Installed Business Intelligence Software Evaluations – This example scenario shows how data can be ingested from an on-premises data warehouse into a cloud environment, then served using a business intelligence (BI) model. This approach can be a final goal or a first step towards complete modernization with cloud-based components.
The following steps build on an Azure Synapse Analytics end-to-end scenario. It uses Azure Pipelines to ingest data from SQL databases into Azure Synapse SQL pools, then transforms the data for analysis.
Installed Business Intelligence Software Evaluations
An organization has a large on-premises data warehouse stored in a SQL database. An organization wants to use Azure Synapse to perform analytics, then serve up these insights using Power BI.
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Azure AD authenticates users connecting to Power BI dashboards and apps. Single sign-on is used to connect to a data source in an Azure Synapse provisioned pool. Authority lies 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 data that has changed since the previous run. 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 identify which data has changed. The most common approach is to use
Value , which tracks the latest 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 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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Section of 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 approach is less convenient than temporal tables, since you must query a separate change table, and changes are tracked by log sequence number instead of timestamp.
Temporal tables are useful for dimensional data, which may change over time. Fact tables typically represent immutable transactions such as sales, in which case it makes no sense to keep system version history. Instead, transactions typically have a column representing the transaction date, which can be used as a watermark value. For example, in the AdventureWorks data warehouse
This scenario uses the AdventureWorks sample database as the data source. An 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 metadata-driven copy tool built into Azure Pipelines incrementally loads all the tables contained within our relational database. By navigating through a 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 pipelines and SQL scripts to generate the control table needed to store the data for the incremental loading process – for example, the high watermark value/column for each table. Once these scripts run, the pipeline is ready to load all the tables in the source data warehouse into the Synapse dedicated pool.
Before the tool loads data, it creates three pipelines to iterate over all the tables in the database.
The copy activity copies data from a SQL database to an 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 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 an ordered set of activities to complete an incremental load pattern. Triggers are used to start a pipeline, which can be triggered manually or at a specified time.
Since the sample database is not large in our reference architecture, we created replica tables without partitioning. For production workloads, using distributed tables is likely to improve query performance. See the guide to designing distributed tables in Azure Synapse. Example scripts run queries using a static resource class.
In a production environment, consider creating staging schedules with round-robin distribution. Then transform and move the data into product tables with clustered columnstore indexes, which provide superior overall query performance. Columnstore indexes are optimized for queries that scan multiple records. Columnstore indexes do not perform 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 the table. Singleton lookups can run much faster using a non-clustered index. However, singleton lookups are generally less common in data warehouse scenarios than in OLTP workloads. For more information, see Index tables in Azure Synapse.
Data types. In that 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 in Azure, specifically the Azure Synapse provisioned pool:
This scenario is delivered with the DirectQuery dashboard because the amount of data used and model complexity is not high, so we can provide a better user experience. DirectQuery delegates the query to the underlying powerful compute 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’s memory, data latency between refreshes can be tolerated, and some complex transformations can occur between the source system and the final model. In this case, end users want full access to the most recent data with no delay in Power BI refreshing, and all historical data, which is larger than the Power BI dataset can handle, between 25-400 GB depending on capacity. size Since the data model in a dedicated SQL pool is already in a star schema and no transformation is required, DirectQuery is a suitable alternative.
Power BI Premium Gen2 gives you the ability to handle large models, paginated reports, deployment pipelines, and built-in analytics services endpoints. You can also have dedicated capacity with a unique value proposition.
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As the BI model grows or dashboard complexity increases, you can switch to mixed models and start importing look-up tables through hybrid tables, and some pre-aggregated data. An option is to enable query caching within Power BI for imported datasets, as well as using the Dual Tables for Storage Mode property.
Within the composite model, the datasets act as a virtual pass-through layer. When a user interacts with a visualization, Power BI generates SQL queries in Synapse SQL pools with dual storage: memory or live query depending on which is more efficient. The engine decides when to switch from in-memory to live query and pushes the logic to the Synapse SQL pool. Depending on the context of the query table, they can act as either cached (imported) or uncached aggregate models. Pick and choose 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 ideas 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 Well-architected Framework.
Security provides assurance against deliberate attacks and misuse 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 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 can’t get it wrong.
This scenario addresses the most demanding security concerns using a combination of layered security controls: network, identity, privacy, and authorization. Power BI uses DirectQuery with single sign-on to store multiple data in an Azure Synapse provisioned pool. You can use Azure AD for authentication. There are also comprehensive security controls for data authorization of provisioned pools.
Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational efficiency. For more information, see the Overview of Cost Optimization column.
This section provides information on the pricing of the various services involved in this solution, and outlines the 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 these resources on demand. Storage resources are billed per terabyte, so your costs will increase as you ingest more data.
Tab on the Azure Synapse Pricing page. There are three main components that influence pipeline prices:
For the core of the pipeline, a daily schedule is triggered for all entities (tables) in the source database. The scenario does not include any data flow. There are no operating costs as less than 1 million pipelines are operated in a month.
Tab on Azure Synapse
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