Open Source Business Intelligence Tools Transforming Coverage Models

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Open Source Business Intelligence Tools Transforming Coverage Models – This example scenario shows how data can be fed from an on-premise data warehouse into a cloud environment and then served using a 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 are based on the Azure Synapse Analytics endpoint scenario. It uses Azure Pipelines to import data from a SQL database into Azure Synapse SQL groups, then transforms the data for analysis.

Open Source Business Intelligence Tools Transforming Coverage Models

Open Source Business Intelligence Tools Transforming Coverage Models

An organization has a large database stored in a SQL database. An organization wants to use Azure Synaps to perform analytics and then serve up those insights using Power BI.

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Microsoft Entra authenticates users connecting to Power BI dashboards and apps. Azure Synapse uses a single sign-on to connect to a data source in a provisioned cluster. Licensing is done at source.

When running automated extract-transform-load (ETL) or extract-transform-transform (ELT), it is more efficient to load only data that has changed since the previous run. Unlike a full load, which loads all the data, it is called an incremental load. To handle increased workloads, we need a way to determine what data has changed. The most common approach is a

A value that tracks the last value of some column in the source table, either a data column or a unique integer column.

Starting with SQL Server 2016, you can use temporary tables, which are system version tables that store a complete history of data changes. The database engine records the history of each change in a separate history table. You can search for historical data and add a

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Item for question. The internal database engine looks up the history table, but it is transparent to the application.

You can use Change Capture Data (CDC) for earlier versions of SQL Server. This approach is more convenient than temporary tables because you have to look up a separate change table and changes are tracked by a daily sequence number rather than a timestamp.

Periodic tables are useful for measurement data that may change over time. Fact tables usually represent a static transaction, such as a sale, in which case it makes no sense to keep a version history. Instead, transactions typically have a column indicating the date of the transaction that can be used as a watermark value. For example, in the AdventureWorks database,

Open Source Business Intelligence Tools Transforming Coverage Models

This scenario uses the AdventureWorks model database as a data source. The incremental data load model is implemented to ensure that we load data that has changed or been added since the last pipeline run.

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A metadata-driven replication tool built into Azure Pipelines continuously loads all the tables in our relational database. You can navigate through the wizard-based experience, connect the Data Copy tool to the source database, and configure incremental or full loading for each table. Data migration tools create pipelines and SQL scripts to create the required control table to store data for the incremental load process – for example, each table’s high watermark value/columns. After these entries are made, the pipeline is ready to add all the tables in the source database to the custom Synapse group.

The tool creates three pipelines to iterate over all the tables in the database before loading the data.

The migration activity moves from the SQL database to the Azure Synapse SQL cluster. 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 deployment environment.

The copy statement is used to load data from the scene environment into a custom Synapse array.

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Pipelines in Azure Synaps are used to define an ordered set of tasks to complete an increasing load pattern. Buttons are used to start the pipeline, which can be activated manually or at a specific time.

Since the sample database in our reference architecture is not large, we created duplicate tables without partitions. Using distributed tables for production workloads will improve query performance. See the guide to designing distributed tables in Azure Synaps. The example scripts execute the query using the static resource class.

In a production environment, consider creating deployment tables with round-robin distribution. Then transform the data into production tables with clustered column store indexes that provide the best overall query performance. Milkstore indexes are optimized for applications that scan many records. The dairy index doesn’t work very well for a single search, i.e. searching for a single row. If you need to perform a single lookup frequently, you can add a clustered index to a table. Singleton searches can run faster using a nonclustered index. However, standalone requirements are typically less in data warehouse scenarios than in OLTP workloads. For more information, see Indexing tables in Azure Synaps.

Open Source Business Intelligence Tools Transforming Coverage Models

Data types. In this case, consider an array or 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, notably the provided Azure Synapse cluster:

This scenario is provided by the DirectQuery control panel, because the amount of data used and the complexity of the model are not large, so we can provide a good user experience. DirectQuery submits queries to a powerful underlying computing engine and uses extensive open source security capabilities. Also, using DirectQuery ensures that the results always match the latest source data.

The import mode provides the fastest query response time and should be considered when the model is completely in Power BI’s memory, allowing for data lag between updates and some complex changes between the source system and the final model. In this case, end users want full access to the latest data without Power BI update delays and to all historical data that is larger than the Power BI database, ranging from 25 to 400 GB depending on size. Since the data model in a particular SQL cluster is already in the star schema and does not need to be changed, DirectQuery is a good choice.

Power BI Premium Gen2 gives you the ability to manage large models, paginated reports, deployment pipelines, and integrated Analysis Services endpoints. You can also have exclusive opportunities with a unique value proposition.

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As the BI model grows or the complexity of the dashboard increases, you can switch to composite models and import parts of lookup tables through hybrid tables and pre-aggregated data. Enabling the lookup cache in Power BI for imported databases is an option, as is the use of duplicate tables for mode feature.

Within the federated model, the databases act as a virtual transition layer. When a user interacts with a visualization, Power BI Synaps SQL stacks generate SQL queries in dual storage: in-memory or direct queries, depending on which is more efficient. The engine determines when to move directly from memory to queries and pushes the logic to the Synapse SQL pool. Depending on the content of the demand tables, they can act as stored (delivered) or non-stored composite models. Choose which table to store and combine data from one or more DirectQuery sources and/or combine data from a mix of DirectQuery sources and imported data.

These ideas implement the pillars of the Azure Well-Architected Framework, a set of principles that can be used to improve the quality of workloads. For more information, see the Microsoft Azure Good Architecture Framework.

Open Source Business Intelligence Tools Transforming Coverage Models

Security ensures that your valuable data and systems are protected against malicious attacks and misuse. See the Security column for more information.

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The frequent headlines of data breaches, malware infections, and malicious code injection are among the broad 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 get it wrong.

This scenario addresses the most demanding security issues using a combination of layered security controls: network, identity, privacy, and authorization. While most of the data is stored in a provisioned Azure Synapse cluster, Power BI uses DirectQuery through single sign-on. You can use Microsoft Entra ID for authentication. There are also extensive security controls for group data access.

Cost optimization is about finding ways to reduce unnecessary costs and improve operational efficiency. See the cost optimization column for more information.

This section provides pricing information for the various services included in this solution and outlines the data sets and decisions made for this scenario.

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Azure Synapse Analytics’ serverless architecture allows you to scale your compute and storage levels independently. Billed resources are charged based on usage, and you can increase or suspend these resources as needed. Storage resources are charged by the terabyte, so the more data you use, the higher your costs will be.

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

The source for the pipeline core is activated on a daily basis for all entities (tables) in the database. There are no data streams in the script. There are no operating costs because there are less than 1 million pipeline transactions per month.

Open Source Business Intelligence Tools Transforming Coverage Models

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