Adverity is a fully-integrated data platform for automating the connectivity, transformation, governance & utilization of data at scale.
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Azure Data Factory
Score 8.6 out of 10
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Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.
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.
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
We can use linked service in multiple pipeline/data load.
It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
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).
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
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
