Microsoft Business Intelligence Designer – This example scenario shows how data can be ingested into a cloud environment from an on-premises data warehouse, then served using a business intelligence (BI) model. This approach can be an end goal or a first step to full modernization with cloud-based components.
The following steps build on the Azure Synapse Analytics end-to-end scenario. It uses Azure Pipelines to ingest data from an SQL database into Azure Synapse SQL pools, and transfers the data for analysis.
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An organization has a large on-premises data warehouse stored in a SQL database. The organization wants to use Azure Synapse to perform analysis, and serve the insights using Power BI.
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Microsoft Entra authenticates users who connect to Power BI dashboards and apps. Single sign-on is used to connect to the data source in Azure Synapse provisioned pool. Authorization happens at the source.
When running an automated extract-transform-load (ETL) or extract-load-make (ELT) process, it is most efficient to load only the data that has changed since the previous run. It’s called an incremental load, as opposed to a full load that loads all the data. To perform an incremental load, you need a way to identify which data has changed. The most common approach is to use a
Value , which tracks the last value of some column in the source table, either a datetime column or a unique integer column.
Starting with SQL Server 2016, you can use temporary tables, which are system-versioned tables that keep a full history of data changes. The database engine automatically records the history of each change in a separate history table. You can query the historical data by adding a
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Just for a query. Internally, the database engine stores the history table, but it is transparent to the application.
For earlier versions of SQL Server, you can use Change Data Capture (CDC). This approach is less convenient than temporal tables, because you have to query a separate change table, and changes are tracked by a log sequence number, rather than a timestamp.
Temporal tables are useful for dimensional data, which can change over time. Fact tables usually represent an immutable transaction such as a sale, in which case keeping the system version history does not make sense. Instead, transactions usually have a column representing the transaction date, which can be used as the watermark value. For example, in the AdventureWorks Data Warehouse, the
This scenario uses the AdventureWorks sample database as a data source. The incremental data load pattern is implemented to ensure that we only load data that has been modified or added since the last pipeline run.
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The built-in metadata-driven copy tool in Azure Pipelines incrementally loads all tables contained in our relational database. By navigating through the wizard-based experience, you can connect the Copy Data tool to the source database and configure incremental or full loading for each table. The Copy Data tool then creates both the pipelines and SQL scripts to generate the control table required to store data for the incremental loading process – for example, the high watermark value/column for each table. Once the scripts are run, the pipeline is ready to load all the tables in the source data warehouse into the Synapse dedicated pool.
The tool creates three pipelines to iterate over all the tables in the database before loading the data.
The copy activity copies data from the SQL database into the Azure Synapse SQL pool. In this example, because our SQL database is in Azure, we use the Azure integration runtime to read data from the SQL database and write the data to the specified staging environment.
The copy statement is then used to load data from the staging environment into the Synapse dedicated pool.
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Pipelines in Azure Synapse are used to define the ordered set of activities to complete the incremental load pattern. Triggers are used to start the pipeline, which can be triggered manually or at a specified time.
Because the sample database in our reference architecture is not large, we created replicated tables without partitions. For production workloads, using distributed tables is likely to improve query performance. See guidance for designing distributed tables in Azure Synapse. The example scripts run the queries using a static resource class.
In a production environment, consider creating staging tables with round-robin distribution. Then transform and move the data into production tables with clustered columnstore indexes, which offer the best overall query performance. Columnstore indexes are optimized for queries that scan many records. Columnstore indexes do not perform as well for singleton lookups, that is, looking up a single row. If you need to perform frequent singleton lookups, you can add a non-clustered index to a table. Singleton lookups can run much faster with a non-clustered index. However, singleton lookups are typically less common in data warehouse scenarios than OLTP workloads. For more information, see Indexing tables in Azure Synapse.
Data types. In this case, consider a heap or clustered index. You can put the columns in a separate table.
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Power BI Premium supports several options for connecting to data sources on Azure, especially Azure Synapse provided pool:
This scenario is delivered with DirectQuery Dashboard because the amount of data used and model complexity are not high, so we can provide a good user experience. DirectQuery delegates the query to the powerful compute engine underneath and uses extensive security capabilities at the source. Also, using DirectQuery ensures that results are always consistent with the latest source data.
Import mode provides the fastest query response time, and should be considered when the model fits completely in Power BI’s memory, the data latency between refreshes can be tolerated, and there can be some complex transformations between the source system and the final model. In this case, the end users want full access to the latest data without delays in Power BI refreshing, and all historical data, which is larger than what a Power BI dataset can handle – between 25-400 GB, depending on the capacity. Grace. As the data model in the dedicated SQL pool is already in a star schema and needs no transformation, DirectQuery is an appropriate choice.
Power BI Premium Gen2 gives you the ability to handle large models, paginated reports, deployment pipelines, and a built-in Analysis Services endpoint. You can also have dedicated capacity with unique value proposition.
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When the BI model grows or the complexity of the dashboard increases, you can switch to composite models and start importing parts of look-up tables, through hybrid tables and some pre-aggregated data. Enabling query caching in Power BI for imported datasets is an option, as well as using dual tables for the storage mode property.
In the composite model, datasets act as a virtual transit layer. When the user interacts with visualizations, Power BI generates SQL queries to Synapse SQL pools dual storage: in memory or direct query depending on which one is more efficient. The engine decides when to switch from memory to direct query and pushes the logic to the Synapse SQL pool. Depending on the context of the query tables, they can act as cached (imported) or uncached composite models. Pick and choose which table to cache in memory, combine data from one or more DirectQuery sources, and/or combine data from a mix of DirectQuery sources and imported data.
These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.
Security provides assurance against deliberate attacks and the abuse of your valuable data and systems. For more information, see Overview of the security column.
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Frequent headlines of data breaches, malware infections and malicious code injection are among an extensive list of security concerns for companies looking to cloud modernization. Enterprise customers need a cloud provider or service solution that can address their concerns because they can’t afford to get it wrong.
This scenario addresses the most demanding security concerns using a combination of layered security controls: network, identity, privacy and authorization. The bulk of the data is stored in Azure Synapse provisioning pool, with Power BI using DirectQuery through single sign-on. You can use Microsoft Entra ID for authentication. There are also extensive security controls for data authorization of provisioned pools.
Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational effectiveness. For more information, see Overview of the cost optimization column.
This section provides information on pricing for different services involved in this solution, and mentions decisions made for this scenario with a sample dataset.
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Azure Synapse Analytics serverless architecture allows you to scale your compute and storage levels independently. Compute resources are charged based on usage, and you can scale or pause the resources on demand. Storage resources are billed per terabyte, so your costs will increase as you absorb more data.
Tab on the Azure Synapse pricing page. There are three main components that affect the cost of a pipeline:
For the heart of the pipeline, is triggered on a daily schedule for all the entities (tables) in the source database. This scenario contains no data flows. There are no operational costs since there are fewer than 1 million operations with pipelines per month.
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