Power BI is a powerful reporting and analytics tool that many of our customers use to build dashboards and gain insights into their financial and operational data from Xledger. There are two primary ways to connect Power BI to your Xledger data:
- Direct connection via the GraphQL API
- Using our Azure-based Data Warehousing solution
Option 1: Connecting Power BI Directly via the GraphQL API
Xledger provides a GraphQL API, which allows users to pull data directly into Power BI. This setup is ideal for customers who want to build lightweight, real-time reports without implementing additional infrastructure.
Key Benefits:
- Quick and easy setup: No additional software is needed. You simply configure Power BI to make authenticated calls to the Xledger API and begin pulling in your desired datasets.
- Customisable queries: Use GraphQL to shape the data exactly as you need it before it enters Power BI.
Important Consideration – 10,000 Record Limit:
A common question we receive is about the 10,000 record limit. This is a standard safeguard used across all modern APIs (not just Xledger’s) to ensure performance and security.
While this does introduce a layer of complexity, many users find it manageable for building reports where the dataset stays within reasonable limits, such as:
- Monthly dashboards
- Department-level views
- Drill down into recent transactions
If your reporting needs require deeper historical analysis, more frequent querying, or broader datasets, we recommend exploring our Data Warehousing solution.
Option 2: Using the Xledger Azure Data Warehousing Solution
For customers needing large-scale reporting across years of data, our Data Warehousing solution offers a highly scalable and robust option.
What It Is:
The Data Warehouse is a pre-built Azure package that includes:
- An Azure SQL Database pre-loaded with schema and table structures matching Xledger’s data model
- An Azure Data Factory (ADF) pipeline to extract, transform, and load (ETL) data from Xledger via the API
- Storage accounts and automation logic for managing refreshes and storing intermediate data
Benefits:
Scalable Reporting
- Unlike the API method, the warehouse is designed to handle millions of records without performance issues.
- This enables organisation-wide reporting, year-on-year comparisons, and audit trail analysis.
Advanced Modelling
- Data is already cleaned, flattened, and structured for analytics.
Automation
- Data refreshes can be scheduled (e.g., nightly), ensuring your reports stay up to date without manual intervention.
- ADF handles paging automatically, overcoming the 10,000-record limit of direct API usage.
Historical Tracking
- Data snapshots allow you to track changes over time, providing support for trend analysis and retrospective reporting.
Compliance and Security
- Installed within your organisation’s own Azure environment, meaning full control over access, backups, and retention policies.
Choosing the Right Approach
| Use Case | Recommended Method |
|---|---|
| Real-time dashboards (small datasets) | Direct GraphQL API |
| Organisation-wide reporting | Data Warehouse |
| Complex joins across multiple tables | Data Warehouse |
| Minimal technical setup required | GraphQL API |
| Long-term historical data analysis | Data Warehouse |
Need Help?
If you're unsure which method suits your reporting needs, our integrations team can guide you through the best-fit approach and even help with setup. Please get in contact with our CSM team, who will be able to set up a call with one of our integration consultants.