Microsoft Business Intelligence Bi Tools For Evaluation As Well As Stating – 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 could be an end goal or a first step towards fully modernizing with cloud-based components.
The following steps are based on the end-to-end Azure Synapse Analytics scenario. It uses Azure Pipelines to ingest data from a SQL database into Azure Synapse SQL pools, then transforms the data for analysis.
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An organization has a large warehouse of data stored in a SQL database. The organization wants to use Azure Synapse to perform analytics, then deliver this information 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 the pool provided by Azure Synapse. Authorization occurs at the source.
When running an automated extract-transform-load (ETL) or extract-load-transform (ELT) process, it is most efficient to load only 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 the data that has changed. The most common approach is to use a
Value , which tracks the most recent value of a column in the source table, either a datetime column or a unique integer column.
Starting with SQL Server 2016, you can use temporal tables, which are system versioned tables that keep a complete history of data changes. The database engine automatically records the history of each change in a separate history table. You can query historical data by adding a
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Clause to a query. Internally, the database engine queries the history table, but 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 that may change over time. Fact tables typically represent an immutable transaction, such as a sale, in which case keeping system version history does not make sense. Instead, transactions usually have a column representing the date of the transaction, which can be used as a watermark value. For example, in AdventureWorks Data Warehouse,
This scenario uses the AdventureWorks sample database as the data source. The incremental data loading model is implemented to ensure that we only load data that has been changed or added since the most recent pipeline run.
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The built-in metadata-driven copy tool in Azure Pipelines incrementally loads all the tables in our relational database. By navigating through the wizard-based experience, you can connect the Copy Data tool to the source database and configure either incremental or full loading for each table. The Copy Data tool then creates both the pipelines and the SQL scripts to generate the control table needed to store the data for the incremental load process – for example, the large watermark value/column for each table. Once these scripts are executed, the pipeline is ready to load all the tables from the source datastore 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 to the Azure Synapse SQL pool. In this example, since our SQL database is in Azure, we use the Azure integration runtime to read the 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 medium 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.
Since the example database in our reference architecture is not large, we created replicated tables without partitions. For production workloads, using distributed tables can improve query performance. See Guide to designing distributed tables in Azure Synapse. The sample 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 provide the best overall query performance. Columnstore indexes are optimized for queries that scan many records. Columnstore indexes do not perform as well for singleton searches, i.e. searching for a single row. If you need to perform frequent singleton lookups, you can add a non-clustered index to a table. Singleton searches can run much faster using 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 those columns in a separate table.
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Power BI Premium supports several options for connecting to data sources on Azure, especially in the pool provided by Azure Synapse:
This scenario comes with the DirectQuery dashboard, because the amount of data used and the complexity of the model are not large, so we can provide a good user experience. DirectQuery delegates querying to the underlying powerful computing engine and uses extensive security capabilities at the source. Also, using DirectQuery ensures that the 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 entirely in Power BI memory, data latency between refreshes can be tolerated, and there might be some complex transformations between the source system and the final model . In this case, end users want full access to the latest data, without Power BI refresh delays, and to all historical data, which is larger than what a Power BI dataset can handle – between 25-400 GB, depending on capacity. the size. Since the data model in the dedicated SQL pool is already in a star schema and requires no transformation, DirectQuery is a suitable choice.
Power BI Premium Gen2 gives you the ability to manage large models, paginated reports, deployment pipelines, and an embedded Analysis Services endpoint. You can also have dedicated capacity with a unique value proposition.
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When the BI model grows or the complexity of the dashboard increases, you can move to composite models and start importing parts of lookup 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.
Within the composite model, datasets act as a virtual traversal layer. When the user interacts with views, Power BI generates SQL queries to store Synapse SQL pools: in memory or direct query, whichever is more efficient. The engine decides when to switch from in-memory query to direct query and pushes the logic to the Synapse SQL pool. Depending on the context of query tables, they can act either as cached (imported) or non-cached composite models. Pick and choose which table to cache, 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 principles that can be used to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.
Security provides safeguards against deliberate attacks and abuse of your valuable data and systems. For more information, see Security Pillar Overview.
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Frequent headlines about data breaches, malware infections and malicious code injection are among an extensive list of security concerns for companies looking to modernize the cloud. Enterprise customers need a cloud provider or service solution that can address their concerns because they cannot afford to be wrong.
This scenario addresses the most demanding security concerns using a combination of layered security controls: network, identity, privacy, and authorization. Most of the data is stored in the Azure Synapse pool, Power BI using DirectQuery through single sign-on. You can use Microsoft Entra ID for authentication. There are also extensive data authorization security controls for provisioned pools.
Cost optimization means looking for ways to reduce unnecessary expenses and improve operational efficiency. For more information, see Cost Optimization Pillar Overview.
This section provides pricing information for various services involved in this solution and mentions the decisions made for this scenario with an example dataset.
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Azure Synapse Analytics’ serverless architecture allows you to scale your compute and storage tiers independently. Compute resources are charged based on usage, and you can scale or pause these resources on demand. Storage resources are billed per terabyte, so your costs will increase as you ingest more data.
On the Azure Synapse pricing page. There are three main components that influence the price of a pipeline:
For the pipeline core, it is triggered on a daily schedule for all entities (tables) in the source database. The scenario does not contain data flows. There are no operational costs because there are less than 1 million pipeline operations per month.
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