Our data is available on-premises, so we are extracting the data from our private on-premises servers and Azure databases.

Our company uses the solution to mine generous amounts of data for customers. We are partners with Microsoft and are totally dependent on the solution. 

Dealing with big data is the primary use case.

Microsoft Azure Synapse Analytics differs from the old traditional on-premise business intelligence operations, where it's set up to do real-time analytics. For example, with IoT devices. Instead of having patients come to the hospital and do their operations, the hospitals will give patients an IoT device and you can monitor the patients in real-time using Microsoft Azure Synapse Analytics.

The point of the solution is to integrate the business into the analytics or vice versa. In the hospital example above, traditionally analytics is to tell us what happened. We look at reports to see what has happened. Microsoft Azure Synapse Analytics puts us more on the spot by telling us what's going to happen. Rather than what did just happen.

We use the product to transform the data from the on-premise environment to the cloud. It is a flexible platform and integrates with multiple data sources.

When this application is in use, it's utilized to extract data and observe its performance and behavior. It also helps to make adjustments to fine-tune the application.

I mainly use it to run queries, deploy servers, and make connections across data. I help clients with data warehousing and analytics.

The use case depends on different things. For example, for banking, it would be risk management, fraud, and anti-money laundering. For retail, it would be an omnichannel customer experience. For sales, it could be upsell, cross-sell, retention. It's different for every industry.

It can be used for reducing hardware expenses.

We are using Microsoft Azure Synapse Analytics with multiple customers for data integration, data migration, and data analytics. I offer these services to my customers as part of my service organization called Edge Technologies.

Customers use the solution for analytics, such as forecasting reports. There are a lot of business cases, we customized them based on the customer.

We use Synapse to build views for our data warehouse. It is the place where we get our reports and views. We ingest data into our SQL Servers and Azure SQL, then build all our reporting views within Synapse.

I'm the cloud architect, and I have a two-person DevOps team plus another enterprise architect. Five people are using the interface at any given time. 

The intention is for the company to utilize Microsoft Azure Synapse Analytics for analytics purposes. The team manages numerous databases and utilizes them to handle multiple tasks.

We have implemented it for two solutions - data warehousing and BI Synapse (Business Intelligence). We also use it for merchandising, where based on our booking patterns, Synapse is used to analyze and target customers with more products. We use it for virtualizing, and BI is for analyzing business strategies.

We primarily use it for data transformation purposes, as well as for storing data. Additionally, we use it to connect to Power BI for reporting purposes.

Our company includes 50 analysts who use the solution on a daily basis to predict customer behavior. 

I use Synapse Analytics to process data for transformation, create external tables, restrict user access, and for reporting.

We use the solution for integration to enable the data to reuse it in the power gap.

We use this solution as a data warehouse. Power BI allows us to visualize the data while analyzing data from the data lake through Serverless. We have not used too much of the pipelines within the Data Factory because we have an isolated Data Factory for that.

Our company uses the solution to provide data warehousing for customers. Most of our customers are mid-sized. 

We use Microsoft Azure Synapse Analytics in several scenarios, we require access to high-performance computing capabilities. This is where the solution proves to be a valuable asset as it offers a node-based solution for computing needs. In numerous cases, we have to undertake intricate data processing operations using the Python programming language, and that's where the solution comes in as an advantageous tool. These are my primary use cases for Synapse. Whenever we need to handle complex data engineering tasks or require significant computing power to accelerate processes, the solution provides the necessary functionality.

We are a marketing company so we help our customers with marketing strategies. The marketing is based on the final customers, and we use Microsoft Azure Synapse Analytics for CDP.

We are using the solution to process a large volume of data. We are taking advantage of the MTPP functionality feature in Azure Synapse Analytics and connecting our reporting dashboard to its dedicated support tool. These are the two primary use cases we are currently leveraging.

Our primary use case for Azure Synapse is as a data warehouse, for creation of data pipelines. It allows login into to one central workspace, manage our databases, and the entire warehouse. We can embed business intelligence (BI), using Power BI. This allows us to show visualizations all in one central place.

We use this solution to create data pipelines and to improve the self-service environment for end users. We use all the functions of Microsoft Azure Synapse Analytics.

Microsoft Azure Synapse Analytics is used for analytics.

It's used for business case analysis and bandwidth utilization, etc. 

We are using its latest version.

We primarily use the solution for our cloud data warehouse. 

I am the Associate Director of a company that is a partner of Synapse.

We use Microsoft Azure Synapse Analytics for a combination of a data warehouse with some advanced capabilities, to allow us to run some machine learning models on top of it. 

I work with Azure Synapse Analytics, Power BI, and Premier as a data administrator, using Microsoft's Azure cloud infrastructure. Azure Synapse Analytics is the principal data administration tool for our internal data warehousing, but we also use it for our customers' data warehousing and analytics in different industries. For data entry, we use Microsoft Power BI.

Internally, we have about 200 personnel currently using Azure Synapse Analytics, including data specialists, service managers, and other tech workers. It is not extensively used by all, however, because we don't often use it for very large amounts of data, except where financial data is involved.

We are using Microsoft Azure Synapse Analytics to find some evidence or outlier in our analysis about the application of the public health service and public money in the sector in Brazil. We are a public institution that is responsible for auditing other public institutions. We use these kinds of tools to find some evidence and outliers to audit with a little bit more documents or evidence.

 I'm a senior consultant.

I use this solution for client service forecasting. I work with medium-sized businesses, and I work through a virtual machine.

I mainly use Synapse Analytics to gather data, do aggregations, and generate reports.

We have been using Microsoft Azure Synapse Analytics as our proof of concept in our data warehouse. As for the analytics part, we have not used it because we do not have any SQL pools. We think it is a bit more than we need because our data is not implicated and the data sets are not very large. It is mostly been used for the detection functions and to link up the sources.

We use it for reporting purposes and for building data warehouses, pipelines, and data lakes for IoT projects.

I work in the financial industry where the most important thing is PII so the solution's data masking feature is very useful for clients. 

We're using this solution for general data integration. We are customers of Microsoft Azure Synapse Analytics and I'm a data specialist. 

I am a consultant, and when I go on-site, it's to determine how they can move to the cloud. Often, clients want to migrate because everyone else is. Others want to because they assume that they are going to save money. They will require less support staff in-house and they won't have to purchase servers.

Customers have a need to cut back on expenses, and equipment. Their servers are becoming obsolete and are required to update their licensing. Rather than doing that and creating a larger overhead, they look into Microsoft Azure. It might be a better way to go and it's less money upfront. 

Some clients have already implemented VMs. A lot of their data is stored that way. There is a difference when you move that to the cloud. 

If you share it back and forth, you have your hybrid where you have it in the cloud and on-premises. It depends on the company and what their plans are.

There is an automated tool that you can analyze your data sources and identify whether or not you can upgrade them and you can also use it for SharePoint. I've used Azure for that purpose.

In regards to data warehouses, I'm doing the upgrades from on-prem to in the cloud, or a combination of both.

We are a consulting agency and the Microsoft Azure SQL Data Warehouse is what we use for general BI reporting and visualization.

The healthcare sector is looking for how to accomplish certain processes because currently a lot of it is pure logistics. The goal is to announce and to make processes more transparent and see if they can be made more efficient. This is where business analytics comes in. The organization is using different kinds of information sources, such as electronic client dossiers or HR systems. They will see what kind of knowledge is required, how the patient or the clients are experiencing what quality of health, and create dashboards to see how the organization is doing. This will show which areas they can improve and which areas are already more efficient to make them more visible. This should allow a better understanding from the management, organization, and healthcare professional.

We use this solution to analyze big data. We collect data from different sources then we analyze that data with Synapse.

I work for a consulting firm, with many different use cases.

Synapse DW is for traditional data warehousing with a large amount of volume, that requires you to do reporting.

In terms of Synapse Analytics, I haven't put it in yet, but I'm looking at it for kind of one-stop-shop or Swiss Army Knife type projects. They've been asking for a factory or creating something that connects to the cosmos to turn it into analytics reportings, just sort of needless connecting and just making it easy from a developer standpoint to deliver on any sort of business analytics that needs to be done.

This is a data warehousing solution.

Our general use of this solution has to do with the needs of one of our clients. They had an SQL server and they were migrating from an on-premises SQL Server to Azure SQL server on the cloud. We use this solution because it fits their budget and they have some clients in other countries they want to give access to the product through Power BI.

Microsoft Azure Synapse Analytics can be used for analytics and brings together data integration.

We have implemented our data warehouse on Synapse.

We are mainly using this solution as an interface between the Data Lake and the users.

We are consultants and this product assists with the services that we offer for our clients. Our company has a large number of consultants who are working in this space.

We are an IT service provider. We develop solutions for our clients and this solution is used in some aspects of that.

We have a huge amount of data, and we need a platform where we can process the data using Spark clusters. We wanted an environment that included databases that are capable of parallel processing, and Azure Synapse was one of the best for that.

The solution is generally used as more of an Analytical Data Warehouse. It's more of an offline enterprise scale. Sometimes I've used it from a marketing perspective, as a marketing data platform. I've not really used it from a risk and the enterprise center as it tends to be more back office, and therefore less around the customer, and more around the finance procurement of HR risk and that type of thing.

We are using this product data warehousing and analytics.

We are using this solution for data warehousing and analytics.

We primarily use the solution for analytic purposes. It allows us to store data for years and then go back and look up information to learn things like how users function, react, follow trends, etc. It allows us to follow past and recent demands for products as well so that we might be able to find the reasoning behind the action or trend.

We are developing our SQL Data Warehouse locally, using SQL Server 2016 on our private cloud, and we will be migrating it to Azure once it is ready.

The company for which I am working deals with products in the leisure industry, such as museums and swimming pools. They use the data warehouse solution for big data reporting. Currently, we are developing a multi-platform data warehouse that can support the reporting requirements for all of the customers. Whatever analytics they want to perform will be done with the data warehouse, and not the SQL database application.

My role is to design the data warehouse, and once that is complete, we need to build the data pipelines so that the data can be moved from the application databases into the warehouse. Then we will design the OLAP servers and OLAP cubes, vertically, to give us a presentation layer on top of the data warehouse. This layer is what the customers will be using.

It is useful for Azure services. I am using its latest version.

I am using Microsoft Azure Synapse Analytics with Snowflake.

We are using it internally and afterward some of the outcomes are made public, either to customers or in different projects.

It's mainly for preparing internal documents when we use Word. Otherwise, we use it for externally or for a lot of PowerPoint and Excel for budget purposes. Being a small company, we don't need a very sophisticated software solution, so with an Excel spreadsheet, we can handle the different budget of different projects simultaneously.

Our primary use case is to process data and provide it to our clients.

I used it for a project thesis for my school project.

We primarily use this solution for source analytics and procurement.

We mostly provide cloud data warehousing platforms for major banks in Canada. What we're trying to do is to create a standard platform environment that is compliant with the regulatory requirements imposed by the government and financial overseeing institutions for the banks. We help them to onboard the lines of business to these platforms and migrate the existing workloads to the cloud platforms.

The primary use case is for the fast retrieval of data, which is in the data warehouse on-premises and migrating it to the cloud.

My primary use case of this solution is as a data warehouse.

We're using this solution for analytics and its modeling capabilities. It allows you to have your visualization on top of your synapse analytics. We are partners with Microsoft. 

We are service providers for our clients.

My clients primarily come from the enterprise segments, such as manufacturing, retail, pharmaceutical, and industrial. Most are supported by a large ERP solution like SAP.

Some of our clients are mid-size and others are large enterprise companies, some of the largest in India.

We use Microsoft Azure Synapse Analytics for analytics.

Our general use case is for government purposes, and therefore, I really can't get into too much detail about it.

We are a software development company and this is one of the solutions that we recommend to our clients. The applications that we develop make use of this technology.

We use the solution on our servers for analytics.

We are a solution provider and we provide different tools for our clients. Microsoft Azure SQL Data Warehouse is one of the solutions that we have implemented.

The use cases vary depending on the client. We have a couple of clients who are moving their data from on-premises to Azure databases, and they are using this solution primarily for reporting analytics. They also use visualization tools on top of the reporting.

There are around 10 people using this solution within our organization and we have plans to increase the usage. 

We use this solution for data warehousing. Specifically, we have an ERP that does not have a direct connection to our Power BI solution. This connection will exist in the future, but in the meantime, we use an ETL process to transport data out of the ERP into Azure SQL, and then from Azure SQL into Power BI. At that point, we can use the dashboard to display our information.

Our environment is a private cloud.

Our primary use case is for gathering data and analytics. We provide insights into vehicle data. We gather millions of records per second and we have various millions of vehicles running across. 

It is for business intelligence. We are gathering all the data from legacy systems and transforming the data. We are then pushing the data back towards Power BI.

We use the private deployment model of this solution.