Welcome to the Iamdata Solutions Asset Management Monthly Newsletter - December 2023
I’m from a MS SQL Server background, so whenever possible, I want to perform as much of my data cleansing and modelling as close to the data source as possible. In most cases, the data I need to build the Power BI reports for my clients are being managed and maintained in various corporate systems – which are primarily SQL Server databases, and incidentally, more often than not , are disparate from each other, so it makes sense to do most of the leg work right there in the SQL Server.
For one thing, I find it much easier to work with the data in the SQL Server where I have all the SQL tools readily available to help me cleanse and model the data before I push it up into Power BI. I can be confident that the data we’re bringing into Power BI is just as we want it, which will contribute to a smoother Power BI workflow and more reliable data-driven insights. Using SQL to pull and manipulate data in Power BI enhances data quality, data performance, and increases security, providing a solid foundation for creating insightful and accurate Power BI visualisations and reports.
I’ve explained why I like to fix most of the data issues at source, so let’s have a closer look at some of the benefits of querying and fixing data issues with SQL:
Data Integrity
SQL queries allow you to perform data validation and cleansing operations, ensuring that the datasets we are using in our Power BI reports are accurate and consistent. You can identify and address issues like missing values, duplicates, and incorrect data, which helps maintain data integrity.
Data Transformation
SQL enables you to perform various data transformations during the data retrieval process. You can aggregate, filter, join, and manipulate data from different sources to create a unified and clean dataset for analysis in Power BI.
Performance Optimisation
We can tap into SQL's performance optimisation capabilities. Writing good SQL allows you to optimise data retrieval performance by fetching only the necessary data, reducing unnecessary processing and network overhead. This helps in faster data loading and report rendering in Power BI.
Custom Calculations
SQL allow you to perform complex calculations and data aggregations directly in the database. This can be more efficient than performing these calculations in Power BI, especially when dealing with large datasets.
Security and Access Control
We can leverage the SQL database's security features, ensuring that users only have access to the data they are authorised to see. This helps in maintaining data privacy and complying with data access regulations.
Schedule and Automation
We can build scheduled tasks to run SQL queries at specific intervals to keep the data in Power BI up-to-date. This automation reduces manual effort and ensures that the reports and dashboards are always using the latest data.
Easier Data Joins
Power BI will only allow one relationship per table. In SQL, we can build complex queries that allows you to join data from multiple tables or datasets, making it easier to work with complex data models in Power BI. This simplifies the process of creating relationships between different tables in Power BI.
Query Optimisation
SQL databases often have built-in query optimisation capabilities, which means that SQL queries can be efficiently executed, further improving performance.
Reusability
We can build Stored Procedures in SQL, which are simply a set of SQL queries. Stored Procedures can be saved and reused, making it easier to replicate data retrieval and transformation processes in Power BI for other reports and dashboards.
Data Consistency
SQL queries allow you to establish data consistency rules, ensuring that data from various sources is standardised and conforms to a common schema. This consistency simplifies report creation and interpretation.
Error Handling
SQL provides robust error-handling mechanisms, enabling you to manage exceptions and errors during data extraction and transformation. This can help identify and address issues promptly, preventing data-related problems from propagating to your Power BI reports.
Scalability
SQL Server is well-suited for handling large volumes of data. By performing data preprocessing in SQL, you can take advantage of SQL Server's scalability features and avoid overburdening Power BI with resource-intensive operations.
Historical Data
SQL queries can easily handle historical data and time-based queries. You can create rolling averages, compare data over time, and perform other time-series analyses, providing valuable insights in your Power BI reports. I know you can easily do this in Power BI, but it’s quite nice if you can pull the data into Power BI ready to consume.
Data Partitioning
SQL databases offer data partitioning capabilities, which can enhance the performance of your data retrieval processes. You can partition data tables based on specific criteria, improving query response times when filtering data in Power BI.
Data Versioning
SQL databases support versioning and auditing, enabling you to track changes to data over time. This feature can be useful for maintaining data lineage and ensuring data traceability in your Power BI reports.
Data Indexing
SQL databases allow for efficient indexing of data, which can significantly speed up data retrieval. You can create appropriate indexes on tables to improve the speed of querying the data in Power BI.
Complex Data Structures
If your data sources involve complex data structures like JSON or XML, SQL queries can be used to parse and flatten these structures into a more tabular format for easy consumption in Power BI.
Data Validation
SQL queries can be used to implement business logic and data validation rules, ensuring that only valid data is passed to Power BI. This helps in preventing data quality issues from affecting your reports.
So, there you have it, you can see now why I love using SQL for data preparation and retrieval. There are so many good tools available in SQL to not only enhance the quality and performance of your Power BI reports but also establish a more streamlined and efficient data workflow from source to visualisation.
This approach ensures that your data-driven insights are accurate, consistent, and readily available for decision-making. 📈📊
I have worked on many different projects with my Local Government clients, from designing and developing Power BI Reports, to building SQL Server databases for spatial data, to managing and maintaining the GIS and the Asset Management systems. If you'd like to discuss how we might work together, then please email Jill at ➡️ jill.singleton@iamdata.solutions
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