Microsoft Business Intelligence Tools List – This example scenario shows how data can be imported into a cloud environment from an on-premises data warehouse and then served by a business intelligence (BI) model. This approach could be the ultimate goal or the first step toward full modernization with cloud-based components.
The following steps are based on an end-to-end Azure Synapse Analytics setup. It uses Azure Pipelines to import data from an SQL database into Azure Synapse SQL pools, and 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 analysis and then serve that information with 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 the data source in the Azure Synapse provisioning group. Authorization happens at the source.
When running an automated Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) process, it is more efficient to load only the data that has changed since the previous run. This is called a gradual load, as opposed to a full load which loads all the data. To perform an incremental load, you need a way to determine what data has changed. The most common way is to use a
Value, which keeps track of the most recent value of some column in the source table, either a date column or a specified integer column.
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Starting with SQL Server 2016, you can use temporary tables, which are system-versioned tables that maintain a complete history of data changes. The database engine automatically records the history of each change in a separate history table. You can check the historical data by sending a
Clause to question. Internally, the history table is consulted by the database engine, 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 timestamps, because you must query an individual change log, and changes are tracked by log sequence number, rather than timestamp.
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Temporal tables are useful for measurement data, which can change over time. Information logs typically represent an immutable transaction, such as a sale, in which case there is no point in maintaining a 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 store, the
This configuration uses the AdventureWorks sample database as the data source. The incremental data load pattern is implemented to ensure that we only load data that has been changed or added since the most recent pipeline execution.
The metadata-based copy tool within Azure Pipelines incrementally loads all the records 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 load or full load for each record. The Copy Data tool then creates both the pipelines and the SQL scripts to generate the control table needed to store data for the incremental load process, for example, the value of the token: high water/column for each table. Once these scripts run, the pipeline is ready to upload all the records in the source datastore to the dedicated Synapse pool.
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The tool creates three pipelines to iterate over all the records in the database before loading the data.
The copy operation copies data from an SQL database to the Azure Synapse SQL pool. In this example, because our SQL database is on 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 are used in Azure Synapse to define the ordered set of tasks to complete the incremental load pattern. Triggers are used to start the pipeline, which can be triggered manually or at a specific time.
Since the sample database in our reference architecture is not large, we create refined non-partitioned tables. For production workloads, using distributed tables is likely to improve query performance. See the 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 to production tables with clustered columnstore indexes, which provide the best overall query performance. Columnstore indexes are updated for queries that parse many records. Columnstore indexes work less well for singleton lookups, that is, looking for a single row. If you need to perform a single search frequently, you can add a nonclustered index to a table. Singleton searches can run much faster by using a nonclustered index. However, singleton lookup is typically less common in data warehouse scenarios than OLTP workloads. For more information, see Index tables in Azure Synapse.
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Type of data. In that case, consider a heap or a clustered index. You can put these columns in a separate table.
Power BI Premium supports several options for connecting to data sources in Azure, especially the Azure Synapse provisioning group:
This scenario is provided by the DirectQuery panel because the amount of data used and the complexity of the model are not high, so we can provide a good user experience. DirectQuery delegates the query to the powerful underlying compute engine and uses extensive security capabilities at the source. Additionally, the use of DirectQuery ensures that the results are always consistent with the most recent source data.
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The import mode provides the fastest query response time and it should be noted that when the model is fully fit in Power BI memory, the data latency between updates and dh ‘ there may be some complex transformations between the origin and final model. In this case, end users want full access to the latest data with no delay in Power BI updates, and all historical data, which is more than the Power dataset can handle for BI: between 25 and 400 GB, depending on capacity. Size. Since the data model in the dedicated SQL pool is already in a star schema and does not require transformation, DirectQuery is a suitable choice.
Power BI Premium Gen2 gives you the power to handle large models, page reports, deployment pipelines, and an integrated Analysis Services endpoint. You may also have a unique capability with a unique value proposition.
When the BI model grows or the complexity of the dashboard grows, you can switch to composite models and start incorporating parts of view tables, through hybrid tables, and some pre-collected data. Enabling query caching within Power BI for imported databases is an option, as is using double tables for the storage method property.
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Within the hybrid model, the databases act as a virtual derivation layer. When the user interacts with the images, Power BI generates SQL queries to Synapse SQL collecting dual storage: in-memory or a direct query, depending on which is more efficient. The engine decides when to go from in memory to a direct query and sends the logic to the Synapse SQL pool. Depending on the context of the query tables, they can be cached (included) or non-cached modules. Pick and choose which table to cache in memory, combine data from one or more DirectQuery sources, and/or combine data from a combination 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 Well-Architected Framework.
Security provides guarantees against deliberate attacks and misuse of your valuable data and systems. For more information, see Security Tree Overview.
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Frequent headlines of data breaches, malware infections, and malicious code injection are among a long list of security concerns for businesses looking to move to the cloud. Enterprise customers need a cloud provider or service solution that addresses their concerns because they can’t go wrong.
This scenario addresses the most challenging security concerns through a combination of layered security controls: network, identity, privacy, and authorization. Most of the data is stored in the Azure Synapse provisioning group, with Power BI using DirectQuery via single sign-on. You can use Azure AD for authentication. There are also extensive security controls for supply group data authorization.
Cost optimization is about finding ways to reduce unnecessary costs and improve operational efficiency. For more information, see Cost Optimization Column Overview.
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This section provides pricing information for various services included in this solution and references decisions made for this scenario using sample data.
Azure Synapse Analytics’ serverless architecture allows you to independently scale your compute and storage tiers. Compute resources are charged based on usage, and you can scale or stop these resources on demand. Storage resources are billed per terabyte, so your costs will increase as you add more data.
On the Azure Synapse pricing page. There are three main components that affect the price of a pipeline:
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For the core of the pipeline, activated on a daily schedule for all groups (programs) in the source database. There are no data streams on the stage. There are no operating costs as there are less than 1 million tubed operations per month.
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