I am not using Cognosys SQL Server currently, but in my past project, I used it for approximately one to 1.5 years.In that project, we had several applications, and we used Cognosys SQL Server to load the operational and transactional data into this database. After that, we had a Snowflake layer as well, a Snowflake database. From Cognosys SQL Server, we pushed the data to our Snowflake database for the analytics use case. Technically, we used Cognosys SQL Server only to store the operational data and the transactional data, not for the analytics. Because it is quite difficult to integrate Snowflake with our application due to some compatibility issues, the application we were using was not compatible with the Snowflake database. For that reason, we had to adopt a SQL Server database. Previously we evaluated Microsoft SQL Server as well, but the initial setup takes a lot of time, which is why we chose Cognosys SQL Server at that time. Once the operational and transactional data was stored in Cognosys SQL Server, we performed the test cases on top of that. Once that was done, we pushed the data to Snowflake database for the analytics purpose. The integration was quite good because we did not face any issue integrating Cognosys SQL Server with Snowflake database. That was simply done by the APIs. Once the data was available in Snowflake database, we had some downstream applications or downstream reporting as well, which we built on Power BI, and that we used to showcase the data. On Cognosys SQL Server side, because in the previous project we had six to seven applications, before adopting Cognosys SQL Server, we fetched the data from the application and the transactional data. We maintained Excel reports and maintained the data in Excel, and then we pushed that data into Snowflake database. It was quite a complex task and a manual task. For that reason, we adopted Cognosys SQL Server to integrate all the applications, all the six to seven applications, with a single database only. There we could perform all our database operations before pushing the clean data into Snowflake. One use case I already provided is as follows: when we did not have Cognosys SQL Server, we were fetching the transactional data directly from the application and doing the transformation and cleaning the data in Excel itself. Once our final data was ready in Excel, we loaded that Excel sheet or Excel data into Snowflake database. That work was fully manual, and we had many chances of error in that manual work. After adopting Cognosys SQL Server, we created a data pipeline in SSIS which pulls the data from the application and loads the data into Cognosys SQL Server. Then and there, we performed the transformation by using the stored procedure or by using the views. The chances of error have been reduced significantly. We were saving a lot of time because previously, for one of the Excel sheets, we were spending one and a half days to transform the data. After adopting Cognosys SQL Server, the transformation and the ETL part could be completed in 15-20 minutes. We used it as a database only. We were fetching the data from the application using the SSIS tool and doing the transformation. At that time, we did not explore much of the capability or the AI capability of it. However, if they have really implemented some of the AI capability, then I would like to explore it more. Currently, I do not have anything to share regarding the AI capabilities.
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I am not using Cognosys SQL Server currently, but in my past project, I used it for approximately one to 1.5 years.In that project, we had several applications, and we used Cognosys SQL Server to load the operational and transactional data into this database. After that, we had a Snowflake layer as well, a Snowflake database. From Cognosys SQL Server, we pushed the data to our Snowflake database for the analytics use case. Technically, we used Cognosys SQL Server only to store the operational data and the transactional data, not for the analytics. Because it is quite difficult to integrate Snowflake with our application due to some compatibility issues, the application we were using was not compatible with the Snowflake database. For that reason, we had to adopt a SQL Server database. Previously we evaluated Microsoft SQL Server as well, but the initial setup takes a lot of time, which is why we chose Cognosys SQL Server at that time. Once the operational and transactional data was stored in Cognosys SQL Server, we performed the test cases on top of that. Once that was done, we pushed the data to Snowflake database for the analytics purpose. The integration was quite good because we did not face any issue integrating Cognosys SQL Server with Snowflake database. That was simply done by the APIs. Once the data was available in Snowflake database, we had some downstream applications or downstream reporting as well, which we built on Power BI, and that we used to showcase the data. On Cognosys SQL Server side, because in the previous project we had six to seven applications, before adopting Cognosys SQL Server, we fetched the data from the application and the transactional data. We maintained Excel reports and maintained the data in Excel, and then we pushed that data into Snowflake database. It was quite a complex task and a manual task. For that reason, we adopted Cognosys SQL Server to integrate all the applications, all the six to seven applications, with a single database only. There we could perform all our database operations before pushing the clean data into Snowflake. One use case I already provided is as follows: when we did not have Cognosys SQL Server, we were fetching the transactional data directly from the application and doing the transformation and cleaning the data in Excel itself. Once our final data was ready in Excel, we loaded that Excel sheet or Excel data into Snowflake database. That work was fully manual, and we had many chances of error in that manual work. After adopting Cognosys SQL Server, we created a data pipeline in SSIS which pulls the data from the application and loads the data into Cognosys SQL Server. Then and there, we performed the transformation by using the stored procedure or by using the views. The chances of error have been reduced significantly. We were saving a lot of time because previously, for one of the Excel sheets, we were spending one and a half days to transform the data. After adopting Cognosys SQL Server, the transformation and the ETL part could be completed in 15-20 minutes. We used it as a database only. We were fetching the data from the application using the SSIS tool and doing the transformation. At that time, we did not explore much of the capability or the AI capability of it. However, if they have really implemented some of the AI capability, then I would like to explore it more. Currently, I do not have anything to share regarding the AI capabilities.