Amazon EMR (Elastic MapReduce) vs. Tableau Cloud

We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.

Incentivized

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.

Incentivized

• EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
• EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
• EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.

Incentivized

• 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.

Incentivized

• It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
• Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
• If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.

Incentivized

• Can be a steep learning curve for new users
• Modeling and building algorithms aren't always intuitive and take some testing/retesting to ensure it's working as it should
• Inability to integrate easily with our HRIS platform. Reports are pulled from HRIS at various intervals and uploaded into Tableau

Incentivized

Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations

Incentivized

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.

Incentivized

I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.

Incentivized

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.

Incentivized

Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.

Incentivized

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.

Incentivized

• It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
• It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
• It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.

Incentivized

• When we release new products, we are now able to quickly see data and toggle between current periods and previous to see performance
• Generating new reports requires less IT time to build
• Data can be shared across many different device types
• We now have integration where our customers can extract data from our software more easily-this was a big ask from our customers

Incentivized