6 April 2022 Literal values vs search strings in Qlik Set Analysis Share this message Every Friday at Bitmetric we’re posting a new Qlik certification practice question to our LinkedIn company page. Last Friday we asked the following Qlik Data Architect certification practice question about search strings in Set Analysis: This is typically a relatively tough question, but fortunately everyone who participated is above average! The correct answer is D You may wonder why this is a tough question, that’s because in the past both single and double quotes would be evaluated as a search expression by Qlik. This has lead to single and double quotes being used interchangeably, while formally only the double quotes are correct. The single quotes (‘) are used to denote a literal value. So by using single quotations in Set Analysis, you are looking for an exact match with that value. In this case the answer B solution will be looking for literally Internal* as field value. By using double quotes (“) Qlik will interpret this as a search value. In this example, the correct answer D, will be conducting a search for Internal*. The asterisk is a wildcard that can match any (sequence of) character(s). In this case Internal-Call, Internal-Chat and Internal-Email would be matched. What about flag fields? Yes, if this particular condition is one you want to test more often then it can be beneficial to create flag fields in your data model. This will make your Set Analysis easier and in cases more performant as well. In the context of this question though, we only wanted to test if you knew the difference between single and double quotes in Set Analysis. We’ve already established that you’re all above average 😉 That’s it for this week. See you next Friday? More from the Bitmetric team Qlik Cloud Backup Protect your investment in Qlik with daily incremental backups stored in an encrypted environment with redundant storage. Available for as little as 2 Euro per day. Learn more. Masters Summit for Qlik The Masters Summit for Qlik provides the next step in your path to becoming a Qlik specialist. As an extra perk, Bitmetric has secured an over $450 discount just for you! Use code BITMETRIC at checkout. Read more here. Expressions Friday Qlik Test Prep Set Analysis Solution How can we help? Barry has over 20 years experience as a Data & Analytics architect, developer, trainer and author. He will gladly help you with any questions you may have. Call us Mail us 17 September 2025 Protected: Qlik vs Power BI: Part 4 – End User Interaction & Integration There is no excerpt because this is a protected post. Power BI Qlik 11 September 2025 Qlik vs Power BI: Part 3 – Front End Development & User Experience Part 3 of our Qlik vs Power BI series explores Qlik’s grid responsive layout and associative model versus Power BI’s precision layout and query-based interactions, highlighting their impact on dashboard design and user experience. Power BI Qlik 4 September 2025 Qlik vs. Power BI: Part 2 – Back-End & Data Modeling This post is Part 2 of our series on Qlik vs Power BI data preparation and modeling. We compare Qlik’s structured, script-driven approach with QVDs to Power Query’s flexible, user-friendly interface, and explain what each means for building reliable data models. Power BI Qlik
17 September 2025 Protected: Qlik vs Power BI: Part 4 – End User Interaction & Integration There is no excerpt because this is a protected post. Power BI Qlik
11 September 2025 Qlik vs Power BI: Part 3 – Front End Development & User Experience Part 3 of our Qlik vs Power BI series explores Qlik’s grid responsive layout and associative model versus Power BI’s precision layout and query-based interactions, highlighting their impact on dashboard design and user experience. Power BI Qlik
4 September 2025 Qlik vs. Power BI: Part 2 – Back-End & Data Modeling This post is Part 2 of our series on Qlik vs Power BI data preparation and modeling. We compare Qlik’s structured, script-driven approach with QVDs to Power Query’s flexible, user-friendly interface, and explain what each means for building reliable data models. Power BI Qlik