Azure Data Science Virtual Machines (DSVM) vs. Looker Studio

Azure DSVM is useful in [a] Machine Learning environment where GPU-based processing is [required]. [The] most relevant [users] for the Azure DSVM is in ML/AI for model training and processing [high-end] CPU tasks with GPU compatibility. Azure DSVM is built for [a] startup to low medium IT environments where the ML/AI-based projects are [carried] out.

Looker Studio is well-suited for those wanting to analyze web/site data and performance quickly. It is simple enough to learn/use for quick report-building or drilling into data. Looker Studio is easier to use/understand than the GA4 console and thus has a better UI/UX. It is an efficient tool for fast, simple data needs—especially for team members with limited analytical capabilities and knowledge.

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• Leveraging data.
• Computer vision.
• Data science.

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• Self-service
• Easy to use, point and click
• Little to no training required
• Easy to share internally and externally
• Rich visualizations
• Canned reports
• Easy to copy/paste/dupe existing reports
• Ability to join data sets
• Easy integration with various data sources
• Flexible data integrations, including lowest common denominator (CSV, XLS, G-Sheets)
• Wide range of APIs
• Secure / authentication via Google SSO
• Easy to share / re-assign ownership of reports and data sources

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• Azure DSVM pricing must be reduced so that an AI-based start-up can use the Azure DSVM.
• Azure must create an environment to use Azure DSVM offline as well.
• Lack of frameworks

• Few functionalities are very exclusive only for data studio.
• It's time taking to load data and at the same time only single Data source can be connected.
• When editing the reports you have to switch between Edit and View mode to see how does the change looks like.

It is the simplest and least expensive way for us to automate our reporting at this time. I like the ability to customize literally everything about each report, and the ability to send out reports automatically in emails. The only issue we have been having recently is a technical glitch in the automatic email report. Sadly, there is almost no support for this tool from Google, but is also free, so that is important to take into consideration

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It is not ideal and requires time and dedication to understand how to work with it. Also, it has a lot of limitations around data it can accept. But in most cases, this tool is sufficient for everyday tasks of product and marketing departments. I wouldn't say that the interface is very user-friendly, but for people who regularly work with analytical tools, it must be ok.

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I give it a lower support rating because it seems like our Dev team hasn't gotten the support they need to set up our database to connect. Seems like we hit a roadblock and the project got put on pause for dev. That sucks for me because it is harder to get the dev team to focus on it if they don't get the help they need to set it up.

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It's within the Azure environment and it's easy to manage.

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The free version of Looker Studio is still better than the leading enterprise-embedded BI tools, despite its weaknesses. The leading embedded BI platforms have terrible visualizations that can be spotted a mile away. They are also primarily locked to a grid, making it very hard to fully customize. The price point is also a major deterrent, since users end up paying for lots of features they might never use. Looker Studio has weaknesses on the blending and modeling side, but we've been able to get by via connection to GBQ and transformation done in dbt.

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• Azure DSVM is little costly with long term support for ML based environments.
• Azure DSVM is very good for short tasking and costs us [a] little low than the on-prem server.
• [Scaling] option is very convenient.

• Free, so the only investment is time
• Because it doesn't have native support of non-Google sources, it can cost more money than Tableau
• The time spent formatting the templates or building connectors can have a negative impact on ROI
• As a agency, charging for the reporting service is profitable after the first month or two after building the dashboard.

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