SnapLogic vs Snowflake

• Their customer service response time is brilliant.
• SFTP tasks do a great job in reading the file directly from the server, instead of having to download it and then read it.
• Tasks expose the ability to run them using external job schedulers.
• Once you have a server up, setting up SnapLogic service does not take long. They have good documentation and customer support too.

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• This is a cloud-based application that provides me a secure platform for the easy sharing of data with my clients.
• I feel confident to connect with my numerous business partners just because of its advanced security and effective programming.
• Through this tool, I am capable of integrating, analyzing, and the entire data in a precise form and with analytics as well, due to which I always have a transparent idea about the data extraction and its transfer from multiple clouds.

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• The installation process is vague, and our customers have issues with using Java for security reasons. If they had an install package that could fix that it would be less of an issue.
• They could make it more convenient to use on the web browser, if I was working on a customer in the Manager I don't want to have to navigate to keep going back to that.

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• Snowflake UI can be clunky and breaks sometimes, which can be annoying
• Snowflake has to be paired with the Data Build Tool (DBT) to allow for templating and macro usage. No inbuilt solution.
• Snowflake Python connector development doesn't necessarily track popular packages such as Pandas as quickly as Pandas releases
• Could do with better machine learning capability over warehouse tables, but I assume this is coming soon

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• SnapLogic cannot solve business problems and you still need a technical person to solve the complex integration issues.
• Our business bought SnapLogic thinking this could replace our current ETL tools such as Informatica. But SnapLogic falls short in a lot of features when compared to Informatica. Later our business decided not to replace Informatica.
• SnapLogic promised to solve big data integration problems, but we did not find a suitable solution that can solve our problems of data integration using SnapLogic (for instance, it could not parse parquet nested file).

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• Faster data retrieval makes real time analysis possible.
• Option to create multiple warehouses enable us to track usages and cost by departments.
• Option to scale up/down computational warehouse to support peak hours and month end loads makes Snowflake cost effective.
• Zero-copy cloning reduces storage and deployment cost.
• Multiple users can access the same amount of data without deadlocks which is a common scenario in relational databases.

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