Adverity is particularly useful if there is a large range of data sets that you want to combine to get an 'overall' view. Previously we had used Google Analytics, but found that this was too useful for our big client accounts that we were working on. So I think that if there is an individual who is responsible for analytics or data, and also a paid media team then this is a tool which is essential. For companies that have limited activity then I think this tool could potentially over-complicate for less reward.
Ideal for daily standard ETL use cases whether the data is sourced from / transferred to the native connectors (like SQL Server) or FTP. Best if the company uses MS suite of tools. There are better options in the market for chaining tasks where you want a custom flow of executions depending on the outcome of each process or if you want advanced functionality like API connections, etc.
I think it would have been more user friendly if there was more labelling capabilities, so that when you are sharing the dashboard they would explain what the data is - rather than depending on someone knowing how to use the dashboards or being there to explain it.
Our teams found that some of the scheduling functionality could be a little big buggy, so this was something that needed extra care and attention, so therefore may be an area that needs to be improved (unless it was just our account and usage).
SSIS has been a bit neglected by Microsoft and new features are slow in coming.
When importing data from flat files and Excel workbooks, changes in the data structure will cause the extracts to fail. Workarounds do exist but are not easily implemented. If your source data structure does not change or rarely changes, this negative is relatively insignificant.
While add-on third-party SSIS tools exist, there are only a small number of vendors actively supporting SSIS and license fees for production server use can be significant especially in highly-scaled environments.
Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
SQL Server Integration Services is a relatively nice tool but is simply not the ETL for a global, large-scale organization. With developing requirements such as NoSQL data, cloud-based tools, and extraordinarily large databases, SSIS is no longer our tool of choice.
Raw performance is great. At times, depending on the machine you are using for development, the IDE can have issues. Deploying projects is very easy and the tool set they give you to monitor jobs out of the box is decent. If you do very much with it you will have to write into your projects performance tracking though.
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
I had nothing to do with the choice or install. I assume it was made because it's easy to integrate with our SQL Server environment and free. I'm not sure of any other enterprise level solution that would solve this problem, but I would likely have approached it with traditional scripting. Comparably free, but my own familiarity with trad scripts would be my final deciding factor. Perhaps with some further training on SSIS I would have a different answer.
This tool has allowed us to be able to see areas for opportunities, when all of the data has been combined
We were also able to better prioritise actions and where to focus our attention, when we had one view where all of the data was together. This allowed us to deliver better results for our clients, and also have a clearer roadmap of activity
