Data engineering teams today face a pressing challenge: nearly 70% of their time is spent just preparing data, not analyzing it. Fragmented tools, duplicated storage, and disconnected environments continue to slow development cycles. Meanwhile, demand for scalable, real-time pipelines and unified analytics keeps rising.
To address these inefficiencies, platforms that consolidate the analytics workflow have surged in adoption—none faster than Microsoft Fabric .
As of April 2025, Microsoft Fabric is used by over 21,000 paying organizations, including more than 70% of the Fortune 500. It’s become the platform of choice for managing everything—from lakehouses and data warehouses to real-time analytics and AI—in one environment. A key part of this shift is Fabric’s integration with Apache Spark, which now allows PySpark notebooks to connect directly to the Fabric Warehouse without external workarounds.
In this blog, we’ll look at how PySpark fits into the Microsoft Fabric Warehouse environment and how it can simplify data engineering tasks—from exploration to data movement—within a unified workflow.
What Is PySpark Notebook in Microsoft Fabric Warehouse? A PySpark Notebook in Microsoft Fabric is a browser-based development environment where users can write and execute Apache Spark code using Python. It is part of Fabric’s Data Engineering workload and designed to support scalable, distributed data processing without managing infrastructure.
Key Capabilities When connected to a Fabric Warehouse, PySpark notebooks enable users to:
Read data directly from warehouse tables using Spark DataFrames Run transformations and filters on large datasets using PySpark syntax Write results back into warehouse tables using a native connector Operate within a unified environment, without exporting data or switching tools This integration eliminates the need for staging via Lakehouse or running post-processing SQL queries outside Spark. It enables full read-write support, making PySpark a natural choice for building end-to-end data preparation and ETL workflows within Fabric.
By combining structured data management and big data compute in one workflow, PySpark notebooks in Microsoft Fabric provide a seamless bridge between engineering, analytics, and reporting.
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Setting Up the Environment for PySpark in Microsoft Fabric Before using PySpark to interact with Microsoft Fabric Warehouse, it’s important to configure your environment correctly. The key requirement here is switching to the appropriate Spark runtime version that supports warehouse write-back capabilities.
1. Access Your Fabric Workspace Start by navigating to app.powerbi.com and opening the workspace where you want to run your PySpark notebook.
In this example, the workspace used is:
Workspace Name: 01 – GA fabric
You can find and open it from your list of available workspaces.
2. Configure the Runtime Environment To enable full support for the Spark-Warehouse connector (especially for write operations), you need to upgrade the Spark runtime to version 1.3.
In your workspace, go to the Settings panel (top-right corner). Scroll down to Data Engineering or Data Science Spark settings. Locate the Environment configuration section. Set the Runtime version to: This runtime version is essential, as the older 1.2 environment does not support the df.write.synapsql() command used to write data back to the Fabric Warehouse.
3. Create a New PySpark Notebook Once your workspace is set to Runtime 1.3:
Click New > Notebook under the Prepare Data section. Name your notebook and assign it to the same workspace. Connect it to a Lakehouse—this is required for initializing the notebook session, even if your end goal is to work with a Fabric Warehouse.
With the notebook created and the environment correctly configured, you’re ready to start importing the necessary libraries and connecting to your Fabric Warehouse using PySpark.
Importing Required Libraries for Spark–Fabric Integration With your PySpark notebook created and the environment set to Runtime 1.3, the next step is to import the required libraries that enable the Spark connector to communicate with Microsoft Fabric Warehouse.
These imports are necessary to access key functions and constants used for both reading and writing to warehouse tables.
Required Imports At the top of your notebook, add the following import statements:
The first import enables core Spark-Fabric integration classes. The second import brings in predefined constants used for operations like setting workspace ID or configuring options during read/write.
These imports are lightweight and don’t require manual installation. They are bundled by default in Fabric Runtime 1.3, so you can use them immediately without installing external libraries.
Spark Session Initialization When you run any cell in the notebook for the first time, Fabric automatically starts a Spark session in the background. This session:
Allocates compute resources Initializes the runtime environment Keeps the session active for subsequent cells (until explicitly restarted)
Once the Spark session is active and the libraries are imported successfully, you’re ready to start interacting with your Fabric Warehouse.
Preparing the Target Warehouse for Testing Before testing read and write operations using PySpark, it’s helpful to clean up your Fabric Warehouse environment. This ensures you’re working with a known state and avoids naming conflicts when re-creating tables.
1. Open the Target Warehouse This warehouse contains all your structured tables used for SQL-based operations.
2. Identify and Remove the Test Table If you’re re-running a test or writing a table with an existing name, delete the old table to avoid conflicts.
In the warehouse explorer, locate the test table (e.g., pyspark_customer). Use the built-in query editor to run a simple drop command:
Execute the command and refresh the schema to confirm that the table has been removed.
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3. Confirm Clean State After refreshing, verify that the table no longer appears under the schema browser. This clears the way to re-create the table during your PySpark write test.
This preparation step is optional but highly recommended when testing writing capabilities—especially if the notebook code creates a table with the same name each time.
How to Read Data from Microsoft Fabric Warehouse Using PySpark With the notebook set up and the Spark session initialized, the next step is to read data directly from tables in your Fabric Warehouse using PySpark.
This is done using the spark.read.snapsql() method, which allows you to query a warehouse table and load the result into a Spark DataFrame.
1. Reading a Table from the Current Workspace To read a table within the same workspace as your notebook:
warehouse01 is the name of the Fabric Warehouse. dbo.customer refers to the schema and table name. The display(df) function renders the DataFrame output inside the notebook. This method allows you to pull full table contents directly into Spark for analysis or transformation.
2. Reading a Table from a Different Workspace If the table you want to query resides in another workspace, you need to provide the workspace ID explicitly.
Step-by-step: Copy the workspace ID from the URL of the source workspace. It appears after /groups/ in the Fabric URL. Use the .option(Constant.WORKSPACE_ID, “<workspace_id>”) method before calling snapsql(). Example:
This allows your notebook to access tables across workspaces securely.
3. Limiting Rows While Reading To fetch only a subset of records (useful for sampling or testing), apply the .limit() method directly to the read command:
This will return only the first two rows from the item table.
4. Applying Row-Level Filters You can also filter the results using typical PySpark filtering syntax:
This reads the full table and returns only the rows where item_id equals 1.
These capabilities let you query Fabric Warehouse tables with ease—whether you’re doing exploratory data analysis , testing queries, or preparing data for transformation within your Spark notebook.
Reading Data Across Microsoft Fabric Workspaces In Microsoft Fabric , PySpark notebooks can access data from warehouses located in other workspaces, not just the one the notebook is assigned to. This is especially useful when working in multi-team or cross-functional environments where data is distributed across projects.
To read from a warehouse table in another workspace, you need to include that workspace’s ID in your Spark read operation.
1. Locate the Workspace ID To find the Workspace ID:
Navigate to the target workspace in your browser. Look at the URL in the address bar—it will look like this:
2. Use the Workspace ID in Your PySpark Command Now use this ID with spark.read.option() before the snapsql() function:
Replace “your-workspace-id” with the actual value from the URL. Replace warehouse01.dbo.geography with your desired table path. 3. Validate the Output Once the command is run:
The Spark engine queries the specified warehouse table across workspaces. The result is loaded into a DataFrame and rendered with display(). This functionality makes it easy to build centralized notebooks that consume data across organizational units, without duplicating storage or creating unnecessary copies.
Steps to Apply Query Constraints on Reads Working with large datasets in Microsoft Fabric Warehouse often requires reading only what’s needed—whether for performance, testing, or data quality checks. PySpark notebooks provide multiple ways to control how much data is pulled from the warehouse and how it’s shaped before processing.
Here are five ways to constrain your reads using PySpark:
Step 1: Limit the Number of Rows Use .limit(n) to fetch a small sample of rows—ideal for validation, schema inspection, or notebook prototyping.
This retrieves just the first 5 rows of the item table.
Step 2: Apply Row-Level Filters Use .filter() to restrict the DataFrame to only rows matching a condition:
This is useful when isolating data for a specific item, customer, or condition.
Step 3: Chain Filters and Limits Together Filters and limits can be combined in sequence for even finer-grained reads:
This returns 10 records where the item belongs to the “Grocery” category.
Step 4: Use Multiple Filter Conditions You can apply compound logic using standard SQL-style expressions inside the .filter() method:
This targets rows where both conditions are met—very useful for slicing operational or transactional data .
Step 5: Assign to Temporary DataFrames for Reuse When constraints are complex or used multiple times, it’s best to assign the result to a new DataFrame for reuse:
These query constraints not only help optimize resource usage but also speed up debugging and testing—allowing Spark to process only what’s necessary inside your Fabric Warehouse connection.
Writing Data Back to Microsoft Fabric Warehouse Once your transformations are complete in PySpark, the next step is to persist the results back to a Microsoft Fabric Warehouse table. This allows processed data to be queried via SQL or used in downstream reports and dashboards.
Fabric Runtime 1.3 enables native write support using a simple .write.synapsql() method—making it possible to push Spark DataFrames directly into the structured warehouse layer.
1. Prepare a Transformed or Filtered DataFrame Before writing, you’ll typically start with a filtered or transformed DataFrame. For example:
This prepares a subset of customer data where geography_key = 1.
2. Write the DataFrame to the Warehouse Use the synapsql() write method to write the DataFrame back into a table within the same warehouse:
If the table does not exist, it will be created automatically. If the table already exists, and no overwrite mode is specified, the write may fail. The target path must follow the format: warehouse.schema.table. This command is only supported on Spark Runtime 1.3 or later.
3. Validate the Write Operation in the Fabric UI After the write completes:
Open the Warehouse in Fabric (e.g., WH01). Click Refresh in the table browser pane. You should now see the newly created table (e.g., pyspark_customer) listed under your schema. This confirms that Fabric has accepted the write and registered the new table.
4. Run SQL Queries to Inspect the Results To confirm that the correct data has been written, run a simple SQL query inside the warehouse’s built-in query editor:
5. Write Again Using a Different Filter You can repeat the process using a new filter or transformation to write to a separate table:
This lets you persist with multiple versions of the dataset with different segmentation logic.
6. Considerations When Writing to Fabric Warehouse Supported only on Runtime 1.3+ Tables are created using schema inferred from the Spark DataFrame Overwriting existing tables is not supported unless explicitly handled Data is staged internally and written efficiently via COPY INTO under the hood
By using write.synapsql(), data engineers can complete the loop—moving from Spark-based processing back to SQL-ready tables—all within Microsoft Fabric’s managed environment.
Developer’s Use Case in Microsoft Fabric For data engineers and developers building on Spark, PySpark notebooks in Microsoft Fabric now serve as a complete development and execution layer within the platform. Instead of exporting data to external tools or switching between environments, teams can handle everything—from reading raw tables to publishing transformed outputs—directly within the notebook.
Key use cases include: Building ETL pipelines that read from Lakehouse or Warehouse, apply Spark transformations, and write back curated datasets. Testing data logic and filters interactively before operationalizing pipelines. Segmenting or enriching data using Spark’s distributed compute and persisting it in Fabric Warehouse for downstream Power BI dashboards . Collaborating in shared notebooks across teams, with versioned code and direct access to enterprise datasets.
This functionality bridges the gap between big data processing and structured analytics—letting Spark-native workflows plug directly into the governed, SQL-ready warehouse layer in Microsoft Fabric.
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FAQs Can I write data from PySpark directly into Microsoft Fabric Warehouse? Yes, starting with Fabric Runtime 1.3, you can write PySpark DataFrames directly to a Fabric Warehouse using the .write.synapsql() method. This makes it easy to move data from Spark into your warehouse tables without needing extra steps or conversions.
Is it necessary to connect a notebook to a Lakehouse even if I only work with a Warehouse? Yes, a connection to a Lakehouse is still required when you create a new notebook. This is because Spark needs a Lakehouse to start the session. But after the notebook is up and running, you’re free to work only with Warehouse tables. You don’t have to read from or write to the Lakehouse if you don’t need it.
How do I read from a warehouse in another workspace? To access tables from a different workspace, add the .option(Constant.WORKSPACE_ID, “”) line before your .snapsql() call. You can find the workspace ID in the URL of the workspace you want to connect to. This lets you reach across workspaces and use shared data.
Can I overwrite an existing warehouse table using PySpark? Overwriting an existing warehouse table isn’t supported by default. If you try to write to a table that already exists, the operation will fail unless you handle it manually . To avoid issues, you should either drop the existing table first or write your data to a new table altogether.
What Spark version is required for Fabric Warehouse read/write? Fabric Runtime 1.3 is the minimum version that supports warehouse read and write. It comes with Spark 3.5 and Delta Lake 3.2. Older runtimes, like 1.2, don’t support writing to warehouse tables, so make sure you’re using the correct version if you plan to use this feature
Do I need to install any external packages to use the Fabric Spark connector? No extra packages are needed. The necessary libraries, such as com.microsoft.spark.fabric and its constants, are already included in the Fabric runtime. Just make sure to import them at the top of your notebook to use them properly.
Can I filter or limit data when reading from a warehouse in PySpark? Yes. After calling .snapsql() to read from the warehouse, you can use typical DataFrame methods like .filter() and .limit() to narrow down the data. This helps manage memory usage and improves performance when you don’t need the full table.
Can we use PySpark in fabric? Yes, you can use PySpark in Microsoft Fabric through its Synapse Data Engineering experience, which provides a fully managed Spark environment. Fabric supports PySpark natively within notebooks, allowing you to write and execute Python-based Spark code for large-scale data transformation, processing, and analytics. You can connect PySpark notebooks to Fabric Warehouses and Lakehouses using the built-in SQL endpoint or the `notebookutils` library to read and write data. Fabric automatically handles Spark cluster provisioning, so there is no manual infrastructure setup required. You can also leverage Delta Lake format, run SQL queries alongside PySpark code in the same notebook, and integrate with Fabric pipelines for orchestration. This makes PySpark in Fabric a practical choice for teams building scalable data engineering workflows without managing separate Spark infrastructure.
What is PySpark notebook? A PySpark notebook is an interactive, web-based development environment that combines Python code with Apache Spark’s distributed computing engine, allowing you to process large-scale data through a cell-by-cell execution model. Each cell can contain code, markdown, or visualizations, making it easy to explore, transform, and analyze data iteratively. In Microsoft Fabric, PySpark notebooks connect directly to the Spark compute engine, letting you run distributed data processing jobs against warehouse tables, lakehouses, and other Fabric data sources without managing infrastructure. This makes them practical for ETL pipelines, exploratory data analysis, and machine learning workflows at scale. Kanerika leverages PySpark notebooks within Microsoft Fabric to build efficient, scalable data engineering solutions that integrate smoothly with enterprise warehousing and analytics environments.
How to create a notebook in Microsoft Fabric? To create a notebook in Microsoft Fabric, navigate to your workspace, click New, and select Notebook from the item creation menu. This opens an interactive environment where you can write and execute PySpark, Python, Scala, or SQL code against your Fabric data sources. Once the notebook is created, attach it to a lakehouse or warehouse by selecting your data source from the left Explorer panel. You can also create notebooks directly from within a lakehouse by clicking Open Notebook in the ribbon. Each notebook supports multiple cells, inline visualizations, and parameterization for pipeline integration. For teams working with Microsoft Fabric Warehouse specifically, connecting the notebook to the warehouse endpoint lets you run PySpark queries against warehouse tables, making it a practical alternative to SQL Analytics for complex data transformation workflows.
Can Spark notebooks write data to a warehouse in fabric? Yes, Spark notebooks can write data to a Microsoft Fabric Warehouse using SQL analytics endpoints or by leveraging the warehouse connector available within the Fabric environment. You can use PySpark to transform data and then write it directly to warehouse tables using the `spark.write` method with the appropriate JDBC connection, or use the built-in Fabric shortcuts and connectors that simplify the process. The notebook reads from your source, applies transformations using PySpark’s distributed processing capabilities, and commits the results to warehouse tables that are immediately queryable via T-SQL. This approach is particularly useful for ETL pipelines where you need Spark’s processing power for complex transformations before landing clean, structured data into the warehouse for downstream analytics and reporting.
Does fabric support Spark? Microsoft Fabric supports Apache Spark natively through its Synapse Data Engineering and Data Science experiences. Fabric provides a fully managed Spark runtime environment where you can create Spark notebooks, build data pipelines, and run large-scale data transformations without managing infrastructure. The platform includes optimized Spark pools, Delta Lake integration, and support for Python, Scala, R, and SQL. Within the Fabric ecosystem, PySpark notebooks connect directly to Lakehouse storage and can read from or write to Warehouse tables using cross-experience queries. Fabric’s Spark environment also supports MLflow for experiment tracking, making it suitable for both engineering and machine learning workloads. Teams working on unified analytics benefit from Fabric’s tight integration between Spark compute and OneLake storage, reducing data movement and simplifying architecture across ingestion, transformation, and serving layers.
Which IDE is best for PySpark? Visual Studio Code with the PySpark extension is widely considered the best IDE for PySpark development, offering syntax highlighting, autocomplete, and seamless integration with Databricks or Fabric environments. Jupyter Notebook and JupyterLab remain popular choices for interactive data exploration since they let you run cells incrementally and visualize outputs inline. For enterprise-scale work on Microsoft Fabric specifically, the built-in Fabric notebook environment is often the most practical option because it comes pre-configured with Spark runtimes, Delta Lake support, and direct warehouse connectivity — eliminating environment setup overhead. DataBricks notebooks offer similar convenience for teams on that platform. The right choice depends on your workflow: VS Code suits developers who prefer local editing with version control, while cloud-based notebooks like Fabric’s serve data engineers who prioritize collaboration and fast iteration on distributed data pipelines.
Is fabric better than Databricks? Whether Microsoft Fabric is better than Databricks depends on your existing tech stack and specific use case. Fabric integrates natively with Microsoft 365, Azure, and Power BI, making it a stronger choice for organizations already invested in the Microsoft ecosystem. Databricks, on the other hand, offers more mature MLflow integration, deeper machine learning capabilities, and broader multi-cloud support across AWS, Azure, and GCP. For data engineering tasks like PySpark notebooks in a warehouse environment, both platforms are capable, but Fabric’s unified lakehouse architecture reduces the need for multiple separate tools. Databricks still leads in advanced ML workloads and has a more established community. If your team is Microsoft-centric and wants a consolidated platform without managing separate services, Fabric offers better value. For ML-heavy or multi-cloud workloads, Databricks remains the stronger option. Kanerika’s experts can help you make an informed decision.
Is PySpark good for ETL? PySpark is excellent for ETL workloads, particularly when dealing with large-scale data transformation across distributed systems. It handles batch processing, streaming data, and complex transformations efficiently by distributing computation across multiple nodes, making it far faster than single-machine alternatives like pandas for high-volume datasets. For ETL pipelines, PySpark offers built-in support for reading from and writing to diverse data sources including databases, data lakes, and file formats like Parquet, Delta, and CSV. Its DataFrame API simplifies data cleansing, filtering, joins, and aggregations, while fault tolerance through RDD lineage ensures reliability in production pipelines. In Microsoft Fabric, PySpark notebooks integrate directly with the Warehouse and Lakehouse, making ETL development more streamlined. Kanerika leverages this capability to build scalable, maintainable ETL pipelines that reduce data processing time and support downstream analytics workloads effectively.
Is PySpark harder than Pandas? PySpark has a steeper learning curve than Pandas, but it isn’t necessarily harder once you understand distributed computing concepts. Pandas uses a simpler, more intuitive syntax and works well for datasets that fit in memory on a single machine. PySpark requires understanding of cluster architecture, lazy evaluation, and distributed data partitioning, which adds initial complexity. However, for large-scale data processing in environments like Microsoft Fabric, PySpark becomes the more practical choice since Pandas struggles with datasets exceeding available RAM. If you already know Pandas, transitioning to PySpark is manageable because many DataFrame operations share similar logic. The key adjustment is shifting from eager execution to PySpark’s lazy evaluation model, where transformations only execute when an action is triggered. For warehouse-scale workloads, the investment in learning PySpark pays off through significantly better performance and scalability.
Is PySpark an API or a library? PySpark is a Python API for Apache Spark, not a standalone library — though it functions like one when installed as a Python package. It exposes Spark’s distributed computing engine through Python bindings, letting you write Spark jobs using familiar Python syntax. The distinction matters: as an API, PySpark serves as an interface layer that translates Python code into Spark’s underlying JVM-based execution engine. When you run PySpark in environments like Microsoft Fabric’s notebook interface, you’re using this API to interact with Spark clusters, process large datasets across distributed nodes, and execute transformations at scale — all without leaving the Python ecosystem.
What is PySpark used for? PySpark is used for distributed data processing, meaning it lets you run Python code across clusters of machines to handle datasets too large for a single computer. It combines Python’s simplicity with Apache Spark’s parallel computing engine, making it practical for ETL pipelines, large-scale data transformations, machine learning workflows, and real-time streaming analytics. Data engineers commonly use PySpark to clean, aggregate, and move data between storage layers in modern lakehouses and warehouses. In Microsoft Fabric, PySpark runs inside notebooks connected to Spark compute pools, letting you query and transform warehouse data at scale without hitting the memory limits of single-node tools like pandas.
Is Databricks in Microsoft Fabric? Databricks is not natively part of Microsoft Fabric, but the two platforms can work together. Microsoft Fabric is Microsoft’s own unified analytics platform that includes its own Apache Spark runtime, meaning you do not need Databricks to run PySpark workloads inside Fabric. Fabric provides built-in Spark compute through its Lakehouse and notebook experiences, which handle most distributed data processing tasks that Databricks would traditionally cover. That said, organizations already invested in Databricks can connect it to Fabric through Azure integration patterns, sharing data via OneLake or Delta Lake formats. For teams building PySpark notebooks in Fabric Warehouse specifically, the native Fabric Spark environment is the intended and fully supported path, removing the dependency on external Databricks clusters entirely.
What is a PySpark notebook? A PySpark notebook is an interactive development environment that combines Python code with Apache Spark’s distributed computing engine, allowing you to process large datasets across multiple nodes in a cluster. It runs in a cell-based interface where you write and execute code incrementally, view outputs inline, and iterate quickly without redeploying entire scripts. Each cell can contain Python, SQL, or markdown, making it useful for both data engineering and exploratory analysis. In Microsoft Fabric, PySpark notebooks connect directly to Fabric’s Spark runtime, letting you read from and write to Warehouses, Lakehouses, and Delta tables using familiar DataFrame syntax. This makes them a practical tool for ETL pipelines, data transformation, and large-scale analytics where pandas alone would hit memory limits.
Is PySpark or sql faster? PySpark and SQL perform comparably in Microsoft Fabric Warehouse because both ultimately execute as distributed Spark jobs under the hood. SQL tends to be faster for simple aggregations, joins, and filtering on structured data because the query optimizer handles execution planning automatically with minimal overhead. PySpark offers more control over complex transformations, iterative machine learning workflows, and multi-step data pipelines where you need procedural logic that pure SQL cannot express cleanly. In practice, well-written SQL often outperforms poorly optimized PySpark code, while PySpark with proper partitioning and caching can surpass SQL on large-scale transformations. For most warehouse workloads in Microsoft Fabric, the performance difference is marginal — choosing between them should come down to use case complexity and team expertise rather than raw speed alone.
Is PySpark a tool or framework? PySpark is a framework, not a standalone tool — it is the Python API for Apache Spark, an open-source distributed computing engine designed for large-scale data processing. As a framework, PySpark provides a structured programming model with libraries for SQL queries, streaming, machine learning, and graph processing. It gives developers a set of abstractions and APIs to build data pipelines and analytics workflows without managing low-level cluster operations directly. In Microsoft Fabric, PySpark runs within notebook environments connected to Spark compute pools, making it straightforward to process warehouse data at scale. The distinction matters because frameworks like PySpark define how you structure your code and interact with the underlying engine, whereas tools are typically purpose-built utilities for specific tasks.
