Business Intelligence Analyst Microsoft – This example scenario shows how data can be ingested from an on-premise data warehouse into a cloud environment, then served using a business intelligence (BI) model. This approach can be the first step towards complete modernization with end-to-end or cloud-based components.
The following steps create an end-to-end Azure Synapse Analytics scenario. It uses Azure Pipelines to ingest data from SQL databases into Azure Synapse SQL pools, then transforms the data for analysis.
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An organization has a large data warehouse stored in a SQL database. The organization wants to use Azure Synapse to perform analytics, then serve up those insights using Power BI.
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Microsoft Intra authenticates users who connect to Power BI dashboards and apps. Single sign-on is used to connect to data sources in an Azure Synapse provisioned pool. Authorization occurs at the source.
When you run an automated extract-transform-load (ETL) or extract-load-transform (ELT) process, it is most efficient to load only the data that has changed since the last cycle. This is 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 determine which data has changed. The most common method is a
Value, which follows the last value of some column in the source table, either a datetime column or a specific integer column.
Starting with SQL Server 2016, you can use temporary tables, which are system version 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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Closed for a question. Internally, the database engine queries the history table, but this is transparent to the application.
For earlier versions of SQL Server, you can change data capture (CDC). This method is less convenient than temporary tables, because you have to query a separate change table, and changes are tracked by log sequence number instead of time.
Temporary tables are useful for dimensional data, which can change over time. Fact tables usually represent changing transactions such as sales, in which case keeping system version history makes no sense. Instead, transactions usually contain a column representing the date of the transaction, which can be used as a watermark value. For example, in the AdventureWorks data warehouse, the
This scenario uses the AdventureWorks sample database as a data source. An incremental data loading pattern is implemented to ensure that we only load data that has changed or been added since the last pipeline run.
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The metadata-driven copy tool in Azure Pipelines incrementally loads all the tables in our relational database. By navigating through a 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 pipelines and SQL scripts to create the control table to store the data required for the incremental loading process—for example, the highest watermark value/column for each table. Once these scripts are run, the pipeline is ready to load all the tables in the source data warehouse into the Synapse dedicated pool.
This tool creates three pipelines to iterate over all tables in the database, before loading the data.
The copy function copies data from an SQL database to an 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 string environment.
The copy statement is then used to load data from the staging environment into Synapse’s dedicated pool.
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Pipelines in Azure Synapse are used to define an ordered set of activities to complete an incremental load pattern. Triggers are used to start the pipeline, which can be started manually or at a specified time.
Because the sample database in our reference structure is not large, we created duplicate tables that have no partitions. For production workloads, using distributed tables is likely to improve query performance. See the guide to designing distributed tables in Azure Synapse. The example scripts run 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 output tables with clustered columnstore indexes, which offer better query performance. Columnstore indexes are preferred for queries that scan multiple records. Columnstore indexes don’t work well for singleton views, it’s looking for a single row. If you need to perform singleton lookups frequently, you can add a non-clustered index to the table. A singleton search can run much faster using a non-clustered index. However, singleton searches are generally less common in data warehouse scenarios than in OLTP workloads. For more information, see Listing tables in Azure Synapse.
Data Types In this case, consider a clustered or clustered index. You may want to put these columns in a separate table.
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Power BI Premium supports several options for connecting to data sources in Azure, especially the Azure Synapse-provided pool:
This scenario is presented with the DirectQuery dashboard because the amount of data used and the complexity of the model is not high, so we can provide a better user experience. DirectQuery sends queries to a 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 response time to queries, and should be considered when the model fits entirely in Power BI memory, data delays between refreshes can be tolerated, and possibly between the source system and the latest There are some complex changes between models. In this case, end users want full access to the most recent data with no delay in updating Power BI, and all historical data, which is larger than what the Power BI dataset can handle – 25-400 Between GB, capacity depends. As the data model in the dedicated SQL pool is already in the star schema and does not require any changes, DirectQuery is a suitable choice.
Power BI Premium Gen2 enables you to manage large models, create paginated reports, deployment pipelines, and analytics service endpoints. You can also have dedicated capacity with a special 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 view tables, through hybrid tables, and some pre-collected 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, the datasets act as a virtual pass-through layer. When users interact with views, Power BI generates SQL queries for the Synapse SQL pool’s dual storage: in memory or directly querying depending on which is more efficient. The engine decides when to query directly from memory 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 tables 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 workloads. For more information, see Microsoft Azure Good Architecture Framework.
Security provides assurance against intentional attacks and misuse of your valuable data and systems. For more information, see Security Pillar Overview.
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Frequent headlines of data breaches, malware infections, and malicious code injection are among the extensive list of security concerns for companies looking to modernize the cloud. Business customers need a cloud provider or service solution that can address their concerns because they can’t get it wrong.
This scenario addresses the most pressing security concerns by using a combination of layered security controls: network, identity, privacy, and authorization. The bulk of the data is stored in an Azure Synapse provisioned pool, using DirectQuery via single sign-on with Power BI. You can use Microsoft Intra ID to authenticate. There are also extensive security controls for data authorization of the provisioned pools.
Cost optimization is looking for ways to reduce unnecessary costs and improve operational efficiencies. For more information, see Overview of Cost Optimization Column.
This section provides pricing information for the various services involved in this solution, and notes the decisions made for this scenario with a sample data set.
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Azure Synapse Analytics Server’s seamless architecture allows you to independently scale your compute and storage. Resource accounting is charged based on usage, and you can turn these resources on or off on demand. Storage resources are billed per terabyte, so your costs will increase as you buy more data.
Tab on the Azure Synapse Pricing page. There are three main components that affect pipeline pricing:
For the main pipeline, all entities (tables) in the source database are created on a daily schedule. The scenario has no data flow. There are no operating costs as there are less than 1 million operations with the pipelines per month.
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