How to Build a Direct Lake Model Step by Step for Advanced Data Modelling
You want to learn how to build a Direct Lake model. You need to follow steps for advanced data modelling in Microsoft Fabric. Direct Lake helps you get faster queries. It gives you flexible backup choices. It also helps you manage your data well.
You can make things work better by tuning performance. You should know how Parquet files work.
You learn new modelling skills. These skills help with big datasets. They also help with SQL backup plans.
This comprehensive guide teaches you useful skills. It shows you advanced ways to get great results.
Key Takeaways
Direct Lake models let you see data right away. You do not need to wait for updates. This helps you make quick choices and get answers fast.
Parquet files help your searches go faster. They also save space. This makes them good for big data in Direct Lake models.
Use strong safety steps like Row-Level Security and encryption. These keep important data safe. Only people who should see the data can get to it.
Take care of your Direct Lake model often. Use VACUUM and OPTIMIZE commands. This keeps it working well and fast.
Learn more coding in languages like PySpark. This can make you better at data modeling. It also helps you use Direct Lake in smarter ways.
Comprehensive Guide: Direct Lake Essentials
Overview of Direct Lake
Direct Lake connects Power BI and Microsoft Fabric to your data in OneLake. This link lets you read data in Delta format right away. You do not need to set up refresh times. Dashboards change as soon as your lakehouse data changes. You can look at huge amounts of data, even petabytes, without making summaries first. You save money because you do not copy data. Direct Lake uses Delta Lake, so you get ACID transactions and schema changes.
Tip: Direct Lake gives you quick answers and works with big analytics.
Insights happen in real time
Handles lots of data
Costs less
Uses Delta Lake
Unique Features
Direct Lake is different from Import and DirectQuery modes. You get data when you need it, without bringing in everything. You get almost real-time speed. Import mode puts all data in memory, so it is fast but does not update right away. DirectQuery shows changes quickly, but it can be slower because it depends on the database. Direct Lake mixes the best parts of both.
Direct Lake gives you flexible access and fast speed. You do not wait for big imports or slow queries.
Benefits for Data Modellers
A Comprehensive Guide shows why Direct Lake is a good pick. You work with big datasets and get results right away. You do not have to worry about copying data. You use ACID transactions to keep things safe. You handle schema changes easily. You save time and resources.
You get analytics in real time.
You work with petabyte-size data.
You save money by using OneLake data.
You keep models safe and current.
A Comprehensive Guide helps you build, tune, and keep your Direct Lake models working well. You learn how to use these features for advanced data modelling.
Setup and Prerequisites
Tools and Platforms
You need some tools to build a Direct Lake model. These tools help you make and change your data models easily. The table below lists the main tools you will use:
Tip: You can use both Power BI Desktop and web modeling. Pick the one that works best for you.
Data Lake Preparation
You must get your data lake ready before you start. Follow these steps to make sure your data lake is set up:
Decide what you want to do and how much data you need.
Check your data sources. Make sure your data is good.
Plan your data lake. Choose a safe and big platform like AWS, Azure, or Google Cloud.
Set rules for data use. Follow laws like GDPR and HIPAA.
Bring in your data and store it. Use batch or real-time ways. Sort data into raw, curated, and consumption zones.
Organize your metadata. Make it easy to find and sort data.
Change and process your data. Build ETL steps and frameworks.
Check if your data is correct. Combine data from different places.
Keep your data safe. Set strong rules for who can see it.
Teach your team how to use the data lake.
Watch how your data lake works. Fix problems to keep it running well.
Look at your data lake often. Make changes to improve it.
A Comprehensive Guide helps you follow these steps. This gives you a strong start.
Parquet File Anatomy
Parquet files are important in Direct Lake models. You should know how they work to store and use data better. The table below shows why Parquet files are useful:
Note: Parquet files help you get answers faster and save space. This is very helpful when you have lots of data.
Data Connection
It is important to connect your Direct Lake model to the right data sources. You need to pick formats that work well. You also want to keep your data safe. Fast data movement and smooth updates are important too.
Supported Formats
Direct Lake works with certain file formats. These formats help you handle big datasets. You can use them to store and get your data easily.
Pick Parquet or Delta Parquet for your data lake. These formats give you quick results and save space. They help you work with lots of data. Your queries will run well and not slow down.
Secure Access
It is very important to keep your data safe. You can use different security protocols to control who can see your data.
Use Row-Level Security so each user sees only their data.
Encrypt your data when it is stored and when it moves.
Share datasets in Power BI to control who can use them.
Encryption keeps your data safe from bad actors. This protects private information and helps people trust your data model.
Transcoding and Framing
You can make things faster with transcoding and framing. Transcoding changes your data into a format Direct Lake reads fast. Framing helps your model update quickly when new data comes in.
Learn how to set up your model before you begin.
Know how Parquet files store your data.
Use transcoding to speed up queries without loading all data.
Use framing to refresh your model with new changes.
Direct Lake uses these steps to work with big datasets. It keeps your analytics current. A Comprehensive Guide will help you learn these steps for the best results.
Model Configuration
Direct Lake Settings
You have to set up your Direct Lake model the right way. First, look at your tables and take out columns you do not need. This makes your model faster and saves memory. Next, check your data types. Try not to use big strings or numbers with too many decimals. This helps your queries use less memory. Make tables that add up data you use a lot. These tables help your reports run fast.
Here are steps to make your Direct Lake model better:
Take out columns you do not need.
Avoid big strings and numbers with lots of decimals.
Make tables that add up data you check often.
Use security settings like Row-Level Security, Column-Level Security, and Object-Level Security.
Try not to run extra queries to save computer power.
Plan how much computer power your users can use.
Set up report views that use query caching.
Tip: Making tables smaller and picking the right data types helps your model work well with big data.
SQL Fallback
Sometimes, your Direct Lake model has to use Direct Query mode. This happens if you use warehouse views, set row-level security in the warehouse, or hit the compute limit for your SKU. You can pick how this works with semantic model settings.
You can choose from these ways for Direct Lake to work:
Automatic: The model uses Direct Lake if it can.
Direct Lake Only: The model only uses Direct Lake. If it cannot, you get an error.
Direct Query Only: The model always uses Direct Query.
Note: Pick Automatic for more choices. Your model will change modes only when it needs to.
Security Measures
You need to keep your data safe all the time. Set up security roles to use row-level security. Give the right people permission to see each Fabric item. Use cloud connections to control who can get in. Always check permissions before you share your models and reports.
Set up security roles for row-level security.
Give permission for each Fabric item.
Use cloud connections to control who gets in.
Check permissions before you share.
🔒 Good security keeps your data safe and helps people trust your model.
Model Building and Optimization
Semantic Model Design
You need to plan your semantic model carefully. Direct Lake models have special rules in Microsoft Fabric. You cannot use calculated columns or tables in Lakehouse. This means you must set up business logic outside the semantic model. If you use views for calculated columns, Direct Lake will change to DirectQuery mode. This can make your reports slower. Composite models do not work with Direct Lake, so keep your tables simple.
Tip: Use clean tables and clear links in your model. Put your business logic in your data lake or ETL steps.
You should use a star schema design. Put facts in one table and dimensions in others. This makes your model easy and fast to use. Take out columns you do not need. Use simple data types. Do not use long text or numbers with many decimals. This keeps your model small and quick.
Performance Tuning
You want your Direct Lake model to be fast. DirectLake mode lets you read Delta tables right away. This makes queries faster and skips copying data. You should pick the best data access modes. Set row limits to keep queries quick. Use smart query tricks for better results.
Pick the best data access modes
Set row limits
Use smart query tricks
You can split big fact tables by year. This makes each table smaller. Direct Lake works best with tables under 3 billion rows. If a table has more than 3 billion rows, it will use DirectQuery mode. This can slow down your queries.
Split big fact tables by year.
Use DirectLake mode for tables under 3 billion rows.
Watch for tables over 3 billion rows. These will use DirectQuery.
Note: DirectLake mode gives fast answers for big data. You do not need to move or copy data.
You should keep your Delta tables healthy. Use the VACUUM command to clean old files. Use the OPTIMIZE command to join small files into bigger ones. V-Order helps compress data and speed up queries. Deletion vectors help manage old data. Pre-warm your cache to make reports load faster.
⚡ Good maintenance and smart design keep your model working well.
Volume Management
You must handle lots of data carefully. Direct Lake reads data straight from Parquet files in OneLake. You do not need Power BI datasets. Reports update as soon as the Parquet file changes. This gives you fast updates and high data flow.
Pre-warm the cache by loading columns you use often during refresh.
Learn how Power BI stores data to make your model faster.
Use Direct Lake mode to read from Parquet files in OneLake.
Get instant report updates when your Parquet files change.
Enjoy fast performance like import mode without extra steps.
You can use REST API to pre-warm your cache. Set up cache refreshes to keep data ready. Use incremental refresh to update only new data. Warm up your cache by hand if you need quick access.
Find columns you use most.
Use REST API for pre-warming.
Set up cache refreshes.
Use incremental refresh for new data.
Warm up your cache by hand when needed.
🚀 Pre-warming and smart refresh plans help you handle big data and keep reports fast.
You should know how Power BI stores and gets data. This helps you make your model fast and reliable. Direct Lake mode lets you work with huge data easily. You get quick queries and instant updates, even with petabytes of data.
Validation and Maintenance
Testing Strategies
You must test your Direct Lake model before using it. First, check if your data connections work. Make sure each table loads with no errors. Run some sample queries to see if results are right. Test your security settings too. Log in as different users to check if they only see their own data. Do performance checks. See how fast reports load and how quick queries finish. If things are slow, look for big tables or long text columns. Fix these by removing extra columns or splitting large tables.
Tip: Test your model after every change. This helps you find problems early.
Maintenance Best Practices
Keep your Direct Lake model healthy with regular care. Plan times to check your data sources. Update your metadata when you add new tables or columns. Clean up old files with VACUUM and OPTIMIZE commands. These make your model faster and save space. Watch your cache. Pre-warm it for columns you use a lot. Set up incremental refresh to update only new data. Check your security roles every month. Remove users who do not need access anymore.
Check data sources every week
Update metadata after changes
Clean up files with VACUUM and OPTIMIZE
Watch and pre-warm cache
Refresh data in small steps
Review security roles each month
🛠️ Regular care keeps your model fast and safe.
Migration Process
If you want to move your models to Direct Lake, follow clear steps. Start with a discovery phase. Look at your current analytics and set business goals. Join a workshop to learn about Direct Lake features. Next, check your models. Use automation tools to see if your semantic models are ready to move. List your reports and tables. Study how they connect and depend on each other. Check if your models can move easily or if you need to change them. Make a report about how easy it is to move and write a plan.
Remember, Direct Lake mode has some limits compared to Import mode. Moving your model may be easy or hard based on its features. Always learn about possible problems before you start. Careful planning helps you move smoothly and avoid surprises.
Advanced Topics
New Features
Direct Lake has new features that make work easier. Now, Microsoft Fabric lets you change semantic models live in Direct Lake mode with Power BI Desktop. You do not have to wait for refreshes. You see your changes right away. This helps you test and improve models faster.
Change semantic models live in Direct Lake mode with Power BI Desktop (Preview)
See updates as soon as you make them
Try new ideas and fix problems fast
Tip: Use live editing to check your changes and make your models better right away.
Troubleshooting
You might have some problems when building Direct Lake models. You can fix these problems with easy steps. The table below shows some problems and how to solve them:
Note: Always check your tables for old dates or double links. Use Python notebooks to make simple tables for better speed.
Elevating Modelling Skills
You can get better by learning advanced skills. Direct Lake works well with code notebooks. These notebooks use PySpark, Scala, Spark SQL, and SparkR. You write code to manage and shape your data. Many Power BI users do not know these languages, but learning them helps you use Direct Lake better.
“Unlike dataflows and pipelines that give a GUI for both data professionals and casual users, notebooks are only code. They use PySpark, Scala, Spark SQL, and SparkR. Most people, especially Power BI users, do not know these languages.”
You can start by trying simple code in notebooks. You will see how coding gives you more control over your data. Practice often and ask for help when you need it. This will help you become an expert in advanced data modelling.
You now know how to build, improve, and take care of Direct Lake models. It is important to manage your models well. Use things like automatic data checks and good metadata plans. Always think about security and make your models run fast. Plan for using more than one cloud if needed.
Here are some tips to help you do well:
Give workspace access only to people who should have it.
Make models with Power BI Desktop or in Fabric’s browser.
Work with data engineers to set rules.
You can learn more by watching the Microsoft Fabric Video Tutorial on LinkedIn Learning.
FAQ
How do you connect Power BI to Direct Lake?
You open Power BI Desktop. You select "Get Data." You choose "Lakehouse" from Microsoft Fabric. You pick your workspace and lakehouse. You load tables stored in Parquet or Delta format.
What file formats work best with Direct Lake?
You should use Parquet or Delta Parquet files. These formats store data by columns. They help you run fast queries and save space. Direct Lake reads these files directly from OneLake.
How do you keep your Direct Lake model secure?
You set up Row-Level Security and Column-Level Security. You encrypt your data at rest and in transit. You give access only to people who need it. You check permissions before sharing reports.
What should you do if your queries run slow?
You check for large tables or columns with long text. You remove extra columns. You split big tables by year. You use the OPTIMIZE and VACUUM commands to clean up files.
Can you edit Direct Lake models in Power BI Desktop?
You can edit semantic models live in Direct Lake mode using Power BI Desktop (Preview). You see changes right away. You test and improve your model without waiting for refreshes.