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Dimensional modeling in Microsoft Fabric Warehouse: Load tables

  • Article

Applies to: ✅ SQL analytics endpoint and Warehouse in Microsoft Fabric

Note

This article forms part of the Dimensional modeling series of articles. This series focuses on guidance and design best practices related to dimensional modeling in Microsoft Fabric Warehouse.

This article provides you with guidance and best practices for loading dimension and fact tables in a dimensional model. It provides practical guidance for Warehouse in Microsoft Fabric, which is an experience that supports many T-SQL capabilities, like creating tables and managing data in tables. So, you're in complete control of creating your dimensional model tables and loading them with data.

Note

In this article, the term data warehouse refers to an enterprise data warehouse, which delivers comprehensive integration of critical data across the organization. In contrast, the standalone term warehouse refers to a Fabric Warehouse, which is a software as a service (SaaS) relational database offering that you can use to implement a data warehouse. For clarity, in this article the latter is mentioned as Fabric Warehouse.

Tip

If you're inexperienced with dimensional modeling, consider this series of articles your first step. It isn't intended to provide a complete discussion on dimensional modeling design. For more information, refer directly to widely adopted published content, like The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd edition, 2013) by Ralph Kimball, and others.

Load a dimensional model

Loading a dimensional model involves periodically running an Extract, Transform, and Load (ETL) process. An ETL process orchestrates the running of other processes, which are generally concerned with staging source data, synchronizing dimension data, inserting rows into fact tables, and recording auditing data and errors.

For a Fabric Warehouse solution, you can use Data Factory to develop and run your ETL process. The process can stage, transform, and load source data into your dimensional model tables.

Specifically, you can:

  • Use data pipelines to build workflows to orchestrate the ETL process. Data pipelines can execute SQL scripts, stored procedures, and more.
  • Use dataflows to develop low-code logic to ingest data from hundreds of data sources. Dataflows support combining data from multiple sources, transforming data, and then loading it to a destination, like a dimensional model table. Dataflows are built by using the familiar Power Query experience that's available today across many Microsoft products, including Microsoft Excel and Power BI Desktop.

Note

ETL development can be complex, and development can be challenging. It's estimated that 60-80 percent of a data warehouse development effort is dedicated to the ETL process.

Orchestration

The general workflow of an ETL process is to:

  1. Optionally, load staging tables.
  2. Process dimension tables.
  3. Process fact tables.
  4. Optionally, perform post-processing tasks, like triggering the refresh of dependent Fabric content (like a semantic model).

Diagram shows the four steps of the ETL process as described in the previous paragraph.

Dimension tables should be processed first to ensure that they store all dimension members, including those added to source systems since the last ETL process. When there are dependencies between dimensions, as is the case with outrigger dimensions, dimension tables should be processed in order of dependency. For example, a geography dimension that's used by a customer dimension and a vendor dimension should be processed before the other two dimensions.

Fact tables can be processed once all dimension tables are processed.

When all dimensional model tables are processed, you might trigger the refresh of dependent semantic models. It's also a good idea to send a notification to relevant staff to inform them of the outcome of the ETL process.

Stage data

Staging source data can help support data loading and transformation requirements. It involves extracting source system data and loading it into staging tables, which you create to support the ETL process. We recommend that you stage source data because it can:

  • Minimize the impact on operational systems.
  • Be used to assist with, and optimize, ETL processing.
  • Provide the ability to restart the ETL process, without the need to reload data from source systems.

Data in staging tables should never be made available to business users. It's only relevant to the ETL process.

Note

When your data is stored in a Fabric Lakehouse, it might not be necessary to stage its data in the data warehouse. If it implements a medallion architecture, you could source its data from either the bronze, silver, or gold layer.

We recommend that you create a schema in the warehouse, possibly named staging. Staging tables should resemble the source tables as closely as possible in terms of column names and data types. The contents of each table should be removed at the start of the ETL process. However, note that Fabric Warehouse tables can't be truncated. Instead, you can drop and recreate each staging table before loading it with data.

You can also consider data virtualization alternatives as part of your staging strategy. You can use:

Transform data

The structure of your source data might not resemble the destination structures of your dimensional model tables. So, your ETL process needs to reshape the source data to align with the structure of the dimensional model tables.

Also, the data warehouse must deliver cleansed and conformed data, so source data might need to be transformed to ensure quality and consistency.

Note

The concept of garbage in, garbage out certainly applies to data warehousing—therefore, avoid loading garbage (low quality) data into your dimensional model tables.

Here are some transformations that your ETL process could perform.

  • Combine data: Data from different sources can be integrated (merged) based on matching keys. For example, product data is stored across different systems (like manufacturing and marketing), yet they all use a common stock-keeping unit (SKU). Data can also be appended when it shares a common structure. For example, sales data is stored in multiple systems. A union of the sales from each system can produce a superset of all sales data.
  • Convert data types: Data types can be converted to those defined in the dimensional model tables.
  • Calculations: Calculations can be done to produce values for the dimensional model tables. For example, for an employee dimension table, you might concatenate first and last names to produce the full name. As another example, for your sales fact table, you might calculate gross sales revenue, which is the product of unit price and quantity.
  • Detect and manage historical change: Change can be detected and appropriately stored in dimension tables. For more information, see Manage historical change later in this article.
  • Aggregate data: Aggregation can be used to reduce fact table dimensionality and/or to raise the granularity of the facts. For example, the sales fact table doesn't need to store sales order numbers. Therefore, an aggregated result that groups by all dimension keys can be used to store the fact table data.

Load data

You can load tables in a Fabric Warehouse by using the following data ingestion options.

  • COPY INTO (T-SQL): This option is useful when the source data comprise Parquet or CSV files stored in an external Azure storage account, like ADLS Gen2 or Azure Blob Storage.
  • Data pipelines: In addition to orchestrating the ETL process, data pipelines can include activities that run T-SQL statements, perform lookups, or copy data from a data source to a destination.
  • Dataflows: As an alternative to data pipelines, dataflows provide a code-free experience to transform and clean data.
  • Cross-warehouse ingestion: When data is stored in the same workspace, cross-warehouse ingestion allows joining different warehouse or lakehouse tables. It supports T-SQL commands like INSERT…SELECT, SELECT INTO, and CREATE TABLE AS SELECT (CTAS). These commands are especially useful when you want to transform and load data from staging tables within the same workspace. They're also set-based operations, which is likely to be the most efficient and fastest way to load dimensional model tables.

Tip

For a complete explanation of these data ingestion options including best practices, see Ingest data into the Warehouse.

Logging

ETL processes usually require dedicated monitoring and maintenance. For these reasons, we recommend that you log the results of the ETL process to non-dimensional model tables in your warehouse. You should generate a unique ID for each ETL process and use it to log details about every operation.

Consider logging:

  • The ETL process:
    • A unique ID for each ETL execution
    • Start time and end time
    • Status (success or failure)
    • Any errors encountered
  • Each staging and dimensional model table:
    • Start time and end time
    • Status (success or failure)
    • Rows inserted, updated, and deleted
    • Final table row count
    • Any errors encountered
  • Other operations:
    • Start time and end time of semantic model refresh operations

Tip

You can create a semantic model that's dedicated to monitoring and analyzing your ETL processes. Process durations can help you identify bottlenecks that might benefit from review and optimization. Row counts can allow you to understand the size of the incremental load each time the ETL runs, and also help to predict the future size of the data warehouse (and when to scale up the Fabric capacity, if appropriate).

Process dimension tables

Processing a dimension table involves synchronizing the data warehouse data with the source systems. Source data is first transformed and prepared for loading into its dimension table. This data is then matched with the existing dimension table data by joining on the business keys. It's then possible to determine whether the source data represents new or modified data. When the dimension table applies slowly changing dimension (SCD) type 1, changes are made by updating the existing dimension table rows. When the table applies SCD type 2 changes, the existing version is expired and a new version is inserted.

The following diagram depicts the logic used to process a dimension table.

Diagram shows a flow that describes how new and changed source rows are loaded to a dimension table, as described in the following paragraph.

Consider the process of the Product dimension table.

  • When new products are added to the source system, rows are inserted into the Product dimension table.
  • When products are modified, existing rows in the dimension table are either updated or inserted.
    • When SCD type 1 applies, updates are made to the existing rows.
    • When SCD type 2 applies, updates are made to expire the current row versions, and new rows that represent the current version are inserted.
    • When SCD type 3 applies, a process similar to SCD type 1 occurs, updating the existing rows without inserting new rows.

Surrogate keys

We recommend that each dimension table has a surrogate key, which should use the smallest possible integer data type. In SQL Server-based environments that's typically done by creating an identity column, however this feature isn't supported in Fabric Warehouse. Instead, you'll need to use a workaround technique that generates unique identifiers.

Important

When a dimension table includes automatically generated surrogate keys, you should never perform a truncate and full reload of it. That's because it would invalidate the data loaded into fact tables that use the dimension. Also, if the dimension table supports SCD type 2 changes, it might not be possible to regenerate the historical versions.

Manage historical change

When a dimension table must store historical change, you'll need to implement a slowly changing dimension (SCD).

Note

If the dimension table row is an inferred member (inserted by a fact load process), you should treat any changes as late arriving dimension details instead of an SCD change. In this case, any changed attributes should be updated and the inferred member flag column set to FALSE.

It's possible that a dimension could support SCD type 1 and/or SCD type 2 changes.

SCD type 1

When SCD type 1 changes are detected, use the following logic.

  1. Update any changed attributes.
  2. If the table includes last modified date and last modified by columns, set the current date and process that made the modifications.

SCD type 2

When SCD type 2 changes are detected, use the following logic.

  1. Expire the current version by setting the end date validity column to the ETL processing date (or a suitable timestamp in the source system) and the current flag to FALSE.
  2. If the table includes last modified date and last modified by columns, set the current date and process that made the modifications.
  3. Insert new members that have the start date validity column set to the end date validity column value (used to update the prior version) and has the current version flag set to TRUE.
  4. If the table includes created date and created by columns, set the current date and process that made the insertions.

SCD type 3

When SCD type 3 changes are detected, update the attributes by using similar logic to processing SCD type 1.

Dimension member deletions

Take care if source data indicates that dimension members were deleted (either because they're not retrieved from the source system, or they've been flagged as deleted). You shouldn't synchronize deletions with the dimension table, unless dimension members were created in error and there are no fact records related to them.

The appropriate way to handle source deletions is to record them as a soft delete. A soft delete marks a dimension member as no longer active or valid. To support this case, your dimension table should include a Boolean attribute with the bit data type, like IsDeleted. Update this column for any deleted dimension members to TRUE (1). The current, latest version of a dimension member might similarly be marked with a Boolean (bit) value in the IsCurrent or IsActive columns. All reporting queries and Power BI semantic models should filter out records that are soft deletes.

Date dimension

Calendar and time dimensions are special cases because they usually don't have source data. Instead, they're generated by using fixed logic.

You should load the date dimension table at the beginning of every new year to extend its rows to a specific number of years ahead. There might be other business data, for example fiscal year data, holidays, week numbers to update regularly.

When the date dimension table includes relative offset attributes, the ETL process must be run daily to update offset attribute values based on the current date (today).

We recommend that the logic to extend or update the date dimension table be written in T-SQL and encapsulated in a stored procedure.

Process fact tables

Processing a fact table involves synchronizing the data warehouse data with the source system facts. Source data is first transformed and prepared for loading into its fact table. Then, for each dimension key, a lookup determines the surrogate key value to store in the fact row. When a dimension supports SCD type 2, the surrogate key for the current version of the dimension member should be retrieved.

Note

Usually the surrogate key can be computed for the date and time dimensions because they should use YYYYMMDD or HHMM format. For more information, see Calendar and time.

If a dimension key lookup fails, it could indicate an integrity issue with the source system. In this case, the fact row must still get inserted into the fact table. A valid dimension key must still be stored. One approach is to store a special dimension member (like Unknown). This approach requires a later update to correctly assign the true dimension key value, when known.

Important

Because Fabric Warehouse doesn't enforce foreign keys, it's critical that the ETL process check for integrity when it loads data into fact tables.

Another approach, relevant when there's confidence that the natural key is valid, is to insert a new dimension member and then store its surrogate key value. For more information, see Inferred dimension members later in this section.

The following diagram depicts the logic used to process a fact table.

Diagram shows a flow that describes how new source rows are loaded to a fact table, as described in the previous paragraphs.

Whenever possible, a fact table should be loaded incrementally, meaning that new facts are detected and inserted. An incremental load strategy is more scalable, and it reduces the workload for both the source systems and the destination systems.

Important

Especially for a large fact table, it should be a last resort to truncate and reload a fact table. That approach is expensive in terms of process time, compute resources, and possible disruption to the source systems. It also involves complexity when the fact table dimensions apply SCD type 2. That's because dimension key lookups will need to be done within the validity period of the dimension member versions.

Hopefully, you can efficiently detect new facts by relying on source system identifiers or timestamps. For example, when a source system reliably records sales orders that are in sequence, you can store the latest sales order number retrieved (known as the high watermark). The next process can use that sales order number to retrieve newly created sales orders, and again, store the latest sales order number retrieved for use by the next process. It might also be possible that a create date column could be used to reliably detect new orders.

If you can't rely on the source system data to efficiently detect new facts, you might be able to rely on a capability of the source system to perform an incremental load. For example, SQL Server and Azure SQL Managed Instance have a feature called change data capture (CDC), which can track changes to each row in a table. Also, SQL Server, Azure SQL Managed Instance, and Azure SQL Database have a feature called change tracking, which can identify rows that have changed. When enabled, it can help you to efficiently detect new or changed data in any database table. You might also be able to add triggers to relational tables that store keys of inserted, updated, or deleted table records.

Lastly, you might be able to correlate source data to fact table by using attributes. For example, the sales order number and sales order line number. However, for large fact tables, it could be a very expensive operation to detect new, changed, or deleted facts. It could also be problematic when the source system archives operational data.

Inferred dimension members

When a fact load process inserts a new dimension member, it's known as an inferred member. For example, when a hotel guest checks in, they're asked to join the hotel chain as a loyalty member. A membership number is issued immediately, but the details of the guest might not follow until the paperwork is submitted by the guest (if ever).

All that's known about the dimension member is its natural key. The fact load process needs to create a new dimension member by using Unknown attribute values. Importantly, it must set the IsInferredMember audit attribute to TRUE. That way, when the late arriving details are sourced, the dimension load process can make the necessary updates to the dimension row. For more information, see Manage historical change in this article.

Fact updates or deletions

You might be required to update or delete fact data. For example, when a sales order gets canceled, or an order quantity is changed. As described earlier for loading fact tables, you need to efficiently detect changes and perform appropriate modifications to the fact data. In this example for the canceled order, the sales order status would probably change from Open to Canceled. That change would require an update of the fact data, and not the deletion of a row. For the quantity change, an update of the fact row quantity measure would be necessary. This strategy of using soft deletes preserves history. A soft delete marks a row as no longer active or valid, and all reporting queries and Power BI semantic models should filter out records that are soft deletes.

When you anticipate fact updates or deletions, you should include attributes (like a sales order number and its sales order line number) in the fact table to help identify the fact rows to modify. Be sure to index these columns to support efficient modification operations.

Lastly, if fact data was inserted by using a special dimension member (like Unknown), you'll need to run a periodic process that retrieves current source data for such fact rows and update dimension keys to valid values.

Related content

For more information about loading data into a Fabric Warehouse, see: