Contents

1 Understanding Composite Models
2 The Simple Import Vs DirectQuery Debate
3 Designing a Hybrid Model – Step-by-Step Framework
4 Working with Aggregations in Hybrid Models
5 Incremental Refresh for Hybrid Models
6 Quick Setup Guide:
7 Query Folding and Advanced Optimisation
8 Common Pitfalls and How to Avoid Them
9 Testing, Monitoring, and Continuous Optimisation
10 Conclusion

Imagine delivering a Power BI dashboard that loads in seconds, stays up-to-date throughout the day, and handles billions of rows without breaking a sweat.

That’s what a Hybrid Model – sometimes called a composite model – can help you achieve.

For years, Power BI developers have faced a choice:

Import Mode for speed, but with long refresh times and high memory consumption
DirectQuery for real-time data, but often with slower visuals and dependency on database performance

A Hybrid Model eliminates that trade-off by allowing different parts of your dataset to use different storage modes.

The real power comes from pairing these modes strategically:

Layering in aggregations, incremental refresh, and query folding to strike the perfect balance.

This guide not only explains how to design such a model but also dives into the why.

Understanding Composite Models

At its core, a composite model lets you mix multiple storage modes in a single dataset:

Import: Data is preloaded into Power BI’s memory for ultra-fast querying. Best for historical, rarely changing data.
DirectQuery: Queries run live against the source system. Best for dashboards that need up-to-the-minute accuracy.
Dual: A flexible mode where a table behaves as Import for some queries and DirectQuery for others.

Why it matters: In large organisations, not all data changes at the same rate.

Financial results from last year don’t need live updates, but a sales pipeline dashboard might.

Trainer Insight: During our Power BI training in London, we often see teams defaulting to either Import or DirectQuery.

The reality is that no single mode works for every scenario.

A composite model allows you to match storage mode to the business question, which is a game-changer for both speed and usability.

When implemented well, it means faster reports, fresher data, and happier stakeholders.

The Simple Import Vs DirectQuery Debate

Often when starting projects, teams will decide early on whether they should use Simple Import or DirectQuery.

But making this decision just sets them up for failure and misses the point!

Choosing just one mode will lead to compromises that hurt either performance or business responsiveness.

A hybrid model, on the other hand, uses the strengths of both, while minimising their weaknesses.

Import-only pitfalls: Long refresh cycles, high memory usage, and slow deployments for large datasets.

Refresh failures can also delay reporting.

DirectQuery-only pitfalls: Every interaction triggers a query to the database.

This can cause slow visuals, unpredictable performance, and an increased load on operational systems.

Trainer Insight: A common pitfall we see is relying too heavily on DirectQuery for every table.

While it keeps data live, it often results in painfully slow visuals.

A hybrid approach prevents this by reserving DirectQuery only for the data that truly needs to be real-time.

Designing a Hybrid Model – Step-by-Step Framework

Designing a hybrid model isn’t just about mixing modes.

It’s about aligning the model to business needs and user behaviour.

Analyse Data Access Patterns – Identify high-frequency queries and which datasets require real-time accuracy.
Segment Tables by Storage Mode – Import for historical data, DirectQuery for operational, Dual for lookups.
Leverage Aggregations – Pre-aggregate large fact tables for most queries.
Implement Incremental Refresh – Refresh only recent partitions.
Optimise Relationships – Minimise cross-storage joins.

Working with Aggregations in Hybrid Models

Aggregations are a performance optimization feature in Power BI.

They allow pre-summarized data to be stored in Import mode, which speeds up Power BI.

Rather than querying detailed data from a live DirectQuery source every time, Power BI can first check a designated aggregation table to check if it already contains the summary.

When to use: If most queries involve grouped summaries (e.g., sales by month, region, product).
How it works: Power BI checks the aggregation table first; if the detail exists there, it returns instantly.
Impact: Can reduce query times from several seconds to under one second.

Trainer Insight: Having run over 600 Power BI courses since 2016, we’ve constantly see that hybrid models often give organisations the breakthrough they’ve been looking for.

We teach teams how to balance Import for historical data and DirectQuery for live feeds.

Then their design models that stay fast, accurate, and aligned with business needs.

Incremental Refresh for Hybrid Models

In large datasets, a full data refresh is very resource-intensive, and time-consuming.

Incremental refresh is a Power BI feature designed to solve this issue by updating only the data that has changed.

This is ideal for our hybrid models!

Refreshing only the most recent period (e.g., last 30 days)
Keeping older partitions static in Import
Combining with DirectQuery for near real-time reporting

Quick Setup Guide:

1. In Power Query, create two parameters named RangeStart and RangeEnd (type: Date/Time).

2. Filter your fact table’s date column: Date >= RangeStart and Date < RangeEnd.

3. In Power BI Desktop, right-click the table → Incremental refresh.

4. Turn it on, set how much data to store (e.g., 2 years) and how much to refresh (e.g., last 1 month).

5. Publish to Power BI Service — incremental refresh runs there, not in Desktop.

Query Folding and Advanced Optimisation

Performance for DirectQuery tables depends largely on query folding.

To maximize efficiency, avoid Power Query steps that prevent folding.

Push complex calculations to the source database, and consider using indexed or materialized views for pre-aggregated data.

This confirms that query folding is occurring.

The filtering and joins are being pushed down to the SQL Server source for optimal performance.

Common Pitfalls and How to Avoid Them

As a trainer, I often see the same mistakes crop up in hybrid model projects.

The first is too many mode transitions.

Mixing Import and DirectQuery without careful consideration leads to unpredictable performance.

It also makes debugging much harder than it needs to be.

Another is not testing query paths.

Developers assume visuals are using cached Import data.

But without tools like Performance Analyzer, it’s easy to miss when DirectQuery is being hit instead.

Finally, gateway performance is often ignored.

Even well-designed models can lag if the gateway isn’t up to scratch – especially with live connections.

Spotting these early helps ensure your visuals hit the right source, and your reports stay fast.

Make sure you use the Performance Analyzer!

This helps identify visuals that unexpectedly query the live source instead of using cached Import data.

Testing, Monitoring, and Continuous Optimisation

Hybrid models require ongoing tuning:

Track visual load times with Performance Analyzer
Inspect SQL sent to sources with Query Diagnostics
Monitor gateway CPU/memory for DirectQuery workloads
Reassess storage mode assignments quarterly

Conclusion

A hybrid model in Power BI blends the best of Import and DirectQuery, giving you both speed and up-to-date insights.

By pairing storage modes wisely and using features like aggregations, incremental refresh, and query folding:

You will optimise performance without losing accuracy.

It’s a practical approach for modern reporting needs – fast where it can be, live where it matters.

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