I mainly use Snowflake Analytics for creating the data lake in Snowflake, specifically the medallion architecture, where we have our data lake with different layers including staging, raw, and then the final layer where our data model sits, enabling us to do reporting on the top of this data model, and we also maintain sandboxes for AI/ML use cases. Majorly, we have an entire data lake in Snowflake Analytics with a huge volume of data and perform data analysis and prepare reports for the same. Regarding data model transformations, I have used features such as functions with tasks and user-defined functions, which help in transformation. Snowflake Analytics notebooks and Snowpark allow writing procedures in JavaScript, Python, or Java, providing strong support for transformations. I am using Snowflake Analytics on the cloud.
Lead Analytics Consultant at a outsourcing company with 51-200 employees
MSP
Top 5
Aug 13, 2025
In our organization, regarding Snowflake Analytics, we have a data engineering team who manages all the work on the back-end side, starting from the data load and managing all those pipelines. The only thing where we utilize Snowflake Analytics is running queries and performing UAT to ensure they are performing well. Apart from that, we also created views and stored procedures in Snowflake Analytics. In our company, we use Snowflake Analytics as a cloud data warehouse. Most of the projects are migration projects where clients migrate from a particular data warehouse or database to Snowflake Analytics. Our data engineering team acquires all the client requirements information, performs all the data engineering work, and puts that data in Snowflake Analytics. After that, they inform the analytics team that the data is pushed to Snowflake Analytics. We can then connect with any BI tool the client wants and start our development.
Solutions Architect at a financial services firm with 5,001-10,000 employees
Real User
Top 5
Apr 28, 2025
Our primary use case involves building a cloud data warehouse to support business analytics. The business requires analytics, and we are building this ecosystem to help senior management with data-driven decision-making.
Senior Software Engineer at a consultancy with 10,001+ employees
Real User
Top 10
Nov 11, 2024
I use Streamlit apps and Snowflake Analytics for analytics, data warehousing, migration, creating reports, and segregation. It is used to create reports for analytics for users.
RSA Country Manager at a non-profit with 1-10 employees
Reseller
Top 10
Sep 3, 2024
Our customer use cases vary. Some are doing AI models to pick up trends in the telco, finance, or insurance sectors. Our insurance clients use it more as a data warehouse or data lake for their customer data.
We use Snowflake as a database or data warehouse. We rely on AWS S3 services for computing and storage. It provides security, including role-based access and data encryption, and is handled at the database level in Snowflake. We use DBT for the data loading process, applying business logic to create the final tables in DBT, which then load the data.
I mainly do data analytics on Snowflake databases, which might be structured, unstructured, or semi-structured. It's basically for relational data, majorly. In some cases, it's for real-time data processing.
We primarily use the solution for database analytics. We can use it for various things, including structured and voice data. We also have a Microsoft SQL server for ONTP we combine and manage all data. We get a data map and have a data layer. For reporting, we use Power BI.
Associate Director - Delivery (Technology DWH & Data Engineer) at MOBIUS KNOWLEDGE SERVICES PRIVATE LIMITED
Real User
Top 10
Apr 5, 2024
We have chosen Snowflake to meet customer expectations. Some customers expect a partnership, while others primarily use migrations. They are using streaming data from an on-premises tool. The customer uses an existing system and partners with on-premises partners; we migrate them into the cloud system. We migrated from Oracle and Zoom to Snowflake using the AWS Group.
We use it in the process of collecting access revenue data from various vendors. Our next step involves integrating this data into Snowflake tables, after which we present it through dashboards for visualization and analysis.
Technical Lead at a computer software company with 10,001+ employees
Real User
Top 5
Feb 13, 2024
Snowflake's automatic optimization and scalability impressed us during POCs. Now, we store our data lake data in Snowflake, seamlessly integrating it with Azure Databricks for business transformations, ensuring smooth analytics processes.
Senior Solutions Architect at a tech consulting company with 201-500 employees
Real User
Top 10
Nov 17, 2023
It's primarily used for data migration, platform organization, and advanced analytics. As an Elite Partner, we provide consulting services to various customers.
We use it for our full application operational analytics, including building, storing, and analyzing the data. It provides us with detailed insight, making it easy to extract needed reports. Everything related to business intelligence, whether it's reporting or analytical modules, is stored on the Snowflake Analytics platform.
Data Analyst at a tech services company with 11-50 employees
Consultant
Apr 14, 2023
There are a lot of use cases for Snowflake. We can use it to build data pipelines. We can also use it to process terabytes of data to generate real-time analytics.
We use Snowflake Analytics to organize data. We create a fact and dimension table, relate it using its cardinality, load it from the source and use it for reporting.
The solution is a flexible data warehouse. It can be used for computing and storage. There's also the potential for automation. With Snowflake, you can use the same copy in multiple places. Once your data is on Snowflake, you can have that data differential to reportings. You can start using the analytics on top of it as well.
Conventional data platforms and big data solutions struggle to deliver on their fundamental purpose: to enable any user to work with any data, without limits on scale, performance or flexibility. Whether you’re a data analyst, data scientist, data engineer, or any other business or technology professional, you’ll get more from your data with Snowflake.
To achieve this, we built a new data platform from the ground up for the cloud. It’s designed with a patented new architecture to be the...
I mainly use Snowflake Analytics for creating the data lake in Snowflake, specifically the medallion architecture, where we have our data lake with different layers including staging, raw, and then the final layer where our data model sits, enabling us to do reporting on the top of this data model, and we also maintain sandboxes for AI/ML use cases. Majorly, we have an entire data lake in Snowflake Analytics with a huge volume of data and perform data analysis and prepare reports for the same. Regarding data model transformations, I have used features such as functions with tasks and user-defined functions, which help in transformation. Snowflake Analytics notebooks and Snowpark allow writing procedures in JavaScript, Python, or Java, providing strong support for transformations. I am using Snowflake Analytics on the cloud.
In our organization, regarding Snowflake Analytics, we have a data engineering team who manages all the work on the back-end side, starting from the data load and managing all those pipelines. The only thing where we utilize Snowflake Analytics is running queries and performing UAT to ensure they are performing well. Apart from that, we also created views and stored procedures in Snowflake Analytics. In our company, we use Snowflake Analytics as a cloud data warehouse. Most of the projects are migration projects where clients migrate from a particular data warehouse or database to Snowflake Analytics. Our data engineering team acquires all the client requirements information, performs all the data engineering work, and puts that data in Snowflake Analytics. After that, they inform the analytics team that the data is pushed to Snowflake Analytics. We can then connect with any BI tool the client wants and start our development.
Our primary use case involves building a cloud data warehouse to support business analytics. The business requires analytics, and we are building this ecosystem to help senior management with data-driven decision-making.
I have to get the data, load it into the warehouse, and analyze it to gain insights from the data.
I use Streamlit apps and Snowflake Analytics for analytics, data warehousing, migration, creating reports, and segregation. It is used to create reports for analytics for users.
Our customer use cases vary. Some are doing AI models to pick up trends in the telco, finance, or insurance sectors. Our insurance clients use it more as a data warehouse or data lake for their customer data.
We use Snowflake as a database or data warehouse. We rely on AWS S3 services for computing and storage. It provides security, including role-based access and data encryption, and is handled at the database level in Snowflake. We use DBT for the data loading process, applying business logic to create the final tables in DBT, which then load the data.
I mainly do data analytics on Snowflake databases, which might be structured, unstructured, or semi-structured. It's basically for relational data, majorly. In some cases, it's for real-time data processing.
We primarily use the solution for database analytics. We can use it for various things, including structured and voice data. We also have a Microsoft SQL server for ONTP we combine and manage all data. We get a data map and have a data layer. For reporting, we use Power BI.
We have chosen Snowflake to meet customer expectations. Some customers expect a partnership, while others primarily use migrations. They are using streaming data from an on-premises tool. The customer uses an existing system and partners with on-premises partners; we migrate them into the cloud system. We migrated from Oracle and Zoom to Snowflake using the AWS Group.
We use it in the process of collecting access revenue data from various vendors. Our next step involves integrating this data into Snowflake tables, after which we present it through dashboards for visualization and analysis.
I use the tool for data warehousing, analytics, and machine learning.
Snowflake's automatic optimization and scalability impressed us during POCs. Now, we store our data lake data in Snowflake, seamlessly integrating it with Azure Databricks for business transformations, ensuring smooth analytics processes.
We use it for tasks related to aggregate functions and statistical analysis.
It's primarily used for data migration, platform organization, and advanced analytics. As an Elite Partner, we provide consulting services to various customers.
It is a cloud data warehouse. We use the solution in our organization for setting up data warehouses, building tables, and querying from the tables.
We use it for our full application operational analytics, including building, storing, and analyzing the data. It provides us with detailed insight, making it easy to extract needed reports. Everything related to business intelligence, whether it's reporting or analytical modules, is stored on the Snowflake Analytics platform.
We use Snowflake Analytics for data cataloging, data science, and managing sales development.
My company uses Snowflake Analytics for data lake, data warehouse, data mart, and other related stuff.
We use the product to generate insights.
There are a lot of use cases for Snowflake. We can use it to build data pipelines. We can also use it to process terabytes of data to generate real-time analytics.
We use Snowflake Analytics to organize data. We create a fact and dimension table, relate it using its cardinality, load it from the source and use it for reporting.
We use the Snowflake platform for analytics and for data integration. We are partners with Snowflake. I'm the managing director.
Snowflake Analytics is used for data warehousing, reporting, and analytics.
The solution is a flexible data warehouse. It can be used for computing and storage. There's also the potential for automation. With Snowflake, you can use the same copy in multiple places. Once your data is on Snowflake, you can have that data differential to reportings. You can start using the analytics on top of it as well.
I mainly use Snowflake Analytics for Power BI reports.