Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
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Tableau Cloud
Score 8.0 out of 10
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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.
$15
per month per user
Tableau Server
Score 7.7 out of 10
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Tableau Server allows Tableau Desktop users to publish dashboards to a central server to be shared across their organizations. The product is designed to facilitate collaboration across the organization. It can be deployed on a server in the data center, or it can be deployed on a public cloud.
Tableau Desktop is great because it has much more extensive capabilities. Tableau Prep is great for ETL. It makes it easy to aggregate multiple data sources, union, clean, etc. It is easy to QA within Prep, and takes a lot of the guesswork out of troubleshooting issues with …
Tableau Online is much better at presenting and visualizing and manipulating your data. While Host Analytics is second to none in data consolidation, Tableau has much greater flexibility in exploring that data.
Both Tableau Online and BI solutions provide visualizations. In Power BI we choose the visualization first, then drag the data into it. In Tableau, we select the data and switch between visualizations on the fly. It’s easier to jump between visualizations in Tableau. Power BI …
Verified User
Analyst
Chose Tableau Cloud
From an analyst point of view, Tableau is the most intuitive tool and it's really easy to use. It's simply the most convenient product and gives the biggest possibilities. Of course, it's more expensive and not all features are necessary for some users. I have chosen Tableau …
The choice to use Tableau Server is really made for you if you already have adopted Tableau Desktop. If you're focused on an on-premise solution, Tableau is probably the way that you'll have to go. Looker and Mode are cloud-based (so is Tableau Online) and offer a true …
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.
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.
Whole funnel and specific channel performance from upper to lower funnel metrics. The ability to view full channel performance for some time, such as weekly, monthly, or quarterly, has truly been monumental in how my team optimizes specific channels and campaigns. Daily performance tracking is a bit overwhelming, with load times and having to refresh specific live views over time. It can be challenging to do so at times, as extensive dashboards take much longer to load.
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.
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.
It's good at doing what it is designed for: accessing visualizations without having to download and open a workbook in Tableau Desktop. The latter would be a very inefficient method for sharing our metrics, so I am glad that we have Tableau Server to serve this function.
Publishing to Tableau Server is quick and easy. Just a few clicks from Tableau Desktop and a few seconds of publishing through an average speed network, and the new visualizations are live!
Seeing details on who has viewed the visualization and when. This is something particularly useful to me for trying to drive adoption of some new pages, so I really appreciate the granularity provided in Tableau Server
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.
Tableau Server has had some issue handling some of our larger data sets. Our extract refreshes fail intermittently with no obvious error that we can fix
Tableau Server has been hard to work with before they launched their new Rest API, which is also a little tricky to work with
It simply is used all the time by more and more people. Migrating to something else would involve lots of work and lots of training. The renewal fee being fair, it simply isn't worth migrating to a different tool for now.
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
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.
Tableau Server takes training and experience in order to unlock the application's full potential. This is best handled by a qualified data scientist or data analytics manager. Tableau user interface layout, nomenclature, and command structure take time and training to become proficient with. Integration and connectivity require proper IT developer support.
Our instance of Tableau Server was hosted on premises (I believe all instances are) so if there were any outages it was normally due to scheduled maintenance on our end. If the Tableau server ever went down, a quick restart solved most issues
While there are definitely cases where a user can do things that will make a particular worksheet or dashboard run slowly, overall the performance is extremely fast. The user experience of exploratory analysis particularly shines, there's nothing out there with the polish of Tableau.
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.
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.
We have consistently had highly satisfactory results every time we've reached out for help. Our contractor, used for Tableau server maintenance and dashboard development is very technically skilled. When he hits a roadblock on how to do something with Tableau, the support staff have provided timely and useful guidance. He frequently compares it to Cognos and says that while Cognos has capabilities Tableau doesn't, the bottom line value for us is a no-brainer
In our case, they hired a private third party consultant to train our dept. It was extremely boring and felt like it dragged on. Everything I learned was self taught so I was not really paying attention. But I do think that you can easily spend a week on the tool and go over every nook and cranny. We only had the consultant in for a day or two.
The Tableau website is full of videos that you can follow at your own pace. As a very small company with a Tableau install, access to these free resources was incredibly useful to allowing me to implement Tableau to its potential in a reasonable and proportionate manner.
Implementation was over the phone with the vendor, and did not go particularly well. Again, think this was our fault as our integration and IT oversight was poor, and we made errors. Would they have happened had a vendor been onsite? Not sure, probably not, but we probably wouldn't have paid for that either
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.
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.
Today, if my shop is largely Microsoft-centric, I would be hard pressed to choose a product other than Power BI. Tableau was the visualization leader for years, but Microsoft has caught up with them in many areas, and surpassed them in some. Its ability to source, transform, and model data is superior to Tableau. Tableau still has the lead in some visualizations, but Power BI's rise is evidenced by its ever-increasing position in the leadership section of the Gartner Magic Quadrant.
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.
Tableau does take dedicated FTE to create and analyze the data. It's too complex (and powerful) a product not to have someone dedicated to developing with it.
There are some significant setup for the server product.
Once sever setup is complete, it's largely "fire and forget" until an update is necessary. The server update process is cumbersome.
