Tableau Cloud (formerly Tableau Online) is a self-service analytics platform that is fully hosted in the cloud. Tableau Cloud enables users to publish dashboards and invite colleagues to explore hidden opportunities with interactive visualizations and accurate data, from any browser or mobile device.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
If you're using Tableau as the primary BI tool, then Tableau Cloud is well suited to publish and share the results with a wide(r) audience. It is well suited for various degrees of self-service proficiency, from pure consumers of analytical work to more advanced users who can use web editing for smaller or larger adjustments, and even for desktop power users who will publish their work to Tableau Cloud. It has many good ways to organize the content and make it easily accessible via search, favorites, folders, collections ("playlists for your data"), or history ("recents"). It might not be ideally suited if there are many on-prem sources to be used (even though there are options to connect them) or if you have very special requirements regarding custom server setup, which is limited in a shared cloud environment like Tableau Cloud.
Tableau Online is completely cloud based and that's why the reports and dashboards are accessible even on the go. One doesn't always need to access the office laptop to access the reports.
The visualizations are interactive and one can quickly change the level at which they want to view the information. For example, one person might be more interested in looking at the country level performances rather than client level. This is intuitive and one doesn't need to create multiple reports for the same.
The feature to ask questions in plain vanilla English language is great and helpful. For quick adhoc fact checks one can simply type what they are looking for and the Natural Language Programming algorithms under the hood parse the query, interpret it and then fetch the results accordingly in a visual form.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
Based on comments from our clients, I awarded it this grade. Non-technical customers frequently compliment us on the ease with which they can utilize Tableau Online. Usability is rarely a source of contention amongst our customers. Few complaints have come from me as a user of our internal products.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
I have not had any issues that require customer support from Tableau at this time, which speaks well to Tableau. I have taken an online course with Tableau and it was very professional and well done, so based on that I would assume a similar level of quality for their customer service.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
In determining whether to go with Tableau Online versus Alteryx, two important factors stood out in determining our go-to solution. First, while Alteryx is an impressive tool for data cleansing, it did not stack up in terms of data visualization capabilities. Tableau, on the other hand, provided us everything we needed in terms of visualizing our data and analytics. The second factor is cost. Well neither solution would be considered cheap, Tableau was the more cost effective solution for our needs.
