure that your Snowflake Native Apps are monitored and troubleshooted effectively. This can help to prevent downtime and ensure that your users have a positive experience.

Here are some key considerations for cost management and resource allocation when using Snowflake Native Apps:

Choose the right pricing plan: Snowflake offers a variety of pricing plans, so it is important to choose the one that is right for your needs. If you are planning to use Snowflake Native Apps for real-time or near-real-time data processing and analytics, you will need to choose a pricing plan that supports these workloads.

Monitor your usage: Snowflake provides a number of tools and reports that can help you to monitor your usage and identify areas where you can save money. For example, you can use the Snowflake Usage Report to see how much you are spending on each type of resource.

Use Snowflake's cost optimization tools: Snowflake offers a number of tools that can help you to optimize your costs. For example, you can use the Snowflake Warehouse Sizing Advisor to recommend the right warehouse size for your needs. You can also use the Snowflake Resource Monitor to monitor your resource usage in real time and make adjustments as needed.

Allocate resources appropriately: When allocating resources to your Snowflake Native Apps, it is important to consider the following factors:

The type of application: Some applications, such as real-time data processing and analytics applications, require more resources than others.

The expected load: If you expect your application to experience a high load, you will need to allocate more resources to it.
Your budget: It is important to balance your resource allocation needs with your budget.
Here are some additional tips for cost management and resource allocation when using Snowflake Native Apps:

Use the Snowflake Native App Framework: The Snowflake Native App Framework provides a number of features that can help you to optimize your costs. For example, the framework provides a built-in caching mechanism that can help to reduce the number of times that your application needs to access the Snowflake database.

Design your applications efficiently: When designing your Snowflake Native Apps, it is important to keep efficiency in mind. For example, you should avoid running unnecessary queries and you should use the most efficient algorithms possible.

Test your applications thoroughly: Before deploying your Snowflake Native Apps, it is important to test them thoroughly under load. This will help you to identify any performance or scalability issues before they cause problems in production.

By following these tips, you can ensure that you are using Snowflake Native Apps in a cost-effective and efficient manner.

Snowflake Native Apps can facilitate real-time or near-real-time data processing and analytics in a number of ways:

Streams: Streams are a Snowflake feature that allows you to ingest and process data in real time. Snowflake Native Apps can use streams to ingest data from a variety of sources, including IoT devices, sensors, and social media platforms.
Once the data is ingested, Snowflake Native Apps can process it using SQL queries and other data processing languages.

Tasks: Tasks are a Snowflake feature that allows you to schedule and execute jobs on a regular basis. Snowflake Native Apps can use tasks to automate real-time data processing and analytics tasks. For example, you could use a task to schedule a job that runs every minute to process the latest data from your IoT devices.

Snowflake Native App Framework: The Snowflake Native App Framework provides a number of features that can help you to build real-time data processing and analytics applications. For example, the framework provides a built-in stream processor that can be used to process data in real time.

Here are some examples of how Snowflake Native Apps can be used to facilitate real-time or near-real-time data processing and analytics:

A financial services company can use Snowflake Native Apps to build and deploy an application that tracks stock prices in real time and generates alerts when prices move above or below certain thresholds.

A manufacturing company can use Snowflake Native Apps to build and deploy an application that monitors the performance of its production equipment in real time and generates alerts when problems are detected.

A retail company can use Snowflake Native Apps to build and deploy an application that tracks customer purchases in real time and recommends products to customers based on their past purchases.

Overall, Snowflake Native Apps are a powerful tool for real-time or near-real-time data processing and analytics. They offer a number of features that can help you to build and deploy applications that can process and analyze data in real time and generate actionable insights.

Snowflake Native Apps are a way to build and deploy applications directly on the Snowflake platform. This means that you can use the same data and infrastructure that you are already using for your other Snowflake workloads to power your applications. This can lead to a number of benefits, including improved performance, scalability, and security.

Traditional Snowflake features, on the other hand, are designed for data storage, processing, and analytics. They do not provide the same level of support for application development and deployment.

Here is a table that summarizes the key differences between Snowflake Native Apps and traditional Snowflake features:

Some of the benefits of using Snowflake Native Apps include:

Improved performance: Snowflake Native Apps are deployed directly on the Snowflake platform, so they have direct access to your data. This can lead to significant performance improvements, especially for complex queries and applications.
Increased scalability: Snowflake Native Apps can scale horizontally with the Snowflake platform. This means that you can add more resources to your application as needed, without having to worry about performance or downtime.
Enhanced security: Snowflake Native Apps benefit from the security features of the Snowflake platform. This includes features such as role-based access control (RBAC), data encryption, and auditing.
Overall, Snowflake Native Apps are a powerful new way to build and deploy applications on the Snowflake platform. They offer a number of benefits, including improved performance, scalability, and security.

Here are some examples of how Snowflake Native Apps can be used:

A data science team can use Snowflake Native Apps to build and deploy a machine learning model that predicts customer churn.
A sales team can use Snowflake Native Apps to build and deploy a CRM application that tracks customer interactions and opportunities.
A supply chain team can use Snowflake Native Apps to build and deploy an application that optimizes inventory levels and transportation routes.
If you are considering building and deploying an application on Snowflake, I encourage you to learn more about Snowflake Native Apps. They may be the best option for your needs.

Efficient data sharing and collaboration are pivotal in today's data-driven business landscape. Snowflake's Native Apps are equipped with features that facilitate these critical aspects:

1. Snowflake Data Exchange: Data Exchange is a marketplace for data providers and consumers. Data providers can publish datasets for others to access, while data consumers can easily integrate these datasets into their analytics processes. This platform encourages collaboration among organizations, opening the door to valuable data insights.

2. Snowflake's Secure Data Sharing: Secure Data Sharing enables organizations to securely share data with their partners, customers, and other stakeholders. With fine-grained access controls and robust security measures, data sharing becomes a streamlined and trustworthy process.

Data science and AI are at the forefront of data innovation. Snowflake recognizes this and has introduced specialized Native Apps to cater to these advanced use cases:

1. Snowflake Data Science: This Native App allows data scientists to perform data science tasks directly within Snowflake. It supports popular machine learning libraries, making model development and deployment a seamless process. By leveraging Snowflake's data-sharing capabilities, data scientists can access, analyze, and train models using Snowflake data.

2. Snowflake's Data Marketplace: This data-sharing platform offers access to a vast array of external data sources, enabling data scientists to enrich their analyses and models with diverse datasets. The integration of these datasets into Snowflake's ecosystem accelerates the development of AI and machine learning models.

Snowflake's Native Apps are a family of specialized applications, each tailored to address specific data tasks and roles. Here are some key Native Apps that are essential for different data professionals:

1. Snowsight - The Integrated Development Environment: Snowsight is Snowflake's native development environment, designed for data analysts, SQL users, and business intelligence professionals. It offers an interactive and user-friendly interface for ad-hoc querying, data exploration, and report generation. Users can collaborate, share insights, and make data-driven decisions.

2. Data Science: Snowflake's data science tools enable data scientists and AI practitioners to access and analyze data within Snowflake, making it easier to train machine learning models. With a secure and integrated environment, data scientists can streamline their workflows and collaborate effectively.

3. Data Sharing and Collaboration: Snowflake's native apps for data sharing and collaboration foster teamwork by simplifying data sharing both within and outside the organization. These apps ensure data accuracy and security while promoting efficient collaboration.

To better understand the value of Snowflake Native Apps, let's explore a few real-world use cases:

1. Business Intelligence (BI) and Reporting: Native apps like Snowsight, Snowflake's integrated development environment, are perfect for data analysts and business intelligence professionals. They offer a user-friendly interface for data exploration, ad-hoc querying, and report generation. Users can extract insights quickly, make data-driven decisions, and share findings with stakeholders.

2. Data Science and AI: Snowflake's native apps are a boon for data scientists and AI practitioners. With features like data sharing and advanced analytics, they can seamlessly access, analyze, and train machine learning models using Snowflake data. This integration accelerates the model development process and promotes data science collaboration.

3. Data Collaboration and Sharing: Organizations can use native apps to facilitate data collaboration among teams and partners. By sharing data directly through Snowflake Native Apps, they can ensure data accuracy, security, and compliance while fostering cross-functional teamwork.

Snowflake Native Apps come with a range of features that set them apart:

1. Integration with Snowflake's Data Sharing: Native apps are tightly integrated with Snowflake's data sharing capabilities, allowing users to share data securely with internal and external parties. This promotes collaboration and data-driven decision-making.

2. Simplified User Experience: These apps provide an intuitive, user-friendly experience, reducing the learning curve for users. They offer a consistent interface and a familiar environment for data tasks.

3. Streamlined Data Access: Native apps allow users to access data stored in Snowflake without the need for complex data transfers or copies. This minimizes data movement and ensures data accuracy.

4. Enhanced Security and Compliance: Security is a priority in Snowflake Native Apps, with built-in security features and robust compliance options. Users can access data while adhering to data privacy and governance regulations.

5. Performance Optimization: These apps are optimized for performance, ensuring efficient data processing and analytics. Users can work with data at scale without compromising speed.

Here at ITS, we are pioneers in developing Snowflake Native Apps. Stay tuned! We will soon be launching our own Snowflake Native App!

At its core, Snowflake Native Apps are specialized applications designed to work seamlessly within the Snowflake platform. They are not just integrations or add-ons but fully integrated tools that leverage Snowflake's architecture and capabilities. These apps cater to specific data roles and responsibilities, making it easier for users to access, analyze, and visualize data.

Snowsight provides a number of integration options with third-party tools and BI platforms. These include:

• Tableau: Snowsight integrates with Tableau, a popular data visualization tool. This integration allows you to export query results and visualizations from Snowsight to Tableau.
• Power BI: Snowsight integrates with Power BI, another popular data visualization tool. This integration allows you to export query results and visualizations from Snowsight to Power BI.
• Qlik: Snowsight integrates with Qlik, another popular data visualization tool. This integration allows you to export query results and visualizations from Snowsight to Qlik.
• Other BI platforms: Snowsight also integrates with a number of other BI platforms, such as Looker, Sisense, and Domo.
• Data warehouses: Snowsight can be integrated with other data warehouses, such as Amazon Redshift and Google BigQuery. This allows you to move data between different data warehouses.
• Data lakes: Snowsight can be integrated with data lakes, such as Amazon S3 and Azure Data Lake Storage. This allows you to analyze data in data lakes using Snowsight.

In addition to these integration options, Snowsight also offers a number of other integration options, such as:

• API integration: Snowsight provides an API that allows you to integrate Snowsight with other applications.
• Webhooks: Snowsight can send webhooks to other applications when certain events occur, such as when a query is completed or when a visualization is updated.
• Custom integrations: Snowsight also allows you to create custom integrations with other applications.

By providing a variety of integration options, Snowsight makes it easy to integrate Snowsight with other tools and platforms. This can help you to get the most out of Snowsight and to improve your data analysis workflows.

Here are some examples of how you can use Snowsight's integration options:

• Use the Tableau integration to export query results and visualizations from Snowsight to Tableau. This allows you to use Tableau's powerful visualization capabilities to create interactive and informative dashboards and reports.
• Use the Power BI integration to export query results and visualizations from Snowsight to Power BI. This allows you to use Power BI's collaboration features to share your insights with others and to work together on data analysis projects.
• Use the Qlik integration to export query results and visualizations from Snowsight to Qlik. This allows you to use Qlik's associative engine to gain deeper insights into your data.
• Use the Amazon Redshift integration to move data between Snowflake and Amazon Redshift. This allows you to take advantage of the best features of both data warehouses.
• Use the Amazon S3 integration to analyze data in Amazon S3 using Snowsight. This allows you to analyze large datasets that are stored in Amazon S3.
• Use the API integration to integrate Snowsight with a custom application. This allows you to build a data-driven application that uses Snowsight for data storage and analysis.
• Use webhooks to send notifications to other applications when certain events occur in Snowsight. For example, you could send a webhook to a Slack channel when a query is completed.

By using the integration options that Snowsight offers, you can integrate Snowsight with other tools and platforms to improve your data analysis workflows and to get the most out of Snowsight.

To set up permissions and access controls for different users within Snowsight, you can follow these steps:

1. Create roles. Roles are used to group users together and grant them specific permissions. To create a role, navigate to the Admin > Users & Roles page in Snowsight. Click the + Role button and enter a name for the role. You can also specify a description for the role.
2. Grant permissions to roles. Once you have created roles, you can grant them permissions to access different objects in Snowflake, such as databases, tables, and views. To grant permissions to a role, navigate to the Admin > Users & Roles page in Snowsight. Select the role that you want to grant permissions to and click the Permissions tab. Click the Grant Permissions button and select the objects that you want to grant permissions to the role for. You can also specify the specific permissions that you want to grant to the role.
3. Assign users to roles. Once you have created roles and granted them permissions, you can assign users to the roles. To assign a user to a role, navigate to the Admin > Users & Roles page in Snowsight. Select the user that you want to assign to a role and click the Roles tab. Click the + Role button and select the role that you want to assign to the user.

Once you have assigned users to roles, they will have the permissions that have been granted to those roles.

Here are some examples of how you can use roles and permissions to control user access to Snowflake objects:

• You can create a role for marketing users and grant them permissions to access the marketing database and tables.
• You can create a role for sales users and grant them permissions to access the sales database and tables.
• You can create a role for finance users and grant them permissions to access the finance database and tables.
• You can create a role for executives and grant them permissions to access all of the databases and tables in Snowflake.

You can also use roles to create a hierarchy of permissions. For example, you could create a parent role called "Manager" and then create child roles called "Sales Manager," "Marketing Manager," and "Finance Manager." You could then grant the "Manager" role permissions to access all of the databases and tables in Snowflake. You could then grant the child roles permissions to access the specific databases and tables that they need to access.

By using roles and permissions, you can control user access to Snowflake objects and ensure that users only have access to the objects that they need to access.

In addition to roles and permissions, Snowsight also provides a number of other features that can be used to control user access to Snowflake objects, such as:

• Resource monitors: Resource monitors allow you to track and control how users are consuming resources, such as CPU and memory.
• Access logs: Access logs track all activity that occurs in Snowflake. You can use access logs to identify users who are accessing unauthorized objects.
• Audit trails: Audit trails track all changes that are made to objects in Snowflake. You can use audit trails to investigate unauthorized changes to objects.

By using the features that Snowsight provides, you can control user access to Snowflake objects and ensure that your data is secure.

Snowsight handles complex queries involving multiple joins and subqueries by providing a number of features that make it easy for users to create and execute these types of queries. These features include:

• Visual query builder: The Snowsight visual query builder allows users to create complex queries without having to write any code. The visual query builder provides a drag-and-drop interface that allows users to easily select tables, join tables, and create subqueries.
• Query suggestions: Snowsight provides query suggestions that can help users to write more efficient and accurate queries. For example, Snowsight can suggest the correct table names, column names, and join conditions.
• Query validation: Snowsight validates queries before they are executed. This helps to identify errors in the query and to prevent the query from failing.
• Query optimization: Snowsight optimizes queries before they are executed. This helps to improve the performance of queries, especially for complex queries involving multiple joins and subqueries.
• Query profiling: Snowsight provides a query profiling tool that can be used to analyze the performance of individual queries. The query profiling tool provides information such as the execution time, the number of rows processed, and the amount of memory used. This information can be used to identify areas where the query can be optimized.

In addition to these features, Snowsight also provides a number of other features that can help users to manage and track complex queries, such as:

• Workspaces: Workspaces allow users to organize their queries and visualizations into logical groups. This can be helpful for managing and tracking complex queries, as users can easily identify the queries that are associated with a particular workspace.
• Permissions: Permissions allow users to control who has access to their queries and workspaces. This can be helpful for ensuring that only authorized users can manage and track complex queries.
• Version control: Version control allows users to track and manage changes to their queries and visualizations. This can be helpful for rolling back changes that have caused problems with complex queries.

Overall, Snowsight provides a variety of features that make it easy for users to create, execute, manage, and track complex queries. These features help to provide a seamless user experience for users of all skill levels.

Here are some specific examples of how Snowsight can be used to handle complex queries involving multiple joins and subqueries:

• Use the visual query builder to create complex queries without having to write any code. The visual query builder provides a drag-and-drop interface that makes it easy to select tables, join tables, and create subqueries.
• Use query suggestions to get help writing more efficient and accurate queries. Snowsight can suggest the correct table names, column names, and join conditions.
• Use query validation to identify errors in your query before you execute it. This can help to prevent the query from failing.
• Use query optimization to improve the performance of your queries. Snowsight optimizes queries before they are executed, especially for complex queries involving multiple joins and subqueries.
• Use query profiling to analyze the performance of your queries and identify areas where they can be optimized.

By using the features that Snowsight provides, users can easily create, execute, manage, and track complex queries.

Snowsight assists users in managing and tracking their query history and interactions with Snowflake in the following ways:

• Query history: Snowsight provides a query history view that shows all of the queries that have been executed by the user. The query history view includes information such as the query text, the start time, the end time, the duration, and the status of the query.
• Query results: Snowsight provides a query results view that shows the results of the most recently executed query. The query results view includes information such as the table schema, the data types, and the data values.
• Query explanations: Snowsight provides a query explanation feature that shows how Snowflake executed a particular query. The query explanation feature includes information such as the logical plan and the physical plan of the query.
• Query alerts: Snowsight allows users to create alerts for queries. For example, a user could create an alert to be notified when a query takes longer than a certain amount of time to execute.
• Query sharing: Snowsight allows users to share queries with other users. This can be useful for collaboration purposes or for sharing best practices.

In addition to these features, Snowsight also provides a number of other features that can help users to manage and track their interactions with Snowflake, such as:

• Workspaces: Workspaces allow users to organize their queries and visualizations into logical groups.
• Permissions: Permissions allow users to control who has access to their queries and workspaces.
• Version control: Version control allows users to track and manage changes to their queries and visualizations.

Overall, Snowsight provides a variety of features that can help users to manage and track their query history and interactions with Snowflake. These features can help users to improve their productivity, collaboration, and data governance.

Here are some specific examples of how Snowsight can be used to manage and track query history and interactions with Snowflake:

• Use query history to identify performance bottlenecks: Users can use the query history view to identify queries that are taking a long time to execute. Once a performance bottleneck has been identified, users can take steps to optimize the query.
• Use query results to debug queries: Users can use the query results view to debug queries that are not returning the expected results. By examining the query results, users can identify the source of the problem and take steps to fix the query.
• Use query explanations to understand how Snowflake executes queries: Users can use the query explanation feature to understand how Snowflake executes a particular query. This can be helpful for optimizing queries and for troubleshooting performance problems.
• Use query alerts to monitor query performance: Users can use query alerts to monitor the performance of their queries. This can be helpful for identifying queries that are taking a long time to execute or that are failing.
• Use query sharing to collaborate with other users: Users can use query sharing to collaborate with other users on data analysis projects. This makes it easy to share queries and visualizations with others and to get feedback.

By using the features that Snowsight provides, users can improve their productivity, collaboration, and data governance.