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Business Intelligence Tools Microsoft – This example scenario shows how data can be ingested from an on-premises data warehouse into a cloud environment and then deployed using a business intelligence (BI) model. This approach could be an end goal or a first step toward full modernization with cloud-based components.

The following steps build 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 and then transform 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 analytics and then deliver those insights using Power BI.

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Microsoft Entra authenticates users connecting to Power BI dashboards and apps. Single sign-on is used to connect to the data source in the deployed Azure Synapse pool. Authorization occurs at the source.

When you run an automated Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) process, it is most efficient to load only the data that has changed since the previous execution. This is called an incremental load, as opposed to a full load, which loads all data. To perform an incremental load, you need a way to determine what data has changed. The most common approach is to use a

Value that tracks the latest 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 store a complete 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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Clause to a query. Internally, the database engine queries the history table, but this 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 using a log sequence number rather than a timestamp.

Temporal tables are useful for dimensional data that can change over time. Fact tables typically represent an immutable transaction such as a sale. In this case, there is no point in retaining system version history. Instead, transactions typically have a column that represents the transaction date and can be used as a watermark value. For example, in the AdventureWorks Data Warehouse

In this scenario, the AdventureWorks sample database is used as a data source. The incremental data loading pattern is implemented to ensure that we only load data that has been changed or added after the last pipeline execution.

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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 the wizard-based experience, you can connect the data copy tool to the source database and configure either incremental or full load 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 load – for example, the top watermark value/column for each table. Once these scripts are executed, the pipeline is ready to load all tables in the source data warehouse into the dedicated Synapse pool.

The tool creates three pipelines to iterate over all 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 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 dedicated Synapse pool.

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Pipelines in Azure Synapse are used to define the ordered set of activities to complete the incremental loading pattern. Triggers are used to start the pipeline, which can fire manually or at a specified time.

Since the sample database in our reference architecture is not large, we created replicated tables without partitions. For production workloads, using distributed tables should improve query performance. See How to design 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. You 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 perform singleton searches, i.e. H. when looking up a single line, not so good performance. If you need to perform singleton searches frequently, you can add a non-clustered index to a table. Singleton searches 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 Index tables in Azure Synapse.

Data types. In this case, consider a heap or clustered index. You can put these columns in a separate table.

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Power BI Premium supports multiple options for connecting to data sources in Azure, particularly the deployed Azure Synapse pool:

This scenario is provided 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 computing engine underneath and leverages extensive security features in the source. Additionally, using DirectQuery ensures that the results always match the latest source data.

Import mode provides the fastest query response time and should be considered when the model fits fully in Power BI’s memory, data latency between updates can be tolerated, and there may be some complex transformations between the source system and the final model. In this case, end users want complete access to the latest data without delays when updating Power BI, as well as all historical data larger than a Power BI dataset can handle – between 25 and 400 GB in size, depending on capacity. Since the data model in the dedicated SQL pool is already in a star schema and does not require any transformation, DirectQuery is the appropriate choice.

Power BI Premium Gen2 gives you the ability to handle large models, paginated reports, deployment pipelines, and integrated Analysis Services endpoints. They may also have dedicated capabilities with unique value propositions.

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As 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 is using dual tables for the storage mode property.

Within the composite model, data sets act as a virtual passthrough layer. As the user interacts with visualizations, Power BI generates SQL queries to Synapse SQL pools with dual storage: in-memory or direct query, whichever is more efficient. The engine decides when to switch from in-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 either cached (imported) or uncached composite models. 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 principles that can be used to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.

Security provides protection against intentional attacks and misuse of your valuable data and systems. For more information, see Security Column Overview.

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Frequent headlines about data breaches, malware infections, and malicious code injections are among the extensive security concerns for organizations seeking cloud modernization. Enterprise customers need a cloud provider or service solution that addresses their concerns because they can’t afford to get it wrong.

This scenario addresses the most challenging security concerns using a combination of multi-layered security controls: network, identity, privacy, and authorization. The majority of data is stored in the provisioned Azure Synapse pool, with Power BI using DirectQuery over single sign-on. You can use Microsoft Entra ID for authentication. Additionally, there are extensive security controls for data authorization of deployed pools.

Cost optimization is about 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 decisions made for this scenario using a sample data set.

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Azure Synapse Analytics serverless architecture lets you scale your compute and storage tiers independently. Compute resources are billed based on usage, and you can scale or pause these resources as needed. Storage resources are priced per terabyte, so your costs increase the more data you hold.

On the Azure Synapse pricing page. There are three main components that affect the price of a pipeline:

For the core of the pipeline is triggered on a daily schedule for all entities (tables) in the source database. The scenario does not contain any data flows. There are no operating costs as fewer than 1 million pipelines are operated each month.

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