Apache Spark vs. Tableau Desktop

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Tableau Desktop
Score 8.3 out of 10
N/A
Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
$70
per month
Pricing
Apache SparkTableau Desktop
Editions & Modules
No answers on this topic
Tableau Creator
$70.00
Per User / Per Month
Offerings
Pricing Offerings
Apache SparkTableau Desktop
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsAll pricing plans are billed annually.
More Pricing Information
Community Pulse
Apache SparkTableau Desktop
Considered Both Products
Apache Spark
Chose Apache Spark
Apache Spark is a fast-processing in-memory computing framework. It is 10 times faster than Apache Hadoop. Earlier we were using Apache Hadoop for processing data on the disk but now we are shifted to Apache Spark because of its in-memory computation capability. Also in SAP …
Chose Apache Spark
How does Apache Spark perform against competing tools? I think Apache Spark does well in processing large volumes of data. The machine learning models also seem to be easier to program and interpret. With that said, the programming side of Apache Spark seems more difficult …
Tableau Desktop
Chose Tableau Desktop
If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via PyCharm, Atom, and Visual Studio IDE. These IDEs cannot directly help you to visualize the data but you can use many python packages to do so …
Top Pros
Top Cons
Features
Apache SparkTableau Desktop
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.5
166 Ratings
4% above category average
Pixel Perfect reports00 Ratings8.3138 Ratings
Customizable dashboards00 Ratings8.9165 Ratings
Report Formatting Templates00 Ratings8.2144 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.8
163 Ratings
8% above category average
Drill-down analysis00 Ratings9.1158 Ratings
Formatting capabilities00 Ratings8.8161 Ratings
Integration with R or other statistical packages00 Ratings8.2121 Ratings
Report sharing and collaboration00 Ratings9.2156 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.7
157 Ratings
4% above category average
Publish to Web00 Ratings9.2148 Ratings
Publish to PDF00 Ratings8.4148 Ratings
Report Versioning00 Ratings8.6115 Ratings
Report Delivery Scheduling00 Ratings9.1122 Ratings
Delivery to Remote Servers00 Ratings8.572 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.6
155 Ratings
6% above category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings8.9153 Ratings
Location Analytics / Geographic Visualization00 Ratings8.8148 Ratings
Predictive Analytics00 Ratings8.7125 Ratings
Pattern Recognition and Data Mining00 Ratings8.02 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.7
141 Ratings
1% above category average
Multi-User Support (named login)00 Ratings8.7138 Ratings
Role-Based Security Model00 Ratings8.4118 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings8.6128 Ratings
Report-Level Access Control00 Ratings9.02 Ratings
Single Sign-On (SSO)00 Ratings8.976 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.4
134 Ratings
5% above category average
Responsive Design for Web Access00 Ratings8.5123 Ratings
Mobile Application00 Ratings8.396 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings8.7116 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Apache Spark
-
Ratings
Tableau Desktop
8.5
63 Ratings
7% above category average
REST API00 Ratings8.455 Ratings
Javascript API00 Ratings8.250 Ratings
iFrames00 Ratings8.648 Ratings
Java API00 Ratings8.645 Ratings
Themeable User Interface (UI)00 Ratings8.452 Ratings
Customizable Platform (Open Source)00 Ratings8.745 Ratings
Best Alternatives
Apache SparkTableau Desktop
Small Businesses

No answers on this topic

BrightGauge
BrightGauge
Score 8.9 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
TIBCO Jaspersoft Community Edition
TIBCO Jaspersoft Community Edition
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
TIBCO Jaspersoft Community Edition
TIBCO Jaspersoft Community Edition
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkTableau Desktop
Likelihood to Recommend
9.9
(24 ratings)
9.0
(193 ratings)
Likelihood to Renew
10.0
(1 ratings)
8.9
(39 ratings)
Usability
10.0
(3 ratings)
8.6
(63 ratings)
Availability
-
(0 ratings)
8.0
(10 ratings)
Performance
-
(0 ratings)
6.1
(9 ratings)
Support Rating
8.7
(4 ratings)
6.9
(56 ratings)
In-Person Training
-
(0 ratings)
9.4
(4 ratings)
Online Training
-
(0 ratings)
8.0
(4 ratings)
Implementation Rating
-
(0 ratings)
8.0
(34 ratings)
Configurability
-
(0 ratings)
8.1
(2 ratings)
Ease of integration
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
7.0
(3 ratings)
Vendor post-sale
-
(0 ratings)
10.0
(1 ratings)
Vendor pre-sale
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Apache SparkTableau Desktop
Likelihood to Recommend
Apache
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.
Read full review
Tableau
Tableau Desktop is one the finest tool available in the market with such a wide range of capabilities in its suite that makes it easy to generate insights. Further, if optimally designed, then its reports are fairly simple to understand, yet capable enough to make changes at the required levels. One can create a variety of visualizations as required by the business or the clients. The data pipelines in the backend are very robust. The tableau desktop also provides options to develop the reports in developer mode, which is one of the finest features to embed and execute even the most complex possible logic. It's easier to operate, simple to navigate, and fluent to understand by the users.
Read full review
Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
Read full review
Tableau
  • An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
  • Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
Read full review
Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
Read full review
Tableau
  • Formatting the data to work correctly in graphical presentations can be time consuming
  • Daily data extracts can run slowly depending on how much data is required and the source of the data
  • The desktop version is required for advanced functionality, editing on [the] Tableau server allows only limited features
Read full review
Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
Read full review
Tableau
Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
Read full review
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Tableau
Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
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Reliability and Availability
Apache
No answers on this topic
Tableau
When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
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Performance
Apache
No answers on this topic
Tableau
Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
Read full review
Support Rating
Apache
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.
Read full review
Tableau
I have never really used support much, to be honest. I think the support is not as user-friendly to search and use it. I did have an encounter with them once and it required a bit of going back and forth for licensing before reaching a resolution. They did solve my issue though
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In-Person Training
Apache
No answers on this topic
Tableau
It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
Read full review
Online Training
Apache
No answers on this topic
Tableau
The training for new users are quite good because it covers topic wise training and the best part was that it also had video tutorials which are very helpful
Read full review
Implementation Rating
Apache
No answers on this topic
Tableau
Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
Read full review
Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Read full review
Tableau
If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via PyCharm, Atom, and Visual Studio IDE. These IDEs cannot directly help you to visualize the data but you can use many python packages to do so through these IDEs.
Read full review
Scalability
Apache
No answers on this topic
Tableau
Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
Read full review
Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
Read full review
Tableau
  • Tableau was acquired years ago, and has provided good value with the content created.
  • Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
  • Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.
Read full review
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