Easy Data TransformĮasy Data Transform is a graphical drag and drop tool for data wrangling and analytics. Easy Data Transform and Tableau Prep are focussed on data wrangling. Which product is the best choice depends on the job in hand, the skills of the user and the budget available.Īlso R, Python + Pandas, Knime and Alteryx all come with bewildering arrays of optional extras for machine learning, forecast, heavy duty stats etc. Learning a programming language gives more flexibility but with a much steeper learning curve, especially if you aren’t an experienced programmer. Pandas is a Python library.Įach of the different approaches have strengths and weaknesses. Easy Data Transform, Knime, Tableau Prep and Alteryx are graphical, no-code/low-code, drag and drop software.These products take 3 different approaches: Time by task (seconds), Easy Data Transform only, Windows vs Mac (smaller is better): Looking at just the Easy Data Transform results, it is interesting to notice that a newish Macbook Air M1 laptop is significantly faster than an AMD Ryzen 7 desktop PC from a few years ago. Memory usage (MB), Windows vs Mac (smaller is better): Time total (seconds), Windows vs Mac (smaller is better): Time by task (seconds) on Mac (smaller is better): Time by task (seconds), on Windows without Power Query (smaller is better): Time by task (seconds), on Windows (smaller is better): Results Windows desktop type:ĪMD Ryzen 7 3700X 8-Core Processor 3.59 GHz with NVIDIA GeForce GTX 1660 If you think we have done something that represents them unfairly, please let us know. Also, exact comparisons aren’t really possible. But we aren’t experts in R, Python + Pandas, Knime, Tableau Prep, Alteryx or Power Query. While we have a horse in this race, we have tried to be fair to all products. It is a very simple benchmark, but we hope it gives an idea of the relative performance. We used default settings for each product. The average time from 3 runs was taken (apart from Power Query, which we only did once because it was so sloooow). writes the joined dataset to a CSV fileĮach run was done from scratch.inner joins the original and sorted datasets using the ‘id6’ column.creates a new dataset by sorting in ascending order of the ‘id5’ column.reads in a 1 million row x 7 column dataset from a CSV file.We also make some comments on the following products, but their licenses don’t allow for competitive benchmarking: This page show results from performance benchmarking the following on-premise (non-cloud) products using a 1 million row dataset: We struggled to find any benchmarks for a range of data wrangling/ETL software, so we have done our own.
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