• Apr, 14, 2023
Guide of Performance and Optimization Best Practices in Qlik

Qlik is a powerful business intelligence and data visualization tool that enables organizations to gain insights from their data. However, as the amount of data in Qlik applications grows, the performance of the application may suffer. In this article, we will provide a guide of performance and optimization best practices in Qlik, covering key areas such as data modeling, dashboard design, scripting, data load, and server configuration.

Data Modeling

Data modeling is one of the most critical aspects of building a Qlik application. A well-designed data model can significantly improve the performance of the application. Here are some best practices to follow when designing your data model:

  1. Keep your data model simple: A simple data model is easier to understand and maintain. Avoid overcomplicating your data model with unnecessary tables or fields.
  2. Normalize your data: Normalize your data by breaking it down into smaller tables. This can help reduce data redundancy and improve query performance.
  3. Use synthetic keys sparingly: Synthetic keys can be confusing and may affect the performance of the application. Try to avoid synthetic keys whenever possible.
  4. Use QVD files: QVD files can help improve the performance of the application by reducing data load times. Use QVD files to store large datasets and load only the data that is needed.

Dashboard Design

Dashboard design is another critical aspect of building a Qlik application. A well-designed dashboard can improve user adoption and engagement. Here are some best practices to follow when designing your dashboard:

  1. Keep your dashboard simple: A simple dashboard is easier to understand and use. Avoid overloading your dashboard with unnecessary charts or data.
  2. Use consistent visualizations: Use consistent visualizations throughout your dashboard. This can help users understand the data better and make it easier to navigate.
  3. Use appropriate chart types: Use appropriate chart types for the data being displayed. For example, use a bar chart for categorical data and a line chart for time-series data.
  4. Optimize chart performance: Optimize chart performance by limiting the number of data points displayed and using appropriate filtering.

Scripting

Scripting is the process of loading and transforming data in Qlik. A well-written script can significantly improve the performance of the application. Here are some best practices to follow when scripting your application:

  1. Keep your script simple: A simple script is easier to understand and maintain. Avoid overcomplicating your script with unnecessary functions or calculations.
  2. Optimize data load times: Optimize data load times by using the appropriate data load techniques, such as incremental load or binary load.
  3. Use variables sparingly: Variables can affect the performance of the application. Try to use variables only when necessary.
  4. Use optimized functions: Use optimized functions, such as resident load, to improve the performance of the script.

Data Load

Data load is the process of loading data into Qlik from various sources. A well-designed data load process can significantly improve the performance of the application. Here are some best practices to follow when loading data into your application:

  1. Use appropriate data sources: Use appropriate data sources, such as QVD files or direct database connections, to load data into your application.
  2. Optimize data load times: Optimize data load times by using appropriate data load techniques, such as incremental load or binary load.
  3. Filter data during the load process: Filter data during the load process to reduce the amount of data being loaded into the application.
  4. Use appropriate data types: Use appropriate data types to improve the performance of the application. For example, use numeric data types for numeric data and date data types for date data.

Server Configuration

Server configuration is another critical aspect of optimizing the performance of your Qlik application. Here are some best practices to follow when configuring your Qlik server:

  1. Allocate appropriate server resources: Allocate appropriate server resources, such as RAM and CPU, to the Qlik server. This can help improve the performance of the application.
  2. Use the appropriate server architecture: Use the appropriate server architecture, such as clustering, to improve the performance and scalability of the application.
  3. Use appropriate security settings: Use appropriate security settings to secure your Qlik application and data.
  4. Monitor server performance: Monitor server performance using Qlik’s built-in monitoring tools or third-party monitoring tools. This can help identify performance issues and optimize the server configuration.

Optimizing the performance of your Qlik application requires careful consideration of data modeling, dashboard design, scripting, data load, and server configuration. By following the best practices outlined in this guide, you can significantly improve the performance of your Qlik application and enable your organization to gain valuable insights from your data.