Open Source Business Intelligence Tools Evaluating Asset Protections – This example scenario shows how to import 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 ultimate goal or the first step to a complete innovation with cloud-based components.
The following steps are based on an end-to-end Azure Synapse Analytics scenario. It uses Azure Pipelines to ingest data from a SQL database into Azure Synapse SQL Sets, and then transforms the data for analysis.
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An organization has a large on-premises database stored in a SQL database. The organization wants to use Azure Synapse to analyze and then deliver these insights using Power BI.
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Microsoft Entra authenticates users who connect to Power BI dashboards and apps. Single sign-on is used to connect to a data source in the Azure Synapse provisioning pool. The order happens at the source.
When you run a transform load (ETL) or load transform (ELT) process, it is most efficient to load only the data that has changed since the previous run. It’s called an incremental load, as opposed to a full load that takes 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 , which tracks the new value of some column in the source table, either a date column or a single integer column.
Starting with SQL Server 2016, you can use temporary tables, which are structured 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
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Sentence to a question. Internally, the database engine requests the history table, but it is transparent to the application.
For earlier versions of SQL Server, you can use dynamic data capture (CDC). This method is less convenient than temporary tables, because you have to query the changes table separately, and changes are tracked by a log sequence number, rather than a timestamp.
Temporary tables are useful for quantitative data, which can change over time. Truth tables often represent an immutable transaction, such as an auction, in which case it makes no sense to maintain system version history. Instead, transactions usually have a document that represents the date of the transaction, which can be used as a watermark. For example, in the AdventureWorks data store, the
This scenario uses the AdventureWorks sample database as the data source. An incremental data load pattern is implemented to ensure that only data that has changed or been added since the most recent execution of the pipeline is loaded.
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The metadata-driven copy tool built into Azure Pipelines additionally adds all the tables in our relational database. As you navigate through the wizard-based experience, you can connect the Copy Data tool to the source database and configure incremental or full loading for each table. The Copy Data tool then creates pipelines and SQL scripts to generate the control table needed to store the data for the incremental load process, for example the water/column high value for each table. Once these scripts run, the pipeline is ready to load all tables from the source datastore into the Synapse trust pool.
The tool creates three pipelines to perform replication on all tables in the database, before loading the data.
The copy task copies data from the SQL database to the Azure Synapse SQL pool. In this example, because the SQL database is in Azure, we use the Azure sync session to read the data from the SQL database and write the data to a specific staging area.
The copy statement is then used to transfer data from the programming environment to the Synapse trust pool.
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Pipelines 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 the pipeline, which can be triggered manually or at a specific time.
As the sample database in our reference architecture is not large, we create replicated tables without partitions. For production workloads, the use of distribution tables is possible to improve query performance. See Guide to setting up shared tables in Azure Synapse. The example scripts run the queries using the static resource class.
In a production environment, consider creating schedule tables with distribution in all cases. The data is then transformed and loaded into production tables with column store indexes, providing better overall query performance. Columnstore indexes are optimized for queries that check multiple records. Columnstore indexes do not work well for individual lookups, i.e. looking up a row. If you need to perform frequent individual lookups, you can add a non-clustered index to a table. Singleton searches can run much faster using a non-clustered index. However, individual lookups are less common in data warehouse scenarios than OLTP workloads. For more information, see Index tables in Azure Synapse.
Data types. In that case, consider a compound or composite index. You can put those columns in a separate table.
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Power BI supports several options for connecting to data sources in Azure, in particular the Azure Synapse provisioning pool:
This scenario is delivered 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 uses various security capabilities at the source. In addition, using DirectQuery ensures that the results are always consistent with the most recent source data.
Import mode provides the fastest query response time and should be considered when the model fits fully into dynamic BI memory, we can tolerate data lag between updates, and there may be some complex changes to the system source and final model. In this case, end users want full access to the latest data without BI power cooling delays and to all historical data, which is larger than the BI power database can hold: between 25 and 400 GB , according to the power. size As the dedicated SQL pool data model is already in a star schema and does not require modification, DirectQuery is a suitable choice.
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When the BI model grows or the complexity of the dashboard, you can switch to composite models and start importing parts of the lookup tables, through hybrid tables and some aggregate data. Enabling query caching within Power BI for imported datasets is an option, as is using two tables for the storage location property.
Within the cluster model, the databases act as a virtual staging layer. When the user interacts with views, Power BI performs SQL queries to Synapse SQL in two storage pools: in memory or a direct query whichever is more efficient. The engine decides when to switch from an in-memory query to a direct query and pushes the logic to Synapse’s SQL pool. Depending on the contents of the query tables, they can act as either cached (imported) or non-dumped collection models. Select and choose which table to cache, combine data from one or more DirectQuery sources, and/or combine data from a combination of DirectQuery sources and imported data.
These plans implement the pillars of the Azure Well-Architected Framework, which is a set of guidelines that can be used to improve operational quality. For more information, see Microsoft Azure Framework Architecture.
Security provides guarantees against attacks and misuse of your valuable data and systems. For more information, see the Security Overview column.
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Recurring themes of data breaches, malware infections and malicious code injection are among the long list of security concerns for companies looking to innovate in the cloud. Business customers need a cloud provider or service solution that can solve their problems because they can’t afford to get it wrong.
This scenario describes the most demanding security issues using a combination of layered security controls: network, identity, privacy, and authorization. Most of the data is stored in the provided Azure Synapse pool, with Power BI using DirectQuery using single sign-on. You can use your Microsoft Sign in ID for authentication. There are also several security controls for data management of the pools provided.
Cost optimization is about finding ways to reduce unnecessary expenses and improve operations. For more information, see the Cost Optimization Overview column.
This section provides information on the cost of the different services involved in this solution, and mentions the decisions made for this scenario with a sample database.
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Azure Synapse’s wireless architecture analysis allows you to scale your compute and storage tiers independently. 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 cost of a pipeline:
For the pipeline core, it is enabled on a daily schedule for all resources (table) in the source database. A scenario does not contain a data stream. There are no service fees because there are less than 1 million jobs with monthly pipelines.
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