Business Intelligence Search – This example scenario shows how to get data into a cloud environment from an on-premises data warehouse and then process it using a business intelligence (BI) model. This approach can be the final goal or the first step towards complete integration with cloud-based components.
These steps are based on a complex Synapse Azure analytics scenario. It uses Azure Pipelines to get data from a SQL database into Azure Synapse SQL pools and then transforms the data for analysis.
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The organization has a large local database stored in a SQL database. An organization wants to use Azure Synapse to analyze and then provide these reports using Power BI.
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Azure AD authenticates users who connect to Power BI spreadsheets and apps. SSO is used to connect to a data source in a provided Azure Synapse pool. Authority takes place at the source.
When you run an export-transform (ETL) or export-load-transform (ELT) process, it is most efficient to collect only the data that has changed since the previous run. This is called an incremental load, as opposed to a full load, which carries all the data. To perform an additional load, you need a way to identify which data has changed. The most common way is to use it
Value that tracks the most recent value of some column in the source table, either a date column or a unique integer column.
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Starting with SQL Server 2016, you can use temporary tables, which are system-level 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 find historical data by clicking on it
Question sentence. Internally, the database engine requests the history table, but this is transparent to the application.
For older versions of SQL Server, you can use data capture conversion (CDC). This method is less convenient than temporary tables because you have to query the change table separately, and the changes are tracked by the log sequence number, not the timestamp.
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Periodic tables are useful for quantitative data that may change over time. Truth tables often represent an immutable transaction, such as a sale, in which case it doesn’t make sense to store the version history of the system. Instead, transactions often have a document that represents the date of the transaction, which can be used as a watermark. For example, in the AdventureWorks Data Warehouse,
This scenario uses the AdventureWorks sample database as a data source. An additional data load pattern is implemented to ensure that only data that has changed or been added since the last time in the pipeline is loaded.
The metadata-based replication tool built into Azure Pipelines iteratively moves all the tables in our relational database. By navigating the wizard-based area, you can connect the Copy Data tool to the source database and configure either incremental or full load for each table. The Copy Data tool then creates both pipes and SQL scripts to generate the control table needed to save the data for the additional load process – for example, high water/column values for each table. After running these scripts, the pipeline is ready to load all the tables in the source database into the Synapse dedicated environment.
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The tool creates three pipelines to iterate through all the tables in the database before loading the data.
A copy task copies data from a SQL database to an Azure Synapse SQL pool. In this example, since the SQL database is in Azure, we use the Azure synchronization session to read the data from the SQL database and write the data to the designated staging area.
The copy command is then used to move the data from the programming area into the Synapse storage area.
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Pipes in Azure Synapse are used to define a set of tasks that are ordered to complete an incremental load process. Triggers are used to start a pipeline that can be triggered manually or at a specific time.
Because the sample database in our reference architecture is not large, we create recursive tables without partitions. In production workloads, the use of partitioned tables is likely to improve query performance. See Guidelines for configuring shared tables in Azure Synapse. The example scripts run the queries using the static resource class.
In a production environment, consider creating scheduling tables with round-robin distribution. Then transform and load the data into production tables with column store indexes that provide the best overall query performance. Column store indexes are optimized for queries that check many records. Column-store indexes don’t work well for single searches, that is, single-row searches. If you need to perform item-by-item searches frequently, you can add a non-clustered index to the table. Singleton searches can run much faster using a non-clustered index. However, fabric searches are typically less common in data warehouse scenarios than OLTP workloads. For more information, see Index tables in Azure Synapse.
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Data types. In that case, consider a composite or composite index. You can place these columns in a separate table.
Power BI supports several options for connecting to data sources in Azure, in particular the pool of Azure Synapse services:
This is the case with the DirectQuery control 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 a powerful underlying computing engine and takes advantage of multiple resource protection capabilities. Using DirectQuery also ensures that the results are always consistent with the latest source data.
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Import mode provides the fastest query response time and should be considered when the model fits completely in the BI dynamic memory, data loss between iterations can be tolerated, and complex changes can occur between the source system and the final model. In this case, end users need full access to new data without power BI refresh lag and any historical data larger than the Power BI dataset can handle – between 25-400GB, depending on capacity. size. Since the data model in the SQL environment is already in the star schema and does not require any modification, DirectQuery is a good choice.
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As the BI model grows or the complexity of the dashboard increases, you can move to composite models and start moving parts of the lookup tables through hybrid tables and some aggregate data. One option is to enable query caching within Power BI for imported data sheets, and use two tables for the storage location property.
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Within the cluster model, databases act as a virtual pass-through layer. When a user interacts with visualizations, Power BI executes SQL queries for two storage of Synapse SQL pools: in-memory or direct query, depending on which is more efficient. The engine decides when to switch from memory to a direct query and puts the knowledge into the SQL Synapse pool. Depending on the content of the query tables, they can act as cached (imported) or non-cached compound models. Select a table to store in memory, combine data from one or more DirectQuery sources, and/or combine data from a mixture of DirectQuery sources and imported data.
These concepts implement the pillars of the Azure Well-Architected Framework, a set of guidelines that can be used to improve the quality of workloads. For more information, see Microsoft Azure Framework Architecture.
Security provides protection against intentional attacks and misuse of your valuable data and systems. For more information, see Security Pillar Overview.
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Frequent headlines about data breaches, malware infections and malicious code injection are among a large 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 afford to get it wrong.
This scenario solves the most demanding security problems by using a combination of layered security controls: network, identity, privacy and authorization. Most data is stored in a provided Azure Synapse pool, with Power BI using DirectQuery via a login. You can use Azure AD for authentication. There are also multiple security checks for data authorization of funds provided.
Cost optimization is about finding ways to reduce unnecessary expenses and improve efficiency. For more information, see the Cost Optimization Pillar Overview.
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This section provides cost information for the various functions included in this solution and outlines the decisions made for this scenario with a sample data set.
Azure Synapse Analytics’ serverless architecture allows you to independently scale analytics and storage tiers. Computing resources are priced based on usage, and you can scale or suspend these resources on demand. Storage resources are priced per terabyte, so your costs will increase as you collect more data.
On the Azure Synapse pricing page. There are three main components that affect the price of a pipe:
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For the pipeline, it runs on a daily schedule for all objects (tables) in the source database. The scenario does not contain any data streams. There are no service charges because there are less than 1 million pipeline services per month.
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