![]() ![]() Less than 1% difference was measured between the 3 runs. Initially, the processing ran on an HDD with a peak speed of 7,200 rpm.įor this new test, I moved the files to my SSD and ran each query 3 times. I refer you to the initial article for all the diagrams of the data processing channels. Results: Effect of an SSD on ETL processing timeįirst of all, let me remind you that I started with a 43.6 GB CSV file (that’s significant!) and that I performed 2 simple operations (a sort and a “group by”). How long does it take to process a dataset of one billion lines stored on an SSD? Datawatch Does anyone use Alteryx or Datawatch to prep their data for Tableau My organization is looking at bringing a tool in for data prep before we get to tableau and would like thoughts, etc. The last one is a solution ranked in the G2 benchmark as “high performer”. Each products score is calculated with real-time data from verified user reviews, to help you make the best choice between these two options, and decide which one is best for your business needs. By contrast, Tableau Prep rates 4.2/5 stars with 56 reviews. The first one is not a “data preparation” solution per se. Alteryx rates 4.5/5 stars with 265 reviews. These are simply the ones I have access to: The choice of ETL’s to compare is entirely arbitrary. If you choose a “no-code” ETL solution, you’d better choose one that is fast, primarily if you work in the cloud and use it often.ĮTL’s : Alteryx vs. Storage has become a very affordable commodity. Remember that your “cloud” bill is first and foremost made up of CPU rental costs. in minutes spent waiting in front of the machine on the one hand.I defended my choices on the speed test by explaining my frustrations with the slowness of some solutions on the one hand and by reminding people that processing time is expensive: In my previous analysis, I compared the processing speed of 4 data preparation solutions: Alteryx, Talend, Tableau Prep, and Anatella.Īfter its publication on social networks, which raised several voices to criticize the content (why test the processing speed?) and the form (why not optimize the configuration by placing the file to be processed on an SSD?) Alteryx is more useful as a data transformation tool, for example parsing strings, adding filter1 for one visualization and filter 2 for the second report. Alteryx also has support for things like workflow-level variables, building custom interfaces, etc. ![]() Today I test the effect of SSD and proprietary file formats on processing speed in Alteryx, Tableau Prep, Talend, and Anatella. b) Alteryx can handle more complex workflow logic than is (easily) possible to build in Prep, and it's easier to package up that logic into macros than in Prep. In a previous article, I realized using a benchmark of 4 ETL solutions to process a file of one billion lines. ![]()
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