Business intelligence architecture

Business intelligence architecture – This example shows how data can be imported into a cloud environment from an on-premises data warehouse and used in a business intelligence (BI) process. This approach can be the end goal or the first step towards a full development with cloud-based features.

The following steps create the final instance of Azure Synapse Analytics. It uses Azure pipelines to get data from SQL Database to Azure Synapse SQL Pools and transforms the data for analysis.

Business intelligence architecture

Business intelligence architecture

An organization has a large data warehouse in a SQL database. An organization wants to use Azure Synapse to perform analytics and present this data using Power BI.

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Azure AD authenticates users connected to Power BI dashboards and applications. Single sign-on is used to connect to a data source in an Azure Synapse pool. Permission comes from the source.

Business intelligence architecture

When using the automated Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) method, it is best to load only the data that has changed since the previous period. This is called an incremental download, as opposed to a full download, which downloads all the data. You need a way to know what data has changed so that you can perform an incremental download. The most common method is a

A value that controls the last value of a particular column in the source table, a time column, or an integer column.

Business intelligence architecture

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Starting with SQL Server 2016, you can use temporary tables, which are system-edited 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 request historical data by adding a

Question statement. The internal database engine queries the history table, but is open to the application.

Business intelligence architecture

For earlier versions of SQL Server, you can use data transformation (CDC). This approach is less convenient than temporary tables because you have to query the change table separately, and changes are tracked by log sequence rather than timestamp.

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Temporary tables are useful for dimensional data that may change over time. Data tables often represent immutable processes, such as sales, where it is impractical to maintain a version history in the system. Instead, the transaction usually has a column representing the transaction date that can be used as a watermark value. For example, in the AdventureWorks repository,

Business intelligence architecture

This article uses a sample AdventureWorks database as a data source. An additional data entry method is implemented to ensure that we only enter data that has changed or been added since the last pipeline was started.

The metadata-driven migration tool built into Azure Pipelines adds all the tables available in our relational database. By navigating through a wizard-based experience, you can connect the Data Distribution tool to a database and configure incremental or full flow for each table. The data migration tool then executes both pipelines and SQL scripts to create the control table needed to store the data for the incremental download process – for example, the maximum value of the watermark / column for each table. After these scripts are run, the pipeline is ready to load all the tables into the database into a dedicated Synapse pool.

Business intelligence architecture

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Before loading data, the tool performs three pipelines to iterate through all the tables in the database.

A copy function 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 runtime to read data from the SQL database and write data to the specified environment.

Business intelligence architecture

The copy statement is used to import data from the platform environment into a dedicated Synapse pool.

Business Intelligence Solution Data Warehouse Architecture With Staging Area And Data Marts

Pipelines in Azure Synapse are used to define an ordered set of tasks to complete an incremental load pattern. Triggers are used to activate pipes, which can be triggered manually or at a set time.

Business intelligence architecture

Since the database sample of our reference architecture was not large, we created replicated tables without partitions. Using distributed tables for many production tasks can improve query performance. See the tutorial on designing distributed tables in Azure Synapse. Sample scripts handle requests using a set of static resources.

Consider creating stage tables with a circular distribution in the production area. Then transform and move data to production tables with clustered store indexes that provide the best query performance. Columnstore indexes are optimized for queries that scan multiple records. Columnstore indexes don’t work well for singleton searches, that is, searching for a single row. If you need to perform singleton lookups frequently, you can add a non-clustered index to the table. Singleton searches can run faster using a disjoint index. However, singleton lookups are less common in data warehousing than in OLTP operations. For more information, see Indexing tables in Azure Synapse.

Business intelligence architecture

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Data types. In this case, consider a clustered or 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 pool provided by Azure Synapse:

Business intelligence architecture

This situation is provided by the DirectQuery dashboard because the number of data used and the complexity of the model are not high, so we can provide a good user experience. DirectQuery forwards the query to a powerful computing engine and uses many security features at the source. Also, using DirectQuery ensures that the results are always consistent with the latest source information.

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The import method provides the fastest response time to the query and must be taken into account when the model is completely in memory in Power BI, data loss between updates can be allowed, and some complex changes are can occur between the source and destination systems. for example. In this case, end users want to get completely up-to-date data without delay in Power BI updates and in all historical data that is larger than the Power BI dataset can handle—between 25 -400 GB in size, depending on power. Because the data model in a dedicated SQL pool is in the star schema and does not require any transformation, DirectQuery is the right choice.

Business intelligence architecture

Power BI Premium Gen2 gives you the ability to run large models, graphical reports, supply pipelines, and built-in Analysis services. You can also have a special ability with a unique value proposition.

As the BI model grows or the complexity of the dashboard increases, you can move to integrated models and start bringing in table view components using hybrid tables and other packaged data. An option is to enable query caching in Power BI for imported data sources, as well as to use two tables for storage method properties.

Business intelligence architecture

Variations For Evolving Business Intelligence Building Business Analytics Architecture

Within the integrated model, the datasets act as a layer of discrete transformation. When a user interacts with visualizations, Power BI creates SQL queries against Synapse SQL pools: in-memory or direct query, depending on which works best. The engine makes the decision to move from memory to a specific query and pushes the memory to the Synapse SQL pool. Depending on the nature of the query tables, they can act as either stored (imported) or non-imported aggregated variables. Pick and choose which table to store in memory, aggregate data from one or more DirectQuery sources, and/or aggregate data from a combination of DirectQuery sources and imported data .

These concepts drive the pillars of the Azure Well-Designed Framework, which are guiding principles that can be used to improve the quality of operations. For more information, see Microsoft Azure Master Plan.

Business intelligence architecture

Security ensures against intentional attacks and misuse of your important data and systems. For more information, see the overview of Security columns.

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Recurring topics about data breaches, malware infections and malicious code injections are just some of the long list of security concerns for companies looking to upgrade to the cloud. Enterprise customers need a cloud provider or service solution that can solve their problems because they can’t afford to miss it.

Business intelligence architecture

This position addresses the most sought-after security issues using multiple security controls: network, identity, privacy, and consent. Most of the data is stored in a pool provided by Azure Synapse, Power BI uses DirectQuery with single sign-on. You can use Azure AD for authentication. There are also extensive security controls for data authorization in secure pools.

Cost optimization is the search for ways to reduce overhead costs and increase operational efficiency. For more information, see the Cost Optimization column summary.

Business intelligence architecture

Business Intelligence Best Practices

This section provides information on the costs of the various services involved in this decision and explains the decisions made in this situation with an example data set.

The serverless architecture of Azure Synapse Analytics allows you to measure compute and storage levels independently. Computer resources are billed based on usage, and you can add or stop these resources as needed. Storage devices are billed per terabyte, so the more data you use, the higher your costs.

Business intelligence architecture

Tab on the Azure Synapse pricing page. There are three main factors that affect pipeline prices:

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For the pipeline basis, the resource is run on a daily schedule for all objects (tables) in the database. There is no data flow in the script. With a throughput of less than 1 million tubes per month, there are no operating costs.

Business intelligence architecture

Tap on Azure Synapse

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