status: 'pending' | 'accepted' | 'rejected'ĭata Generator for Retail is licensed under the MIT License, sponsored and supported by marmelab.Discovering these nuggets during a demo really makes for a much more compelling demo and better illustrates the value of QlikView.Import generateData from 'data-generator-retail' const data = generateData ( ) // now do whatever you want with the data. Next, loop through that table and autogenerate the amount of records that was calculated for each day.Īdditionally, if this is a sales demo, I will add a few ‘discoveries’ to the data manually. First generate a table of 365 days that follow the sin/cos wave. You can also use a sine or cosine function to generate a pattern that looks seasonal. Using NormInv() makes your charts look much nicer too, instead of the flat, linear distribution that Rand() gives you. For example, many salespeople/products/employees/etc will be near average performers but there’s always a few that are stand-out performers (and underperformers). One of the things I like to use is the NormInv() function instead of Rand(). It’s true that getting everything to look real can be quite a challenge. You can generate all of this in a big, flat table and split it out later if necessary. I would generate the random sales amount and sales date in the first load and then use preceding load to generate random costs and payments that are in line with the sales amount. You can use the Sin() or Cos() functions to create (relatively) realistic seasonal patterns in your data.You can use the NormInv() function in QlikView to generate random values that follow a normal distribution, in my opinion this makes for much nicer, more realistic charts.nuysoft / Mock A simulation data generator Mock. Some additional tips for nice, random data in QlikView: Mock.js is a simulation data generator to help the front-end to develop and prototype separate from the back-end progress and reduce some monotony particularly while writing automated tests. In any case, if you ever need some realistic test data then Mockaroo is definitely worth a look. Alternatively, you can use this tool to generate your dimension tables while using the ‘old’ approach to generate big fact tables. You may want to add more fields or delete unwanted fields. Step 5: The form gets loaded with default fields. Step 4: Click on ‘Generate Dummy Data’ button. Step 3: As the file contains macros, you may be asked to enable the macros to use the tool. While the 100,000 row limit is on the low side when you want to test the performance implications of various approaches, this does seem to be a very useful tool for customer demo’s. Click here to Download Dummy Data Generator Tool. You can even save your schemas and retrieve them directly from Mockaroo in QlikView: You can generate up to 100,000 rows of random data, which can be exported to many different formats, CSV being the most useful for QlikView. This tool lets you generate dozens of different data types, from random dates, string and numbers to cities, names and even shirt sizes. Can be useful for charts and stuff, I guess. press F9 to recalculate and get a brand new set of data. Jun 19th 2015 1 Not sure how useful this will be, but it generates random sample demo data using Excel formulas. You can imagine that I was pleasantly surprised when I caught a tweet by Qlik’s Michael Tarallo yesterday in which he mentioned an online tool for generating test data: Mockaroo. Reactions Received 1 Points 27,606 Posts 5,327. While this approach works well it can be quite a hassle, especially if you want to create dimension values that look plausible. The database was filled with names, cities, geographical locations, FK links and I. Michael Gaertner, Quintech In less than the time it took me to get my coffee, I had a database with 2 million rows of data for each of 10 tables. Faker is a Python library that helps you generate fake data. With SQL Data Generator I generated better data in only half an hour, and then, after this initial customization was done, in only seconds, with just one click. Then I put together some load statements like the one shown below and voila, random data. As we develop data applications or pipelines, we need to test them with data that resembles. For dimension labels I use mapping tables containing, for example, first and last names or company names. Some of these are shown in the image above. I usually create random data by using variables that generate different types of random data. As I am probably not the only one with this need, here’s a short and sweet post on how to generate random data for use in QlikView. For example to use in an example file on this blog, to deliver a customer demo or just to test out something new.
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