To ensure optimal performance and a smooth experience when working with your architectural data in Ardoq, understanding efficient data access methods is crucial. This article explains Ardoq's powerful way of accessing and visualizing information, designed for scalability and speed, and also provides guidance if you're using the conventional workspace-based method.
Unlock Scalability: Explore Data Efficiently with the Viewpoint Builder
For the most efficient and scalable way to access and visualize your data in Ardoq, when dealing with any size dataset, we highly recommend using Viewpoints.
Accessing data through Viewpoints represents our ongoing commitment to improving your experience with large and complex architectural datasets. We strongly encourage all customers to transition to using Viewpoints for a more efficient, scalable, and future-proof data exploration and visualization experience.
Viewpoints fundamentally change how you interact with your data. Instead of loading an entire workspace, this approach allows you to selectively access precisely the information you need, when you need it. This provides significant benefits:
Streamlined Data Exploration: The Viewpoints allow for selective and focused data access, making it easier to explore and understand specific subsets of your data without being overwhelmed. Instead of loading an entire workspace, the Viewpoint Builder allows you to precisely select only the specific data you need for your analysis or visualization. This reduces computational load and speeds up your workflow.
Enhanced Efficiency: By focusing on only the relevant data, you can quickly build the views you need, accelerating your data exploration and analysis.
Scalability: This method is designed to perform seamlessly with datasets of any size, providing a consistent and fast experience.
Understanding the Conventional Workspace-Based Data Access
For users who might still access their data by navigating directly through their Ardoq workspaces, it's helpful to understand this conventional method. This approach, familiar to many long-time users, is primarily based on the workspace-data-loading paradigm.
With this method, you need to open a complete workspace to navigate and visualize data. Think of it as the "open everything first, then remove what I don't need" approach. While straightforward for smaller datasets, this method can sometimes lead to performance considerations, particularly when dealing with very large datasets.
Performance Recommendations for the Conventional Workspace-Based Method
In case you continue to use the conventional workspace-based data access method, or for specific smaller-scale use cases, the following recommendations help maintain optimal performance:
Workspace Size: We recommend having a maximum of 5,000 components and references per workspace for optimal performance.
View Visualization: When visualizing data within views, it's suggested to display a maximum of 1,000 components and references at a time.
Performance Optimization Techniques: To manage the number of elements shown and improve clarity, utilize features such as:
Filters and Graph Filters: To narrow down your focus.
Degrees of Relationship (available on the Block Diagram, Relationships, Dependency Map, Swimlanes, and Aggregation views): To control the depth of connections displayed.
Grouping and Collapsible Groups: For tidier graphs and better organization and analysis of your data.
Learn More About Data Access Patterns in Ardoq
To help you transition and make the most of these capabilities, please explore the following resources:
Understanding the Differences: Get a comprehensive overview of how the old (traditional) and new data access patterns compare:
Getting Started with the Viewpoint Builder: Learn step-by-step how to leverage the improved Viewpoint Builder for accessing, exploring, and visualizing your data: