A company generates l GB of ticketing data daily. The data is stored in multiple tables. Business users need to see trends of tickets processed for…

The scenario describes a classic big data challenge where the full dataset (720 GB) is too large for efficient in-memory analysis. The user requirements are split: frequent analysis of high-level trends (aggregated data) and rare access to transaction-level details. On-Demand App Generation (ODAG) is the Qlik Sense feature specifically engineered for this purpose. It uses a small, fast summary app with pre-aggregated data for trend analysis. When a user needs to investigate details, they make selections in the summary app, and ODAG dynamically generates a new app containing only the relevant slice of transactional data. This provides optimal performance for the primary use case while still enabling deep-dive analysis when required.

A. Creating two separate, manually linked apps is a less efficient and less integrated solution. ODAG provides a seamless, automated workflow for the user.

B. Loading the entire 720 GB of transactional data into a single app would lead to extremely long reload times and poor user interface performance, failing to meet business needs.

D. This option is logically flawed. If the app only contains aggregated data, it is impossible to apply filters to see transaction-level data because that detail does not exist in the app's data model.

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1. Qlik Sense on Windows Help, May 2024. Section: "Managing big data with Qlik Sense". This document explicitly states, "On-demand app generation (ODAG) lets you load and analyze big data sources. Users are provided with an aggregated view of the data in a selection app. They can select the dimensions and measures they want to see, and then generate an app with the detailed data." This directly aligns with the proposed solution in option C.

2. Qlik Sense for Administrators Help, May 2024. Section: "Managing on-demand apps". This source describes the architecture: "In big data environments, it is often impractical to load an entire data set into a Qlik Sense app in memory... The on-demand selection app acts as a front end for selecting a slice of the data in the large data repository. The on-demand template app is a template for the generated on-demand apps." This confirms that ODAG is the intended mechanism for handling large data volumes by separating aggregated and detailed views.

3. Qlik Sense for Developers Help, May 2024. Section: "Building on-demand apps". This guide for developers reinforces the concept by detailing the creation of the two-part ODAG structure (selection app and template app), which is the core of the solution described in the correct answer. It outlines how the selection app works with aggregated data and the template app is used to generate the detailed view on demand.