Business Intelligence Tools Delivered As A Service

Business Intelligence Tools Delivered As A Service – This example scenario demonstrates how data can be ingested from an on-premises data warehouse into a cloud environment and then presented using a business intelligence (BI) model. This approach can be an end goal or a first step towards full modernization with cloud-based components.

The following steps are based on the end-to-end Azure Synapse Analytics scenario. It uses Azure Pipelines to fetch data from a SQL database to Azure Synapse SQL pools, then transforms 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 analysis and then present these insights using Power BI.

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Azure AD authenticates users connecting to Power BI dashboards and applications. Single sign-on is used to connect to the datasource in the pool provided by Azure Synapse. Authorization takes place at the source.

When you run an automatic extract-transform-load (ETL) or extract-load-transform (ELT) operation, it is most efficient to load only the data that has changed since the previous run. This is called incremental load as opposed to full load which loads all the data. To perform an incremental upload, you need a way to determine what data has changed. The most common approach is a

A datetime column or a unique integer column that follows the most recent value of some column in the source table.

Starting with SQL Server 2016, you can use temporary tables, which are system-versioned tables that keep a full history of data changes. The database engine automatically saves the history of each change in a separate history table. You can query historical data by adding

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Clause for a query. Internally, the database engine queries the history table, but it is transparent to the application.

For earlier versions of SQL Server, you can use change data capture (CDC). This approach is less useful than temporary tables because you have to query a separate change table and changes are tracked with a log sequence number rather than a timestamp.

Temporary tables are useful for dimension data that may change over time. Fact tables often represent an immutable transaction such as a sale, in which case it wouldn’t make sense to keep system version history. Instead, transactions usually have a column that represents the transaction date and 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 load model 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 built-in metadata-driven copy tool in Azure Pipelines incrementally loads all tables in our relational database. By navigating the wizard-based experience, you can connect the Data Copy tool to the source database and configure incremental or full loading for each table. The Data Copy tool then creates both pipelines and SQL scripts to create the necessary control table (for example, high watermark value/column for each table) to store the data for the incremental load operation. After these scripts are run, the pipeline is ready to load all tables from the source data warehouse into the Synapse dedicated repository.

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 environment into the Synapse dedicated repository.

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Pipelines in Azure Synapse are used to define the sequential set of events to complete the incremental load model. Triggers are used to start the pipeline, which can be triggered manually or at a specified time.

Because the sample database in our reference architecture is not large, we created duplicate tables without partitions. For production workloads, using distributed tables can improve query performance. See the guide for designing distributed tables in Azure Synapse. The sample scripts run queries using a static resource class.

In a production environment, consider creating staging tables with sequential deployment. Then transform and move data into production tables with clustered columnstore indexes that offer the best overall query performance. Columnstore indexes are optimized for queries that scan many records. Columnstore indexes do not perform well for singular searches, i.e. a single line search. If you need to perform singular searches frequently, you can add a nonclustered index to a table. Singleton searches can run much faster using a nonclustered index. However, singular calls are generally less common than OLTP workloads in data warehouse scenarios. For more information. Indexing 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 several options for connecting to data sources on Azure, specifically the pool provided by Azure Synapse:

This scenario comes with the DirectQuery dashboard, as 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 underlying computing engine and leverages extensive security capabilities at the source. Also, using DirectQuery ensures that 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 completely in Power BI’s memory, when the data delay between refreshes is tolerable, and there may 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 any delay in Power BI refresh and any historical data larger than a Power BI dataset can handle (between 25-400GB depending on capacity). dimension. DirectQuery is a suitable choice because the data model in the dedicated SQL pool is already in a star schema and does not require any transformations.

Power BI Premium Gen2 gives you the ability to manage large models, paginated reports, deployment pipelines, and built-in Analytics Services endpoint. You can also have special capacity with unmatched value proposition.

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When the BI model grows or the dashboard complexity increases, you can switch to composite models and start importing parts of the lookup tables via hybrid tables and some pre-assembled data. In addition to enabling query caching in Power BI for imported datasets, using binary tables for the storage mode feature is also an option.

Within the composite model, datasets act as a virtual transition layer. As the user interacts with the visualizations, Power BI generates SQL queries to synthesize dual storage in SQL pools, either in memory or in a direct query, depending on which is more efficient. The engine decides when to switch from the in-memory query to the direct query and sends the logic to the Synapse SQL pool. Depending on the content of the query tables, they can act as cached (imported) or non-cached composite models. Pick and choose which table to cache into 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, a set of guiding principles that can be used to improve the quality of a workload. For more information. Microsoft Azure Well-Architected Framework.

Security ensures against deliberate attacks and misuse of your valuable data and systems. For more information. Overview of the security column.

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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 seeking cloud modernization. Enterprise customers need a cloud provider or service solution that can address their concerns because they can’t afford to be misunderstood.

This scenario addresses the toughest security concerns using a combination of layered security controls: network, identity, privacy, and authorization. Most of the data is stored in the repository provided by Azure Synapse with Power BI using DirectQuery via single sign-on. You can use Azure AD for authentication. Provided repositories also have extensive security controls for data authorization.

Cost optimization is about looking for ways to reduce unnecessary expenses and increase operational efficiency. For more information. Overview of the cost optimization column.

This section provides information on the pricing of the different services included in this solution and discusses the decisions taken for this scenario with a sample dataset.

Hr Service Delivery

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 receive more data.

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

For the core of the pipeline, it is triggered on a daily schedule for all entities (tables) in the source database. The scenario does not contain a data stream. There are no operating costs as less than 1 million transactions are processed through pipelines per month.

Tab in Azure Synapse

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