Observed Signal · May 17, 2026 · Technical Guide · Source: DEV Community · Impact: 1/5 · Sentiment: Positive
Guide: Connect Power BI to PostgreSQL
This technical how-to explains connecting Microsoft Power BI to PostgreSQL databases for business intelligence, covering both local and cloud deployments. It walks through steps in Power BI Desktop (Get Data → PostgreSQL Database → enter server and database, authenticate, select tables), using Power Query to transform or load data, and creating model relationships (example joins between sales, customers, products, inventory). For cloud setups the guide uses Aiven for PostgreSQL: obtain host/port/db/credentials, download the CA SSL certificate, and enable SSL in Power BI to establish a secure connection. The article also highlights why SQL databases and SSL matter for reliable, scalable analytics and gives an example SQL aggregation query for pre-aggregation before visualization.
Practical step-by-step tutorial for BI practitioners; useful operational guidance but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Power BI is Microsoft’s business analytics platform for connecting to multiple data sources and building interactive visualizations.
- The guide details connecting Power BI Desktop to a local PostgreSQL database by selecting Get Data → PostgreSQL Database and entering server (e.g., localhost), database name and authentication.
- For cloud PostgreSQL (Aiven), users must obtain host, port, database name, username and password and download the provider’s CA SSL certificate to enable secure SSL connections in Power BI.
- Example tables loaded into Power BI in the tutorial include Customers, Products, Sales and Inventory.
- The article explains creating model relationships (e.g., sales.customer_id → customers.customer_id) and using Power Query for data transformation (remove duplicates, rename columns, change types, merges).
Connected Companies & Entities
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Guide: Connect Power BI to SQL Databases
This tutorial explains how to connect Microsoft Power BI to SQL databases, focusing on local PostgreSQL instances and managed cloud PostgreSQL provided by Aiven. It outlines steps in Power BI Desktop to open Get Data, choose PostgreSQL, provide server/database details and credentials, and load tables via the Navigator. For cloud-hosted Aiven PostgreSQL the guide highlights obtaining connection details, downloading and using an SSL certificate, entering connection parameters in Power BI, and loading tables to build a data model. The article also covers why SQL skills matter for querying, filtering, aggregation and table creation, and emphasizes the importance of building correct data models (primary/foreign keys) so Power BI calculations and joins work correctly.
Power BI: Connect, Preview and Transform Any Data
This technical how-to guides data analysts through connecting nine common data sources to Power BI and preparing them with Power Query. It covers connection steps for Excel, CSV, PDF, JSON, SharePoint folders, SQL Server, MySQL, web endpoints, and Azure Analysis Services, and shows how to launch Power Query, preview and profile data (Column Quality, Distribution, Profile), expand hierarchical JSON, authenticate to SharePoint/Azure sources, choose Import vs DirectQuery for databases, and apply transformations before loading to the data model. The article emphasizes profiling data quality, documenting applied transformations, and preserving relationships when combining multiple sources into a single Power BI model.
Practical Guide: Azure PostgreSQL Migration and Tuning
A hands-on tutorial describing how the author completed the Microsoft Applied Skills assessment to configure and migrate to Azure Database for PostgreSQL Flexible Server. The post covers security and monitoring setup (Entra ID authentication, private endpoints, diagnostic settings), restoring an adventureworks sample database with a non-fatal PostgreSQL B-tree index row-size error, creating a cross-region read replica in Canada Central for a primary in Canada East, and query performance tuning using EXPLAIN and targeted index creation.
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