Tag Archives: data wrangling

Easy Data Transform progress

I have been gradually improving my data wrangling tool, Easy Data Transform, putting out 70 public releases since 2019. While the product’s emphasis is on ease of use, rather than pure performance, I have been trying to make it fast as well, so it can cope with the multi-million row datasets customers like to throw at it. To see how I was doing, I did a simple benchmark of the most recent version of Easy Data Transform (v1.37.0) against several other desktop data wrangling tools. The benchmark did a read, sort, join and write of a 1 million row CSV file. I did the benchmarking on my Windows development PC and my Mac M1 laptop.

Here is an overview of the results:

Time by task (seconds), on Windows without Power Query (smaller is better):

I have left Excel Power Query off this graph, as it is so slow you can hardly see the other bars when it is included!

Time by task (seconds) on Mac (smaller is better):

Memory usage (MB), Windows vs Mac (smaller is better):

data wrangling/ETL benchmark memory Windows vs Mac

So Easy Data Transform is nearly as fast as it’s nearest competitor, Knime, on Windows and a fair bit faster on an M1 Mac. It is also uses a lot less memory than Knime. However we have got some way to go to catch up with the Pandas library for Python and the data.table package for R, when it comes to raw performance. Hopefully I can get nearer to their performance in time. I was forbidden from including benchmarks for Tableau Prep and Alteryx by their licensing terms, which seems unnecessarily restrictive.

Looking at just the Easy Data Transform results, it is interesting to notice that a newish Macbook Air M1 laptop is significantly faster than a desktop AMD Ryzen 7 desktop PC from a few years ago.

See the full comparison:

Comparison of data wrangling/ETL tools : R, Pandas, Knime, Power Query, Tableau Prep, Alteryx and Easy Data Transform, with benchmarks

Got some data to clean, merge, reshape or analyze? Why not download a free trial of Easy Data Transform ? No sign up required.

Analyzing COVID19 data with Easy Data Transform

I have continued to make lots of improvements to Easy Data Transform, including:

gather, spread, summary and substitute transforms
a batch processing mode

Here is a video of me using Easy Data Transform to analyze the data.europa.eu COVID19 dataset. Hopefully it gives an idea of what the software is capable of.

Easy Data Transform v1.1.0

I released v1.1.0 of Easy Data Transform this week. It is a big upgrade, with some major new features.

easy-data-transform-v110

There is a new Javascript transform. This allows you to create custom transforms for anything that is too specialist to do with the other 37 built-in transforms. I’m not a fan of Javascript, with its horrible scoping and typing, and I would have preferred Python or Lua. But there is a Javascript engine built into Qt, so this was the easiest way to add scripting. Now if you want to multiply two columns of your data together in Easy Data Transform, you can just do this:

javascript-data-transform-v110

You can also access Javascript maths, date and string functions. So you can do some pretty complicated stuff. Hopefully the built-in transforms are enough to cover 95% of data transformations. But the new Javascript transform adds some serious flexibility for the remainder. The Qt Javascript engine is also pretty fast. In testing I was able to multiply values from 2 columns together across 10,000 rows in less than 0.03 seconds.

There is a new Lookup transform. This allows you to lookup values for one dataset in another dataset. For example, if you have a dataset with a column for country code and another dataset with columns for the country code and tax rate, you can look up the tax rate by country code.

Previously you could only output your data in Excel and delimited text formats (including CSV and TSV). The new release also adds output to JSON, HTML, Markdown, vCard, YAML and XML formats.

I have improved the speed of the Join transform significantly using hashing. This makes a big difference with large datasets.

To save time, Easy Data Transform guesses the likely columns you want to use as keys when you Join, Intersect, Lookup or Subtract two datasets. For example if 2 datasets both have colummns called ‘ID’ with lots of unique values that are common to both columns, it will choose those two columns as the default key columns. I have improved the heuristic used to set the default columns.

You can now add comments to input, transform and output nodes as a note to a colleague or your future self.

You can now snap your input, transform and output nodes to a grid, so you can keep your layout all lovely and neat.

I have also made some bug fixes and minor improvements.

Haven’t tried Easy Data Transform yet? Got some table or list data that you need to wrangle into a more useful form? Take the free trial for a spin.

Eating my own dogfood

Eating your own dogfood There is a story that a president of a pet food company ate some of his own dog food, to show how good it was. I’m not sure how tasty dog food really needs to be, given that dogs are happy to lick their own backsides. But his commitment is admirable. The least we can do as software developers is to use our own software as much as possible. After all, if you don’t use it, how can you expect anyone else to?

In that spirit I have been using my new Easy Data Transform product as much as possible. The biggest project so far has been merging two databases, for a charity that I volunteer at. I created an Airtable database for the charity. But volunteer information was already in a separate CRM. I imported relevant CRM data into Airtable, but the CRM system remained in use for emailing volunteers for a couple of years while I concentrated on Airtable and other tasks. In that time the Airtable database has become a roaring success for the charity. So we eventually decided to retire the CRM system and also use Airtable as our CRM.

Consequently I had to merge the latest CRM data into Airtable. I exported the relevant data from each as a CSV and then proceeded to merge the mailing list tags from the CRM into a new column in Airtable. I also created tables of discrepancies for the charity staff to work through. For example, where the telephone numbers or emails had been added or updated in one database, but not the other.

When I had initially imported the CRM data into Airtable, I had imported the CRM ID record. So those records were easy to match between Airtable and the CRM using a simple join on the ID. However any records added subsequently to Airtable or the CRM did not have matching IDs. So I had to match those by first name + last name or email address. The data was quite ‘dirty’, as is invariably the case with real world data. A phone number may be “0123 456 789” on one system and “01 23456789” on another. A volunteer might be “Chris” in one database and “Christopher” in another. Also some contacts had multiple entries in the CRM system. So this was not a trivial problem.

You can get an idea of what was involved from the screenshot above. The two pink input nodes are the 2 databases exported as CSV files, the blue nodes are various transforms (joining, filtering, removing spaces etc) and the green nodes are the outputs (e.g. lists of telephone and email differences, lists of people in one database, but not the other etc). Quite a lot of the transforms are just column renames (in future I should probably support renaming multiple columns in one transform).

I think this would have been a horrific task using Excel, SQL, Beyond Compare or any of the other tools I had to hand, amazing tools as they may be for other tasks. But Easy Data Transform performed brilliantly, even if I do say so myself. It was particularly helpful that you could see the whole process step-by-step and backtrack or branch at any point without losing previous changes.

While eating my own dogfood, I found one bug (related to carriage returns inside CSV records) and quite a few minor annoyances. These have now been fixed in the latest release. I also added a new ‘Compare Columns’ transform, which was really useful for this sort of work. So it was a very useful experience and I really recommend ‘eating your own dogfood’ as much as you can, along with usability testing.

Have you got some data that needs cleaning, merging, de-duping or filtering? Analytics, log files, emailing lists, databases? Of course you do! Why not give Easy Data Transform a try. It is free while it is in beta. Let me know how you get on.