Microsoft Sql Web Server Information Tools Business Intelligence 2012 – Business Intelligence (BI) is a set of products that allow organizations to make intelligent decisions based on information from various internal and external data sources.
Microsoft BI helps customers model, analyze, and deliver capabilities that business users can embed on mobile devices, on the web, or in applications.
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On-premise BI capabilities are available in SQL Server Analysis Services and Reporting Services as well as Power BI Desktop and Excel.
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Power BI represents cloud BI capabilities with data connections to many cloud data sources and SaaS applications, including large volumes of data in Azure SQL Data Warehouse. In addition to cloud data sources, customers can access on-premises data sources such as SQL Server Analysis Services using an on-premises data gateway.
Advanced Analytics Architecture Project Artificial Intelligence Azure Azure Data Factory Azure Machine Learning Azure Stream Analytics Banking Big Data Business Intelligence CEP CFO Cognitive Services Cortana Intelligence Suite Customer Analytics HDW Education Education HDW Education A Education XVite Education Education Xnd ive In-Memory Machine Learning Mahoot Microsoft BI Microsoft R Mobile Devices NoSQL Oil and Gas PerformancePoint Services Power BI Power Query Power View Reporting Services Retail SharePoint Insights Spark SQL Server Vision This example scenario demonstrates how data can be infused from an on-premises data warehouse into a cloud environment, then served using Business Intelligence (BI). model This approach can be the end goal or the first step towards full modernization with cloud-based components.
The following steps build Azure Synapse Analytics in an end-to-end scenario. It uses Azure Pipelines to ingest data from SQL Database to Azure Synapse SQL Pools, then transforms the data for analysis.
An organization has a large on-premises data warehouse stored in a SQL database. The 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 a pool provided by Azure Synapse. Authentication occurs at 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 previous run. This is called an incremental load as opposed to a full load which loads all the data. To handle incremental load, you need a way to identify which data has changed. A common approach is to use a
Value , which tracks the latest value of some column in the source table, be it a datetime column or a unique integer column.
Starting with SQL Server 2016, you can use temporary 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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A condition 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 use 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 rather than timestamp.
Temporary tables are useful for dimensional data, which may change over time. Fact tables typically represent an immutable transaction, such as a sale, in which case it doesn’t make sense to keep a system version history. Instead, transactions usually contain a column representing the transaction date, which can be used as a watermark value. For example, in the AdventureWorks data warehouse, the
This scenario uses the AdventureWorks model database as the data source. An incremental data load model is implemented to ensure that we only load data that has been modified or added since the most recent pipeline run.
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A built-in metadata-driven replication tool within Azure Pipelines incrementally loads all the tables in our relational database. By navigating through a wizard-based experience, you can connect the Duplicate Data tool to the source database and configure incremental or full loading for each table. The Duplicate 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, more 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.
The tool creates three pipelines to iterate over all the tables in the database before loading the data.
A 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 a set of sequenced activities to complete an incremental load pattern. Triggers are used to start the pipeline, which can be triggered manually or at a specified time.
Since the sample database in our reference architecture is not large, we created duplicate tables without any partitions. For production workloads, using distributed tables is likely to improve query performance. See the guidance for 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 production tables with clustered columnstore indexes, which deliver the best overall query performance. Columnstore indexes are optimized for queries that scan multiple records. Columnstore indexes don’t perform well for singleton lookups, that is, looking up a single row. If you need to perform singleton lookups frequently, 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 heaped 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 pool provided by Azure Synapse:
This scenario is delivered with 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 represents the query to the underlying powerful compute engine and utilizes extensive security capabilities at the core. Also, using DirectQuery ensures that the results are always consistent with the latest source data.
Import mode provides faster query response times and should be considered when the model fits perfectly within Power BI’s memory, can tolerate data latency between refreshes, and may have some complex transformations between the source system and the final model. In this case, end users want full access to the latest data with no delay in Power BI refresh and all historical data size larger than the Power BI dataset can handle, between 25-400 GB depending on capacity. Because the data model in a dedicated SQL pool is already in the star schema and requires no transformation, DirectQuery is the ideal choice.
Power BI Premium Gen2 gives you the ability to manage large models, pop-up reports, deployment pipelines, and built-in analytics services endpoint. You can also have a dedicated capability with a unique value proposition.
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As the BI model grows or dashboard complexity increases, you can switch to composite models and start importing parts of look-up tables through hybrid tables and some pre-stored data. An option is to enable query caching in Power BI for imported datasets, with the Use Dual Tables for Storage Mode property.
Within the composite model, the datasets act as a virtual pass-through layer. As users interact with visualizations, Power BI generates SQL queries to Synapse SQL pools for dual storage: in memory or direct query based on which is more efficient. The engine decides when to switch from in-memory to a direct query and pushes the logic to the Synapse SQL pool. Depending on the context of query tables, they can act as cached (imported) or uncached associative models. Pick and choose which table to store 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 workload quality. For more information, see Microsoft Azure Well-Architected Framework.
Security ensures 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 injections are among the extensive lists 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 get it wrong.
This scenario addresses the most demanding security concerns using a combination of layered security controls: network, identity, privacy, and authorization. For the most part
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