Unlock the Power of Incremental Refresh in Power BI

Hey there!

Keeping on top of the latest data is tough, especially when working with enormous datasets. Refreshing can take hours – or even days! – slowing you down and costing a fortune in compute resources.

But what if you could refresh just the new data? Update only what‘s changed rather than everything?

Well, my friend, you‘re in luck… because you can!

Incremental refresh is an absolute game-changer when it comes to keeping giant datasets current. Let me walk you through exactly how it works and how to configure it step-by-step.

What is Incremental Refresh?

With incremental refresh, Power BI intelligently partitions your data based on a date/time field. This allows it to refresh only the latest pieces rather than the entire dataset.

It‘s like only washing the dirty dishes in your sink rather than rewashing the clean ones again and again.

According to Microsoft, enabling incremental refresh can reduce refresh times by up to 90%. And because only a fraction of the data is updating, it saves massively on compute costs.

Consider a 100 million row dataset that takes 10 hours to fully refresh. With incremental refresh of the past 7 days only, it may refresh in just 1 hour instead!

Some key ways incremental refresh will benefit you:

• Blazing fast refresh times – Up to 10-20x faster by refreshing less data
• Huge cost savings – Reduce cloud compute costs by 80-90%
• No historical data loss – Retain context while staying current
• Effortless scale – Handle billions of rows without slowdowns
• Near real-time insights – Refresh up to 48x daily

It‘s clear that incremental refresh offers game-changing performance. But where does it really shine?

Top Uses Cases for Incremental Refresh

Retail Sales Data

Refresh daily sales figures without having to rebuild entire reports. Stay on top of daily results while retaining historical context.

A retailer could refresh a 5 year sales history dataset daily by incrementally adding just the last 24 hours of new sales data.

Website Visitor Analytics

Analyze trends in website visitors with up-to-the-minute data. Incrementally add the latest traffic stats without having to reprocess months of historical data.

Supply Chain Monitoring

Stay on top of inventory levels, logistics data, and supply chain KPIs with near real-time refresh. Add the latest incremental snapshots to identify issues early.

IOT and Time Series Data

Efficiently handle endless streams of time series data from IOT devices, sensors, web logs, and more. Minimize latency while eliminating data loss.

As you can see, incremental refresh is a perfect fit for many modern analytics use cases dealing with continuous, ever-growing data flows.

Okay, ready to enable this magic yourself? Let‘s look at how incremental refresh compares to standard full refresh first.

Full Refresh vs. Incremental Refresh

Full refresh reimports the entire dataset from the source system every single time. This ensures any data changes in the underlying database are reflected but comes at the cost of long refresh times and high resource demands.

Incremental refresh, on the other hand, partitions the dataset and only refreshes the slices within your defined date/time range. This means you get the latest data without the downsides of full refresh.

Let‘s compare the two approaches:

	Full Refresh	Incremental Refresh
Data Updated	All data reimported from source	Only new data based on partition filters
Performance	Slow, full data reprocessing	Fast, focused on new data
Resource Usage	High, full dataset processed every time	Low, only new partitions processed
Cost	Expensive due to high resource utilization	Reduced costs from lower resource utilization
User Experience	Poor, reports unavailable during long refreshes	Seamless, background refreshes with no interruption
Historical Data	Requires full reload, loses historical context	Retains history outside refresh partitions
Real-time Insights	No, full refreshes are too slow for real-time	Yes, supports near real-time incremental updates
Ideal For	Small datasets with infrequent changes	Large and complex datasets with frequent changes

As you can see, incremental refresh is vastly preferable over full refresh in most scenarios. But it does come with some limitations.

Limitations of Incremental Refresh

While extremely useful, incremental refresh isn‘t a silver bullet. Be aware of a few constraints:

• Large partitions – Massive date ranges still require substantial resources, so use the smallest effective range.
• Many partitions – Numerous partitions can add overhead, try consolidating based on how often data actually changes.
• Parameter tuning – Getting the partition start/end parameters right is key to optimizing performance and cost.
• Not real-time – Data latency is still based on refresh schedule. For true real-time, explore DirectQuery or materialized views.
• DB load – Despite less data, incremental refresh can still tax databases, so mind capacity planning.

However, these limitations are generally minor compared to the enormous time and cost benefits incremental refresh provides.

Now let‘s walk through how to set it up step-by-step.

Prerequisites for Incremental Refresh

Before we get to the configuration, there are a few requirements your environment needs to meet:

• Power BI License – Requires a Pro license for scheduled refresh. Premium license required for DirectQuery real-time partitions.
• Date column – A column identifying record date/time must exist, typically a Date or DateTime field.
• Query folding – Data source must support query folding to filter dataset by date/time column.
• Single data source – All partitions must share the same underlying data source.

As long as your dataset, data source, and Power BI license meet these prerequisites, you‘re ready!

Now let‘s dive into the configuration steps…

Step-by-Step Guide to Configuring Incremental Refresh

Here is the high-level process we‘ll walk through:

Let‘s explore each step:

Step 1: Import Dataset into Power BI Desktop

First, we need to get the dataset imported into Power BI Desktop, where we‘ll configure incremental refresh before publishing to the cloud.

To import data:

1. Open Power BI Desktop
2. Select Get Data
3. Choose data source (SQL Server, Excel workbook, etc)
4. Select database and tables to import
5. Load data into Power BI Desktop

Now the raw dataset is loaded and ready for the next steps.

Step 2: Add Range Parameters

We need to define the parameters that specify the incremental refresh partitions.

Here‘s how:

1. In Power BI Desktop, select Transform Data to open Power Query editor
2. In the Queries pane, select the query you want to enable incremental refresh on
3. Click the date column you want to partition data by
4. Select Manage Parameters from the column header overflow menu
5. Create a new parameter named RangeStart with data type Date/Time
6. Repeat to create a RangeEnd parameter

This gives you the refresh range bookends to slice your dataset by.

Step 3: Configure Incremental Refresh

Almost there! Next we enable incremental refresh on the dataset itself.

In Power BI Desktop:

1. In the Fields pane, right-click the dataset
2. Select Incremental Refresh from the menu
3. Check the box to enable incremental refresh
4. Enter RangeStart parameter as refresh range start
5. Enter RangeEnd parameter as refresh range end
6. Select Ok to save settings

Your dataset is now partitioned by the date range parameters, ready for incremental refresh.

Step 4: Publish to Power BI Service

The last step is publishing up to the Power BI Service for scheduled refresh:

1. In Power BI Desktop, select Home > Publish
2. Choose where to publish the dataset
3. Select Publish to upload to the Power BI Service

Once published, you can enable automatic scheduled refresh in the Power BI Service based on your preference – daily, hourly, etc.

And that‘s it – you‘re now refreshing only new data! Your reports will stay up-to-date while resources and time are saved.

Best Practices for Optimizing Incremental Refresh

To really maximize the performance and cost benefits of incremental refresh, keep these tips in mind:

• Start small – Begin with smaller date ranges and increase gradually to ensure manageable refresh durations.

• Align to data frequency – Partition based on how often data actually changes, such as daily/weekly rather than hourly.

• Use recent data – Focus refresh partitions on more recent data where insights matter most.

• Test and tune – Monitor refresh metrics and tune parameters for optimal performance.

• Schedule wisely – Run refreshes during off-peak times to minimize database load.

• Augment with DirectQuery – Combine scheduled incremental refresh with DirectQuery real-time partitions.

• Monitor usage – Keep an eye on resource utilization in Power BI to quickly catch any issues.

Following these best practices will help you smooth out incremental refresh performance while maximizing value.

Takeaway

There you have it – everything you need to enable incremental refresh for blazing fast and efficient dataset refreshes.

The key takeaways are:

• Incremental refresh intelligently partitions data for focused updating
• It offers massive improvements in refresh speeds and cost savings
• Requirements are straightforward – just need a date column and single source
• Configuration involves adding range parameters and enabling the feature
• Follow best practices to optimize for performance and efficiency

Now put these steps into action for your own datasets. I think you‘ll be amazed by the performance gains. No more waiting hours or days for giant datasets to update!

Have any other tips for tuning incremental refresh? Share your thoughts below!