A lot of integration is my use case. We move data around and our teams that we support write integration packages to move data from one source to another source or to change data as needed.

Moving data makes things so much easier. We have a lot of data movement, and we create many data marts from sources, which allows us to create data marts very easily.

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I work with all of them as a Microsoft Certified Trainer, particularly around the Business Intelligence SQL space. I tend to do quite a bit of work around the SSAS space, SSIS, SSRS, and integrating that into a lot of SharePoint and other spaces.

The challenge is building from the KPIs. Being able to put your KPIs in place starting with understanding a board of directors and their needs, and being able to build the KPIs for an organization from that level is important. Then being able to deliver those on a dashboard, such as Power BI, or something else, also matters. Additionally, being able to build cubes that allow for rapid analysis over a long period of time is crucial. The one thing which is missing often enough is building out your data warehouse with your data models, so helping to build the data models is very important. I appreciate how it handles large data in your warehouse.

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My main use case for SSIS is reporting. I pull reports from warehouse management and various entities to ensure that everything aligns with the reports.

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I primarily use SSIS to create and fix packages in Integration Services on-premises. I use it for creating data pipelines, moving data from on-premises to the cloud, and ensuring the data is available for Power BI reports. View full review »

We use SSIS for data extraction, transformation, and loading processes. This includes extracting data from various sources like Oracle and SQL Server, and then transforming it for use in reporting tools like Power BI dashboards. While we haven't directly migrated data between databases using SSIS, we have used it extensively for ETL tasks, such as extracting data for analysis and reporting purposes.

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We use the solution for ETL. It helps to move data from one server to another and translate it in the middle. 

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I use SSIS for data loading ETL processes. I am responsible for the EDW layer and get new data from other data sources into the EDW via SSIS.

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I use the company's solution to build dashboards and back-end data integration with Microsoft SQL Server and Power BI.

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Presently, we have decommissioned the solution. Earlier, we used SSIS with the SQL Server.

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SSIS (SQL Server Integration Services) is primarily an ETL tool. It's frequently used for data integration and transformation. In the past, we used it to integrate data into our systems. Microsoft is now focusing on Azure Data Factory for cloud solutions, but SSIS is still a viable on-premises option.

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We have a consolidated data warehouse. We use an SSIS tool as a pipeline to fetch the data from multiple sources such as Excel, CSV, Oracle database, or APIs. Further, we put the data into the SSIS database. We are using the solution for ETL purposes.

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We primarily use the solution for getting the data from the application database and to the OLAP system. We extract the data from OLAP system and build data warehouses. Basically, this solution is used to build OLAP.

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The use case is related to all reporting and transactions. On an hourly basis, we will pull the data for a few reports. Most of the time, it is a real-time integration between the applications.

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Mostly we are receiving files in either XML or Excel and then we collect them in databases. Sometimes it's CSV and sometimes it is also Excel. Mostly, it is XML.

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We use SSIS to copy production data to Reporting Repositories. We use it for data movement from and to SQL or flat files such as CSV or fixed data.  This is primarily to build and update a data lake and data warehouse. 

More custom use cases are to do DQ validation comparing source to destination, and to extract data from bat files and SSIS files in order to build a metadata repository for documenting and mapping all the ETL packages.  We use this to find the packages responsible for any particular table or file, or vice versa.  This system, along with statistics logging, also gives us summary reporting capability.

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It was used for a couple of purposes, for example, we used it in our data warehouse when we wanted to create a dashboard that would give us an idea about, for example, the data itself. We had a client that would ask about a couple of requirements, like the past data from the last month or last year for a particular region for a particular client, and so on. I was using SSIS to store data incrementally and show the data using SSIS. 

For example, if we have created a couple of tasks or schedules, we would create a couple of packages and schedule them overnight. Every night it could fetch the data incrementally from the data warehouse. Every week, there was a requirement from the client that I need to add particular data to be changed and shared over email weekly. That particular data could be fetched from the data warehouse, and it will be shared with the client every week. 

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I am using SSIS for a primary database.

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We're SSIS for flat file data ingestion. Our data sources are Excel files, but if the data sources are SQL servers, I use store procedures instead of SSIS packages.

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One of our primary use cases is working with issues related to the electronic components of cars.

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We use the product sometimes to implement some ETL processes.

We have to transform some data from students so that we can help to make some decisions on what we call deterioration. We receive a lot of data from the government, and sometimes we have to split it into several parts as it is a huge amount of data. We have to put it in a standardized way. We use the solution as a product. It is one of the use cases that we do. When we have other systems, like for staff to control, we also use SSIS since it's appropriate to our system, and we need to put it in certain ways so that we can read it and manage it. 

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We use this solution as an ETL tool to move large volumes of data from one server to another.

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We are using SSIS for applications in development and in production. We have applications that need to have a database to support them and those applications need to have different integration with other applications.

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The one internally also developed some applications for some specific problems. For that use case, we are using the solution for different purposes, for example, for integration between data disks to databases, as well as for the application level side of integration. We are using the SSIS service actually. And we also have different expertise on the database level. We're also using it for different scenarios. We use it with Microsoft products. It's a part of our strategy, in the data aspect.

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We have some business requirements, and we use this solution to create the machine tables, measures and APIs.

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Our company sells contracts when you buy a car. We sell aftermarket insurance for the tyre, wheel, ding, dent, windshield, etc. When somebody buys a contract, we capture all of that data into a legacy database PostgreSQL, and my task is to incorporate that into our financial platform using T-SQL. So, I write queries, procedures, and views. I use SSIS, and I use SSRS. My job is to get the data into our financial system so that we can process claims, payments, cancellations, and refunds.

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The solution is primarily used for the ETL process. Another use case is transforming data from one format to another format.

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I was using the solution to implement some business logic. What we do with the data collected is, for example, use it with Excel and some other databases. We do write our own logic to input the data into the SQL server. We'll be taking inputs and putting them into our business logic.

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SSIS orchestrates data transformation in the environment, which includes Oracle and SQL Server databases.

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The solution is a business application. It's used for integrations between different databases, migrating from one to one, and importing data from one thing to something else.

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SSIS is mainly used for data migration transfer.

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We are using SSIS for ETL and ELT in a business intelligence context. We bring in data from different sources and use SSIS transform and load it into our intermediate database where we are developing dashboards. There are three people doing configuration and about 50 end-users. 

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SSIS is helpful when you have tasks in a particular workflow that need to be completed on different schedules. You can start a specific task, and SSIS goes through the workflow to check a few things before it decides whether the task needs to proceed. Right now, maybe a quarter of the company is using it, but we plan to expand use in the future. 

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This is an infrastructure tool and our primary use case is for data warehouse integration.

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We use SSIS to integrate investment data we collect from different places into our portfolio management system. We get results out of portfolio management systems and integrate that into the downstream back-office, risk, and regulatory reporting systems. All the developers at my company use it. 

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We have data that needs to be migrated. There is also a scan inventory. We create web data, pull it, search it, and then find answers and report stakeholders. So for this process, we use the SSIS.

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We are using SSIS for all of our relational database management systems (RDBMS) data.

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We use SSIS for transformation and migration.

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I have used this solution for integrating SQL into life insurance projects.

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We use SSIS to migrate from an old server to a new one and to add some services. 

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I am an ETL developer working as an information zone leader. We are an outsourcing company to our customers.

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We are a solution provider and SSIS is one of the products that we implement for our clients. I work as an integrator and a data flow developer.

SSIS is primarily used as part of the data flow for loading data into the data warehouse and exchanging data between applications.

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We primarily use SSIS to collect data from one system and transfer it to another.

The Classical ETL processing of data.

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We have used SSIS in many ways. Primarily, it has been used for building ETLs for populating data warehouse and staging areas. We have developed a number of data marts that were populated. We build data migration packages, which have been reused a number of times with minimal configurations. Additionally, we build complex data integrations solutions and data hand-offs between different applications. We have even used it for creating and parsing SWIFT messages for data integration purposes. We also used it for email triggers. 

Now SSIS has introduced Big Data and cloud components. Though we haven't used any of these yet but it's really a very good addition to enhance SSIS capabilities.

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We are using it in our company and for our clients. I have experience in working with the whole data cycle, which includes data collection, transformation, and visualization. I have worked with the end-to-end process, and I have handled data integration, analysis, and visualization.

I specialize in Microsoft tools, and I have used SSIS for data integration and Power BI for data visualization. I have also worked with Tableau for data visualization and Talend for data integration.

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We are a software development company and implement solutions like SQL Server SSIS for our clients. We do not use this product ourselves in the company but have experience with it because our client asked for it.

Our clients use it to transform data and generate reports. The use cases are generally simple, and not advanced or complex.

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We use this solution to ingest data to a data lake that is built on SQL Server.

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Our primary use for this solution is to move data between points and applications.

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We use this solution for extracting data from various databases and saving it in our data warehouse.

We use the on-premise deployment model.

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I'm using the solution primarily for e-memory and e-database calculation, mostly data blending (ETL) and massive table joining, etc. I also use it for data preparation and modeling.

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We primarily use the solution for integration packages for ETL in order to build data warehouses.

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We use the on-prem version of this solution. Our primary use case of this solution is for integrated data on the SQL server. 

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We've used the solution to create some data flows for one of the governmental sectors here in Saudi Arabia. I have created some applications for exporting data from Oracle databases to SQL databases.

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Our primary use is as an ETL tool to move data across our various environments.

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We use this solution for data warehousing.

We are using the on-premise deployment model.

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We primarily use the solution for data transformation.

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We use this solution for ETL, which includes data summation and cleaning. 

This solution used in an on-premise deployment, for now.

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I use this solution to create BI reports. I have used SSIS in more than twenty projects over the past six years.

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The primary use case for this solution varies according to the customer, but it typically involves moving data from OLTP systems into a data warehouse and/or data marts.

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Movement of data and creation of files. ALl the typical things that you would have a ETL solution do. Data movements were in the millions and no calculations were completed. This means it was always a select * from where ever it was coming from and going to. Light translations like concatenation was being used. 

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SSIS is a very flexible solution that allows data to be generated through code or external software. As a result, we can create reproducible patterns and improve code quality.

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