Mastering Qlik Application Reloads in Qlik Cloud: A Comprehensive Guide
- Mark Costa
- Jun 1
- 6 min read

In the dynamic landscape of business intelligence, keeping data fresh and relevant is paramount. Qlik Cloud offers a robust ecosystem for data analytics, with various methods to reload Qlik Analytics, Script, and Data Flow Applications—collectively referred to as Qlik Applications. Each reload method serves specific use cases and can be triggered through different mechanisms. This comprehensive guide explores these methods in detail, empowering developers and administrators to make informed decisions based on their unique requirements.
Understanding the Reload Object: The Foundation of Qlik Reloads
At the core of Qlik's reload functionality lies the Reload Object - a comprehensive record that captures essential details about each reload event. This object stores critical information including:
Application identifier
Reload request parameters
Precise start and end timestamps
Identity of the initiator
Current reload status
Detailed execution logs
It's crucial to note that reload types generating a Reload Object are subject to a 192 reloads per day limitation per Qlik Application. While additional capacity can be purchased, this constraint translates to a minimum interval of approximately 8 minutes between consecutive reloads - a significant consideration when designing your reload strategy.
Exploring Qlik Cloud Reload Types: A Detailed Analysis
A Reload Type identifies the trigger or source that initiates a reload event. Qlik Cloud currently supports six distinct reload types, each with unique characteristics and optimal use cases:
Hub (Manual Reload): The On-Demand Solution
Manual reloads initiated directly from the Qlik Cloud Hub provide immediate, on-demand data refreshes without requiring technical setup.
How to Execute:
Navigate to the Qlik Cloud Hub, right-click the desired Qlik Application, and select either "Reload Now" or "Run Task" from the context menu.
Ideal Scenarios:
Quick validation of script changes
Immediate data updates for time-sensitive analyses
Ad-hoc testing during development phases
Advantages:
Zero configuration required
Immediate execution on demand
Straightforward user experience for non-technical users
Limitations:
Requires manual intervention for each execution
Cannot be automated or scheduled
Contributes to the daily reload quota limitation
Not suitable for production environments requiring consistent data refreshes
Chronos (Scheduled Reload): The Legacy Scheduler
Chronos provides time-based scheduling for automated reloads at predefined intervals, though it has been superseded by the more advanced Choreographer system.
How to Execute:
Right-click the Qlik Application in the hub, select "Schedule", then create a new task with your desired frequency (daily, weekly, monthly, or yearly) - (Deprecated. Use Choreographer).
Ideal Scenarios:
Regular data updates with predictable patterns
Overnight processing of large datasets
Environments with stable, time-based data refresh requirements
Advantages:
Fully automated execution without manual intervention
Reliable scheduling for consistent data freshness
Simple configuration for basic scheduling needs
Limitations:
Minimum scheduling interval restricted to one hour
Limited flexibility for complex scheduling patterns
Counts toward the daily reload quota
Being phased out in favor of Choreographer
External (API-Triggered Reload): The Integration Powerhouse
External reloads leverage Qlik's REST API to initiate reloads programmatically from external systems, offering maximum flexibility for sophisticated integration scenarios.
How to Execute:
Implement API calls to the Qlik REST API Endpoint "POST Reloads" (Qlik Dev API) from your external applications or scripts.
Ideal Scenarios:
Integration with external data pipelines
Event-driven architectures requiring conditional reloads
Complex enterprise systems with multiple dependencies
Advantages:
Exceptional flexibility for custom integration patterns
Programmable conditional logic for reload decisions
Seamless incorporation into existing enterprise workflows
Ability to trigger reloads based on external events or data changes
Limitations:
Requires technical expertise in API integration
Necessitates external systems for orchestration
Contributes to the daily reload quota
Potentially complex to maintain and troubleshoot
Automations (Qlik Automate Triggered Reload): The Workflow Orchestrator
Automations leverage Qlik's built-in workflow engine to trigger reloads as part of sophisticated process chains, offering powerful orchestration capabilities within the Qlik ecosystem.
How to Execute:
Design workflows in Qlik Automate using blocks such as "Do Reload," "Call URL," "Raw API Request," or "Reload App" to incorporate reload operations into your automation sequences.
Ideal Scenarios:
Complex multi-step data processing workflows
Conditional reload scenarios based on data validation
Parallel execution of multiple related applications
Sophisticated notification and error-handling requirements
Advantages:
Seamless integration with other Qlik Automate capabilities
Support for complex conditional logic and branching
Ability to chain multiple reloads in sequence or parallel
Frequent execution capability (as often as every 30 seconds)
Visual workflow design requiring minimal coding
Limitations:
Increased complexity for extensive workflows
Maintenance challenges in intricate scenarios
Consumes automation execution quota
Contributes to the daily reload quota
Learning curve for effective workflow design
Data-Refresh (Data Triggered Reload): The Data-Driven Approach
Data-Refresh reloads respond automatically to updates in underlying QVD files generated by data pipeline projects, creating a truly data-driven refresh mechanism.
While no longer directly available in the Hub interface, this functionality can be configured using Qlik Cloud REST APIs (Qlik Dev API) by setting the autoReload property to true.
How to Execute:
Automatically initiated when QVDs from Qlik Talend Cloud Data Integration projects are refreshed.
Ideal Scenarios:
Data pipelines with QVD dependencies
Environments requiring immediate response to data changes
Multi-tier data architectures with cascading updates
Advantages:
Ensures data is always current with source changes
Eliminates unnecessary reloads when source data remains unchanged
Creates efficient, event-driven data pipelines
Optimizes resource utilization
Limitations:
Requires Qlik Talend Cloud subscription
Technical complexity due to API configuration requirements
Risk of unintended parallel reloads in complex dependency chains
Contributes to the daily reload quota
Steeper learning curve for implementation
Choreographer (Internal System-Triggered Reload): The Modern Task Manager
Choreographer represents Qlik Cloud's evolution in task management, combining scheduling capabilities with basic task chaining functionality in a unified interface.
How to Execute:
Configure tasks through the Choreographer interface in Qlik Cloud, specifying schedules and dependencies between tasks.
Ideal Scenarios:
Environments requiring both scheduling and basic task dependencies
Straightforward reload chains with clear predecessor-successor relationships
Organizations transitioning from Chronos to more advanced orchestration
Advantages:
User-friendly interface with visual task relationships
Integrated scheduling and dependency management
Future-proof platform receiving ongoing enhancements
Simplified management of reload sequences
Limitations:
Currently limited support for complex conditional logic
Less flexible than full Automations for sophisticated scenarios
Contributes to the daily reload quota
Still evolving in terms of feature completeness
Beyond the Quota: Unlimited Reload Methods
The following reload methods operate outside the standard Reload Object framework and are therefore exempt from the 192 daily reload limitation - making them invaluable for high-frequency reload scenarios:
Load Data Button / Qlik-CLI: The Developer's Toolkit
These methods provide direct reload capabilities without generating Reload Objects, offering unlimited execution frequency—particularly valuable during development and testing phases.
How to Execute:
Load Data Button: Access through the Load Script interface, Qlik Analytics Application Sheet, or Dashboard
Qlik-CLI: Utilize the App Reload Command (Qlik CLI) from command-line interfaces
Ideal Scenarios:
Development environments requiring frequent reloads
Scenarios with high iteration/frequency requirements
Situations demanding unlimited reload frequency
Advantages:
Simple, accessible execution methods
No contribution to daily reload quota limitations
Perfect for development and high-frequency reload needs
Qlik-CLI offers extensive automation possibilities
Limitations:
Limited reload history (only available in audit logs)
Reduced visibility into reload performance metrics
Requires command-line familiarity for Qlik-CLI approach
Utilize the Power of Qlik-CLI
Qlik-CLI deserves special attention as a remarkably versatile tool that extends well beyond simple reload operations. This command-line interface unlocks integration possibilities with virtually any platform supporting CLI commands, including PowerShell, Python, and various CI/CD systems.
With Qlik-CLI, developers can craft sophisticated custom task management solutions that address even the most complex orchestration requirements. The tool's flexibility makes it an excellent choice for organizations with unique workflow needs or those requiring tight integration with existing enterprise systems.
Strategic Considerations for Optimal Reload Selection
When determining the most appropriate reload method for your specific scenario, consider these critical factors:
Data Freshness Requirements: How current must your data be? Minutes, hours, or days?
Automation Needs: Is manual triggering acceptable, or must the process be fully automated?
Integration Complexity: Does your solution need to interact with external systems or respond to external events?
Workflow Characteristics: Are your reloads standalone operations or part of larger process chains?
Resource Constraints: How does the daily reload quota impact your architecture decisions?
Technical Expertise: What implementation and maintenance capabilities exist within your team?
Conclusion: Crafting Your Optimal Reload Strategy
The diverse reload methods available in Qlik Cloud provide remarkable flexibility to address virtually any data refresh requirement. By thoughtfully selecting the appropriate reload type based on your specific needs, you can significantly enhance resource utilization, operational efficiency, and overall management of your Qlik Cloud environment.
A well-designed reload strategy balances data freshness requirements against system resources, creating a sustainable approach that scales with your organization's evolving analytics needs. Whether you prioritize simplicity, automation, integration, or unlimited frequency, Qlik Cloud offers a reload method perfectly suited to your unique requirements.
Download the Qlik Cloud Application Reload Methods Cheat Sheet
To help you implement the optimal reload strategy for your Qlik Cloud environment, we've created a comprehensive cheat sheet summarizing all reload methods, their characteristics, and selection criteria.
Download the Qlik Cloud Application Reload Methods Cheat Sheet (PDF):
This printable reference guide provides:
A complete comparison of all six reload types
Decision matrix for method selection
Key considerations for implementation
Quota management strategies
Keep it handy as you design and optimize your Qlik Cloud data refresh processes!
Event though I understand this is a Qlik Cloud post, do you happen to know if Choreographer is implemented (or will be implemented) on the OnPrem version? And what kind of breaking changes should we be aware of? Thanks for the posting
Thank you for sharing. It's great to see these arguably missing details from the documentation being supplemented by those experienced in the tool. I will certainly refer to the material (and the cheat-sheet) on every opportunity I have from now. Thank you again and, as always: happy coding.