What is your primary use case for Snowflake?

Mostly, we use it for the data warehousing side of use cases, where you have, like, a huge amount of data, and you are required to do reporting in terms of data science, data warehousing, or ad hoc reporting. The use cases we have used are, for example, data coming from MedTech devices, mostly sensor data, which we need to load in Snowflake and do data analytics. We have been using the tool for a couple of MedTech clients.

Our company uses the solution for building a data platform, data warehouse, and data transformation. The product is somewhat used for data analytics, but it is mostly for data engineering.

I use the tool with visualization tools like Tableau and Power BI. We load the data into these tools and use them to build customer reports. We often need to write scripts to perform transformations before sending the data to the visualization tools.

The solution has use cases related to retail stores and sales.

We use Snowflake as a database to store all the data we stream from the source system.

Snowflake is good for analytical purposes when you have a lot of historical or sales data that you need to release and use for different types of analysis, such as tracking sales and measuring the performance of your sales team and product.

I use it for data warehousing. I just design databases, put data in there, and get data out.

We use the solution for data storage and integration purposes.

We use Snowflake as a data migration platform.

Our primary use case for Snowflake is inputting data generated by AWS.

Our current plan and use case for this solution is to migrate the data from on-premises to the cloud. We are currently using on-prem monitor data and providing it on the cloud. We are using Snowflake so that once the data is in there, we are trying to create shares over it so that external systems can't consume it.

I mostly build or design data warehouse analytics solutions using Snowflake.

In our organization, data is often spread out over multiple on-premise and cloud-based platforms. Snowflake is an agnostic platform, meaning it can be used regardless of which cloud provider we use and can serve as a single source for all of our data. Our use case then is to ensure that all data is located in one place, utilizing Snowflake as the platform.

The use cases are data warehousing, data pipelining, data engineering, reporting, and dashboarding. Instead of using Oracle or other systems, you can simply put Snowflake there. It works like a normal database. This way, you avoid consuming the system with extra data. So the experience so far is good.

We're using it more for data warehousing and distribution. Snowflake is a SaaS platform, so I'm using whatever is the latest version.

I am using Snowflake for all our apps and data warehousing requirements.

We are currently using this product for data lake and data warehouse projects. Snowflake creates our repositories and enables the view from other tools like Docker and Power BI to create that data mesh. We use the data to create a process inside the warehouse. We are customers of Snowflake and I'm the senior manager of information technology infrastructure.

My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources. The ultimate goal is to provide reporting and analytics to all departments in the company.

We're using Snowflake for Power BI Cloud. We had a cloud version of Snowflake, so we were connecting to the Snowflake database and importing required tables into our system, Power BI Desktop. From there, we linked those tables and created a semantic layer, an internal layer between the frontend and backend, and then we tuned the data. Then we used both the tables to tie into the dashboards that we developed. The dashboards show the sales information or marketing information. It's a cloud solution.

We have day-in and day-out data coming from our various heterogeneous source systems. We read from that and we load data into our target tables, or rather data lake and data warehouse that is built into Snowflake. We can even translate and delete items. Apart from that, we use the tasks and schedule jobs within Snowflake. Once the data is generated into the final data material, we share it with the client for their review.

We are using it for optimizing costs and using financial data.

We are also using Apigee we have various consumption patterns, data enrichment, and few shedding of the data, and everything goes into Snowflake. If it is multiple consumers, it goes into AMQ, Kafka, or multiple streams to consume. There are specific APIs that we offer after we send the data into the S3 bucket. We have Apigee APIs for consumption, and there are three to four different patterns. For example, we enrich the data, flatten it, and structure everything before the customers going to go into Snowflake. There are going to be specific clients who need specific data from the overall data lake, those are going to be exposed as APIs. We have multiple customers needing the same data and for this, we move them into the streaming Kafka. Apigee does not communicate directly with Snowflake. We have data registration, and everything is coming into something that is called the trusted bucket. The Apigee interface API is written off the S3 bucket. The S3 bucket data is moved into the Delta Lake, and where the data are stored from the Delta Lake, it sends it to Snowflake. We have Apigee going to Delta Lake and S3 bucket, but Apigee does not go to Snowflake, these are two areas where it goes to. We have Kafka consuming directly off Delta Lake, and it sends data to Kafka through the AMQ. We have its setup, and we have interfaces that come directly to Snowflake to pull the data. It is then flattened and enriched, and it is used for many purposes, such as reporting.

We are silver or gold partners. The main use case is that we are building a data lake. We are creating a couple of downstream applications as well that will be used by data scientists. So, we will have a single data lake that will be used across the organization by different business domain users. The data is multi-source. We have data from SAP, JDE, and some Excel files.

I am a solutions architect for Snowflake.

I have been working on Redshift, Snowflake, and AWS RDS Oracle. In the particular case of RDS Oracle, they were migrating from on-prem Solaris equipment to cloud-based RDS. I would suggest Snowflake for anyone with the need for a reporting/business analytics view of their data that wants only wishes to maintain technical FTE's around processing the data into or out of a data repository but, doesn't want to go to extent of technical management of "AWS clusters" for the data repository.

We use Snowflake for data warehouse modeling and reports.

We use it as a traditional data warehousing application that we then set all of our reporting tools on top of.

Snowflake is used in my organization for our data warehouse.

We are using it for a migration from on-prem to cloud.

I use this solution for actively building out the cloud data warehouse and data platform for enterprise level customers as well as startups. Generally, our clients are looking for a data warehouse on the cloud to enable them to scale infinitely at a lower cost. I've worked for a finance analytical team building their data lake, the data platform on top of Snowflake, as well as for a telehealth team. It's basically about getting data from multiple sources and building out an entire data platform with data governance. We are customers of Snowflake.

We are using it as a Datalake and a DWH.

We have a data mart, and we are using it to share data with big enterprise customers with major security requirements.

It is used in my company as well as in my client's company. We are a system integrator, so naturally, we need to have the centers of excellence and competencies in Snowflake.

We have used Snowflake as a data warehouse solution in one of my projects and as a combination of data lake and DWH for another project. In the second project, we migrated from a SQL DB to Snowflake as the DB was becoming a bottleneck in terms of storage and also in speed of execution of the queries as the data was growing. We also have JSON, which is hard to store and process in a SQL database. This is something that is handled beautifully by Snowflake. In the first project, we used Snowflake as a simple DWH to store and process data. Also, as a BI reporting source.

We use the solution for data warehouses and data modeling.

We're running a POC to test scalability, performance, on-demand resource management, workload management, et cetera. The security aspect will also be important for us.

The solution provides services to our customers.

We primarily use the solution to build some cost-effective solutions for a data warehouse, mostly for all non-transactional data.

For Snowflake, we had four main use cases. The first use case was related to a data warehouse, and my banking client wanted to move his SQL Server database to Snowflake. All the source systems were also on Oracle and file-based systems, and the target data warehouse was SQL Server. From SQL Server, the client wanted to move to Snowflake. The second use case was related to a chat or messaging client. They were using EMR Hadoop as their data warehouse, but it was not performing, so we had to move the EMR Hadoop to Snowflake. The third use case was related to a ServiceNow compliance system, where ServiceNow was using SAP HANA for its reporting data warehouse, but it was too slow. It was not performing, and it was causing a lot of problems. We moved that ServiceNow compliance system from SAP HANA to Snowflake. The fourth use case was related to a huge SQL Server database for a banking client. We moved the entire SQL database to Snowflake.

We implement this solution for our customers. It is a cloud data warehouse. It is SaaS, and it can be run on Azure, AWS, or something else. We are using its latest version.

We are using it for our security products. We have a trial account, and we are using the trial database and practicing on top of it. We have the latest version of this solution.

It was only a workshop with training to know the tool. We were just testing the technology, and it was just a demo of the tool. We wanted is to connect switches with IoT and use Snowflake as an engine to process all the big data. It was on top of AWS, but our infrastructure is on top of the Google Cloud Platform. The intention was to see if we can process on the front-end that we have. We have a console that processes a big amount of data. Instead of using BigQuery, we used Snowflake to see if it is cheaper than using BigQuery, but Snowflake wasn't cost-effective. In the end, we didn't go for this solution. We just saw how it can be implemented, but we never bought anything.

I have used it in my previous company. It was just a SQL server data warehouse using reporting tools on top of it. It was an on-premise SQL server environment, and it was a typical data warehouse use case, but we wanted to do things faster and more cost-effectively. We used it to modernize our data warehouse. We didn't want to invest more in on-premise servers, and we were looking for a way to quickly get more data joined together.

We are a consulting company so our primary use depends on the niche that we are providing the services to and on which of the different versions they have. I think we are mainly using Snowflake Enterprise. In general, it is being used for integrating information. Snowflake is a database platform, it gives information to support analytic needs, such as advanced data analytics like machine learning. In some of those cases it is also used for descriptive analytics, for instance BI.

We use this solution for business intelligence. We have three persons in the team for developing the end product.

We primarily use the solution for the data warehouse.

We are a management consulting firm and do not use this product for ourselves. Rather, it is a service for our clients.

We use it for presentations to clients.

I work for a company that are Snowflake partners and help clients implement solutions using Snowflake.

We have different data models established on Snowflake so our primary use case is to store data from different sources, such as Azure Data Factory, or Databricks. We use it to create the data coming from different sources, and then we store the data. In addition, we also have a reporting structure that we use. We are partners with Snowflake and I'm a lead data engineer.

We use Snowflake for data warehousing.

Snowflake is used for very large data, such as in the case where tables might contain 600 to 700 million records.

We work with multiple customers who were asking for this and other similar solutions. We've since had several team members certified in Snowflake and we have a certified team working with that solution and keeping up to date with developments. I'm the general manager of the company and we are implementers.

I work as a data scientist and our primary use of Snowflake is for machine learning. Recently, we were trying to extract data to determine the best configuration settings for one of our products.

We primarily use the solution in order to have the daily transactions of trades. It's to manipulate and find out the benchmark of every broker and institutional manager.

We use the solution for data. We like that there are so many different formats and many structures for analysis.

The primary use case is big data warehouses.

We are a big data company. We have many thousands of devices deployed from our customer base. These devices upload data, on an hourly basis, to a central storage. Next, we run some ETF processes that crunch and process data, then we store that data in a structured way on Snowflake. Over the past six months, it has been more of a development project. I am using the latest version.

The primary use case for Snowflake is in our data warehouse project. We have a private DW and whoever has the credentials can access it. I am a data integration developer and we are using ETL tools to extract the data from different source systems and then load it in the data warehouse.

We use this product basically for developing an IoT (Internet of Things). Currently, we are sending data from our S3 (Simple Storage Service) storage. In the future, we are planning to directly stream data to Snowflake.

We needed a data warehouse and we made a decision on what is the right tool for us as a data warehousing tool by comparing products. We looked into Microsoft Azure, Red Shift and Snowflake. In the end, we decided on Snowflake because it looks more up to date, it seemed much better purposed as a data cloud solution. It was developed from scratch and dedicated to being used on the cloud and that was what we were looking for. It was not just an on-premises system which was then converted to use on the cloud. It was completely developed from scratch and purely focus on the cloud. Because it was programmed with that dedication, it has some significant advantages.

We primarily use the solution for data warehousing.

We are an IT Analytic Consulting company and we work with many different products. We have Snowflake and a Snowflake account mainly for education purposes and our internal training. We connected it to different sources, mainly internal sources. Most of them are on-premises and some are on the cloud. The deployment model is public.

Our aim was to migrate everything from on-premise, so we just migrated as it is and then we had issues. Some use cases that were running on-premises were not installed. We just went through each case and then finalized the issues with some of the packages that were not working or some users that were not getting what they were expecting. We did deep analysis on each and every case and then looked for options in Snowflake and are now working with the team to move everything over to Snowflake.

We use Snowflake for our data warehouse. Amazing product. Redshift cannot compete with a true elastic data warehouse where you can scale computing by just issuing a SQL query (increase computer power) and resizing it down or putting computing unit to sleep. Snowflake has many more features: When combined with Alooma, it's the best data integration system. No need for Talend and all these cumbersome tools.