DEV Community: Jesse Ngugi

How to Publish a Power BI Report and Embed It into a Website: A Complete Step-by-Step Guide

Jesse Ngugi — Fri, 03 Apr 2026 10:45:45 +0000

Microsoft Power BI is one of the most powerful business intelligence tools available today. Developed by Microsoft, it allows users to connect to hundreds of data sources, transform raw data using Power Query, create stunning interactive visualizations, and build sophisticated DAX measures for advanced analytics. Whether you are a data analyst in retail, finance, healthcare, or marketing, Power BI turns complex datasets into actionable insights that can be shared across teams.

The publishing and embedding process is the bridge between your local report development in Power BI Desktop and making that report available to end users on the web. Publishing uploads your .pbix file to the Power BI Service (the cloud-based platform at app.powerbi.com), where it becomes a live, refreshable report. Once published, you can generate an embed code — an HTML iframe snippet — that lets you seamlessly integrate the interactive report directly into any website, intranet portal, SharePoint page, or custom web application.

This process is not only straightforward but also highly secure when done correctly. It supports scheduled data refreshes, row-level security (RLS), and collaboration. However, it requires a Power BI Pro or Premium Per User (PPU) license for full sharing and embedding capabilities (free licenses can only publish to “My Workspace” with limited options). In this comprehensive 1,600+ word guide, we will walk through every step with detailed instructions, real-world best practices, troubleshooting tips, and high-quality screenshots to ensure you can successfully publish and embed your reports.

Prerequisites Before You Begin

Before starting, ensure you have:

Power BI Desktop installed (latest version from the Microsoft Store or official download page).
A Microsoft 365 account with Power BI Pro or Premium licensing.
A completed .pbix report file ready for publishing (with proper data modeling, relationships, and visuals).
Access to the Power BI Service (sign in at app.powerbi.com).
For website embedding: basic HTML knowledge or access to a CMS like WordPress, Wix, or Squarespace that supports custom HTML/iframes.

Note: If your report uses DirectQuery or Import mode with large datasets, Premium capacity is recommended for better performance and larger refresh limits.

Step 1: Creating a Workspace in Power BI Service

Workspaces are collaborative containers in the Power BI Service. They act like folders where you can store reports, dashboards, datasets, and dataflows. Unlike “My Workspace” (personal space), shared workspaces allow team members with appropriate licenses to collaborate securely. Creating a dedicated workspace is the recommended first step for professional publishing.

Detailed step-by-step instructions:

Open your web browser and navigate to app.powerbi.com. Sign in with your work or school Microsoft account.
On the left navigation pane, click Workspaces.
At the bottom of the Workspaces list, click the + Create a workspace button.
In the “Create a workspace” pane that appears on the right:
- Enter a unique Workspace name (e.g., “Sales Analytics Team”).
- Add an optional Description.
- Optionally upload a custom Icon (recommended for branding).
- Under Advanced settings, you can enable Premium capacity if available (for larger datasets and AI visuals).
Click Apply or Create.

The new workspace will appear in your list. You can now invite team members via the workspace settings (Manage access) and assign roles: Admin, Member, Contributor, or Viewer.

Why this matters: Workspaces enable governance, version history, and controlled sharing. Without one, your published report stays isolated in My Workspace.

Step 2: Uploading and Publishing Your Report

With the workspace ready, it is time to publish the report from Power BI Desktop to the cloud.

Detailed step-by-step instructions:

Open your .pbix file in Power BI Desktop.
Go to File > Publish > Publish to Power BI.
In the “Publish to Power BI” dialog, sign in if prompted.
Select your newly created workspace from the dropdown list.
Click Select (or Publish in some versions).
Power BI will upload the file, compress datasets if necessary, and publish. You will see a success message with a direct link: “Open report in Power BI Service.”
Click the link to view the live report in the browser.

Once published, the report becomes a cloud asset. You can schedule automatic data refreshes under Settings > Dataset > Scheduled refresh (up to 8 times/day for Pro, 48 for Premium). If your model uses gateway connections for on-premises data, configure the On-premises Data Gateway first.

Best practice tip: Always use a dedicated workspace and publish under a service principal account in enterprise environments to avoid license conflicts.

Step 3: Generating the Embed Code in Power BI Service

Now that the report is live in the service, generate the embed code for website integration.

Detailed step-by-step instructions:

In the Power BI Service, navigate to your workspace and open the published report.
At the top-right of the report canvas, click the ... (ellipsis) menu.
Select Embed > Embed in website or portal (this is the standard public-embed option; for secure enterprise embedding, use Power BI Embedded API later).
A new pane opens displaying the embed code: an HTML <iframe> snippet with your report’s unique URL, width, height, and parameters.
Customize options if needed:
- Allow filters (to show slicers).
- Show navigation (page tabs).
- Fit to page or fixed dimensions.
Click Copy to copy the entire iframe code to your clipboard.
(Optional) For more control, use Embed > Secure embed or generate an embed token via the Power BI REST API for authenticated scenarios.

Important security note: The default “Embed in website or portal” makes the report publicly accessible (anyone with the link can view it). For sensitive data, use row-level security, publish to a private app, or implement Power BI Embedded with Azure AD authentication.

Step 4: Embedding the Report on a Website

Embedding is as simple as pasting the iframe code into your website’s HTML.

Detailed step-by-step instructions:

Open your website’s HTML editor (or CMS code block):
- For static HTML sites: edit the .html file.
- For WordPress: add a Custom HTML block.
- For SharePoint: use the Embed web part.
Paste the copied iframe code inside the <body> section where you want the report to appear. Example code:

<iframe 
  width="100%" 
  height="800" 
  src="https://app.powerbi.com/reportEmbed?reportId=xxxxxxxx&groupId=yyyyyyyy" 
  frameborder="0" 
  allowFullScreen="true">
</iframe>

Adjust width and height for responsiveness (use CSS media queries or width="100%" with a container div).
Add styling for seamless integration:

<div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;">
  <iframe 
    style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;" 
    src="YOUR_EMBED_URL" 
    frameborder="0" 
    allowfullscreen>
  </iframe>
</div>

Save and publish the page. Test the embed on desktop and mobile devices.
Optional enhancements:
- Add JavaScript to handle report events (e.g., filtering via postMessage API).
- For production websites, consider Power BI Embedded (Azure service) for custom authentication and branding removal.

Troubleshooting common issues:

Blank iframe: Check browser CORS policy or embed URL parameters.
“Report not found”: Verify workspace permissions.
Slow loading: Optimize your data model and use Direct Lake mode if on Premium.

Advanced Considerations and Best Practices

For enterprise-scale embedding, move beyond simple iframes to Power BI Embedded capacity in Azure. This allows you to embed reports into custom applications without requiring viewers to have Power BI licenses. You generate embed tokens programmatically via REST APIs and can white-label the experience completely.

Always implement:

Row-level security (RLS) to show users only their data.
Automatic page refresh settings.
Usage monitoring via the Power BI admin portal.
Mobile-optimized layouts (Power BI reports automatically adapt, but test thoroughly).

Common pitfalls include exceeding dataset size limits (1 GB for Pro, 10 GB+ for Premium) and forgetting to set up gateway refreshes for on-premises sources.

Conclusion: Key Insights

Publishing a Power BI report and embedding it into a website democratizes data access, turning static PDFs into live, interactive experiences. The entire process — from workspace creation to iframe integration — can be completed in under 15 minutes once you are familiar with the UI. The combination of Power BI Desktop’s authoring power and the Service’s cloud scalability makes it an unbeatable solution for organizations of any size.

Key insights to remember:

Always publish to a shared workspace for collaboration and governance.
Use “Embed in website or portal” for quick public sharing; upgrade to Power BI Embedded for secure, licensed-free viewing.
Test embeds across devices and browsers before going live.
Monitor performance and security — a well-embedded report can drive business decisions instantly.
Keep your data model optimized; poor modeling causes slow embeds regardless of hosting.

By following this guide, you now have the complete technical knowledge to publish professional-grade Power BI reports and seamlessly integrate them into any website. Start experimenting today — your next data-driven web experience is just a few clicks away.

This article is also available on:

Medium: ngugiauraa
Linkedin: Jesse Ngugi
Github: ngugiauraa

Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained

Jesse Ngugi — Tue, 31 Mar 2026 07:11:35 +0000

Data modeling is the foundational process of defining how different tables in a dataset relate to one another so that Power BI can efficiently filter, aggregate, and analyze information. In Power BI, data modeling happens primarily in three places: Power Query Editor (for joins and transformations), Model View (for relationships), and Report View (for visuals and DAX). A well-designed model turns raw data into fast, accurate insights—reducing report load times from minutes to seconds and preventing incorrect aggregations.

At its core, data modeling answers: “Which tables contain facts (numbers we measure) and which contain dimensions (context for filtering)?” It also defines how filters propagate across tables. Poor modeling leads to slow reports, wrong numbers, or circular dependency errors. In real-world scenarios—like a retail company analyzing $50M in annual sales—proper modeling enables slicing sales by product, customer segment, or date hierarchy in milliseconds.

Fact Tables vs. Dimension Tables

Every strong model starts with this distinction:

Fact tables store measurable, numeric events (sales amount, quantity sold, profit). They are usually tall and narrow, with many rows and mostly numeric columns. They contain foreign keys linking to dimensions.
Dimension tables provide descriptive context (product name, customer city, date). They are short and wide, with fewer rows and many text/attribute columns. They contain the primary keys.

Real-life example: In a retail analytics project, FactSales (10 million rows) holds SalesAmount, Quantity, OrderDateKey. DimProduct (5,000 rows) holds ProductID, ProductName, Category, Color. Analysts filter FactSales by DimProduct[Category] without ever touching the fact table directly.

Fact tables are optimized for aggregation; dimensions are optimized for filtering and grouping.

SQL Joins in Power BI (Power Query Editor)

Power BI performs joins inside Power Query Editor during data loading. These are true SQL-style joins that physically combine or filter rows into a new table. Power Query supports all standard join types via Merge Queries.

Here are all six joins with explanations, real-life examples, and how they work:

INNER JOIN

Returns only rows that match in both tables.

Power Query step: Merge Queries → Join Kind: Inner.

Real-life: Join FactSales with DimProduct to get only sold products. Unsold products (no sales) are excluded.

SQL equivalent: SELECT * FROM FactSales INNER JOIN DimProduct ON FactSales.ProductKey = DimProduct.ProductKey
LEFT OUTER JOIN (Left Join)

Returns all rows from the left table + matching rows from the right. Non-matches get nulls.

Real-life: Keep all sales rows and bring in product details; if a sale has a missing product, still keep the sale.

Most common in Power BI.
RIGHT OUTER JOIN (Right Join)

Opposite of Left: all rows from right table + matches from left.
FULL OUTER JOIN

All rows from both tables; nulls where no match.

Real-life: Compare two sales systems to find discrepancies (sales in one but not the other).
LEFT ANTI JOIN

Rows from left table that have NO match in right.

Real-life: Find sales records with missing product master data (data quality check).
RIGHT ANTI JOIN

Rows from right table with no match in left.

Step-by-step in Power BI:

Open Power Query Editor (Transform Data).
Select first table (e.g., FactSales).
Home → Merge Queries.
Choose second table and matching columns (ProductKey).
Select Join Kind (e.g., Left Outer).
Click OK → Expand the new column to bring in desired fields.
Close & Apply.

These joins happen at refresh time and create a new physical table or column set.

Joins vs. Relationships: The Critical Difference

Joins (Power Query) physically merge data at load time. They increase model size and are best for one-time transformations or cleaning.

Relationships (Model View) are virtual links. They do not duplicate data. Instead, they tell the DAX engine and visuals how filters should flow. Relationships are evaluated at query time, making them far more efficient for large models.

You almost always do light joins in Power Query (to clean dimensions) and then build relationships in the model for analysis.

Power BI Relationships Explained

In Model View, you create relationships by dragging columns or via Manage Relationships.

Key properties:

Cardinality:
- 1:1 (rare, e.g., customer to customer details)
- 1:* (most common: dimension to fact)
- : (many-to-many – avoid if possible; use bridge tables)
Cross-filter direction:
- Single (default): filter flows from dimension → fact only.
- Both: bidirectional (use sparingly; can cause performance issues and ambiguity).
Active vs Inactive:
- Active (solid line): default filter path.
- Inactive (dotted line): used only when explicitly activated via DAX USERELATIONSHIP().

Step-by-step to create relationships:

Switch to Model View.
Drag the key column from dimension to fact (e.g., DimDate[DateKey] to FactSales[OrderDateKey]).
Or: Modeling → Manage Relationships → New.
Set cardinality and cross-filter direction.
For inactive: create the relationship, then edit and uncheck “Make this relationship active.”

Real-life: A sales model has one DimDate but needs filtering by both Order Date and Ship Date. Create two relationships: one active (Order), one inactive (Ship). In DAX measures, use USERELATIONSHIP(FactSales[ShipDateKey], DimDate[DateKey]).

Data Modeling Schemas

Star Schema (Recommended)

One central fact table connected to multiple dimension tables. Simple, fast, easy to understand.

Use case: 90% of business reports. Retail sales, finance P&L, HR headcount.
Snowflake Schema

Dimension tables are normalized further (e.g., DimProduct → DimCategory → DimSubCategory).

Use case: When dimensions are very large and shared across multiple facts (enterprise data warehouses). Slightly slower than star due to extra joins.
Flat Table / DLAT (Denormalized Large Analytical Table)

Everything flattened into one giant table.

Use case: Very small models (<1M rows), Direct Lake mode in Power BI, or when maximum query speed is needed and model size is not a concern. Great for Excel users transitioning to Power BI.

Best practice: Start with Star. Only snowflake if dimension size forces it.

Role-Playing Dimensions

A single dimension used in multiple contexts (e.g., DimDate for OrderDate, ShipDate, DueDate).

Solution in Power BI:

Duplicate the dimension table in Power Query (reference query) and rename (e.g., DimOrderDate, DimShipDate).
OR: Create one table + multiple inactive relationships + USERELATIONSHIP() in measures.

Common Modeling Issues & Fixes

Circular relationships: Caused by bidirectional filters. Fix: set to Single direction or use bridge tables.
Many-to-many without bridge: Leads to incorrect totals. Fix: create a bridge table with unique combinations.
Incorrect cardinality: Power BI guesses wrong. Always double-check.
Performance bottlenecks: Too many inactive relationships or giant flat tables. Monitor with Performance Analyzer.
Ambiguous relationships: Multiple paths between tables. Fix: make one active, others inactive.

Step-by-Step: Building a Complete Model in Power BI

Load data via Get Data.
In Power Query: clean, merge light joins if needed, set data types.
Close & Apply.
Switch to Model View → Auto-detect relationships (or manually create).
Hide unused columns, create hierarchies (Year > Quarter > Month in DimDate).
Write DAX measures using relationships.
Test in Report View with slicers.

A well-modeled Power BI file with 10+ tables can answer complex business questions instantly while a poorly modeled one struggles with basic totals.

Mastering data modeling separates basic dashboard creators from true analytics professionals. Star schemas, proper cardinality, and strategic use of relationships and joins are the keys to scalable, trustworthy insights in Power BI.

This article is also available on:

Medium: ngugiauraa
Linkedin: Jesse Ngugi
Github: ngugiauraa

The Role of Microsoft Excel in Real-World Analytics:

Jesse Ngugi — Wed, 25 Mar 2026 21:21:47 +0000

Microsoft Excel remains a cornerstone of data analytics in industries ranging from finance and retail to healthcare and supply chain management. Despite the rise of specialized tools like Python, R, Tableau, and Power BI, Excel's accessibility, flexibility, and integration with Microsoft 365 make it indispensable for analysts handling datasets from thousands to millions of rows. In real-world scenarios, Excel powers everything from ad-hoc reporting and KPI tracking to complex financial modeling and predictive forecasting. According to industry reports and case studies, small-to-medium enterprises (SMEs) and even large corporations rely on Excel for 70-80% of initial analytics workflows before scaling to enterprise solutions.

This technical article explores how Excel is applied in practical analytics, with step-by-step explanations of core features, formulas (presented in KaTeX for precision), and illustrative examples. We will use a consistent sample dataset: a retail sales table with columns Date, Region, Product, Sales Amount, and Units Sold (e.g., 10,000 rows of transactional data). All visuals are rendered as realistic Microsoft Excel 365 screenshots for clarity.

Data Acquisition and Import

Real-world analytics begins with ingesting heterogeneous data sources. Excel supports direct imports via Get Data (Power Query) from CSV, JSON, SQL databases, web APIs, and even SharePoint or Azure.

Step-by-step process:

Go to Data > Get Data > From File > From Text/CSV.
For web data: From Web and enter URL.
For databases: Use ODBC connectors.

In practice, a supply chain analyst might import daily inventory feeds from ERP systems. Power Query handles schema drift automatically. For our sample dataset:

Load CSV into Power Query Editor.
Promote headers and change data types (e.g., Date to Date, Sales Amount to Currency).

This ETL foundation prevents downstream errors. Formula tip: After import, use =TEXTBEFORE(A2, " ") or modern dynamic arrays like =FILTER(Table1, Table1[Region]="East") for quick subsetting.

Data Cleaning and Transformation

Raw data is rarely analysis-ready. Excel's Power Query and built-in functions tackle 80% of cleaning tasks in business settings, such as removing duplicates in customer records or standardizing addresses.

Key techniques:

Power Query transformations: Remove duplicates, split columns (e.g., split Product into Category/Subcategory), unpivot columns for normalized format.
Formulas for cleaning:
- Trim whitespace: ( =TRIM(A2) )
- Handle errors: ( =IFERROR(VLOOKUP(...), "N/A") )
- Date standardization: ( =DATEVALUE(TEXT(A2, "mm/dd/yyyy")) )
- Conditional cleaning: ( =IF(AND(B2>0, C2<>"Invalid"), B2, 0) )

In a healthcare analytics case (e.g., patient records), analysts use =CLEAN() to strip non-printable characters from imported EHR exports and =PROPER() for name standardization.

Real-world impact: A retail firm reduced data prep time by 60% using Power Query to merge weekly sales from multiple stores, detecting anomalies via =UNIQUE() and conditional formatting (highlight duplicates >3 occurrences).

Exploratory Data Analysis (EDA) with Formulas and PivotTables

EDA in Excel reveals patterns quickly. PivotTables are the Swiss Army knife—handling aggregation without formulas for massive datasets.

Core formulas for EDA:

Aggregations with conditions: [ =SUMIFS(D2:D10001, B2:B10001, "East", C2:C10001, "Laptop") ] (Sums sales for East region, Laptop product.)
Averages and counts: [ =AVERAGEIF(E2:E10001, ">0", D2:D10001) ] (Average sales per unit sold.)
Ranking: ( =RANK.AVG(D2, $D$2:$D$10001, 0) )
Text-based insights: ( =COUNTIF(B2:B10001, "East") )

PivotTable example:
Insert > PivotTable from the sales table. Drag Region to Rows, Product to Columns, Sales Amount to Values (Sum). Add slicers for interactive filtering. Calculated fields:

{Profit Margin} = {Sales Amount} * {0.35}/{Units Sold}}

In marketing analytics, teams use =CORREL() for campaign ROI correlation:

[ =CORREL(G2:G100, H2:H100) ] (Ad spend vs. conversions).

A real-world retail case study (inspired by business intelligence examples) involved a chain analyzing seasonal trends. PivotTables identified a 42% sales spike in Q4 for electronics, guiding inventory allocation.

Statistical Analysis and Predictive Modeling

Excel's Data Analysis ToolPak (enable via File > Options > Add-ins) unlocks regression, ANOVA, and histograms—critical for finance and operations.

Key statistical formulas:

Descriptive stats: [ =STDEV.S(D2:D10001) ] (Sample standard deviation of sales.) [ =MEDIAN(E2:E10001) ] (Median units sold.)
Linear regression (built-in or manual): Use LINEST array formula: [ =LINEST(D2:D10001, E2:E10001, TRUE, TRUE) ] Returns slope, intercept, R², etc. For forecasting: [ =FORECAST.ETS(A10002, D2:D10001, A2:A10001) ] (Exponential smoothing for next period sales.)
Correlation matrix: ( =CORREL() ) across multiple pairs.

In financial risk analytics (e.g., portfolio variance), analysts compute VaR approximations with Monte Carlo via RAND() and NORM.INV.

Case study integration: In a supply chain optimization scenario (similar to documented ERP-to-Excel workflows), a logistics firm used regression on historical shipment data to predict delays. The model ( y = \beta_0 + \beta_1 x_1 + \epsilon ) (where ( x_1 ) = fuel price) achieved 85% accuracy, saving $250K annually in penalties.

Data Visualization and Interactive Dashboards

Visuals turn insights into decisions. Excel's charts, conditional formatting, and slicers create executive dashboards used daily in boardrooms.

Techniques:

Dynamic charts linked to PivotTables.
Sparklines: Insert > Sparklines for inline trends.
Conditional formatting: Data bars for sales performance, color scales for variance.
Dashboard layout: Use Insert > Icons and shapes for professional polish.

Example KPI formula in dashboard cell:

[ =TEXT(SUM(D2:D10001)/1000000, "$0.0") & "M" ] (Formats total sales as $1.2M.)

In a marketing team’s real-world use, a campaign dashboard with TREND() lines forecasted a 15% uplift, visualized via combo charts.

Advanced Analytics: Power Pivot, DAX, and Automation

For larger datasets (>1M rows), Power Pivot enables in-memory modeling. Relationships replace VLOOKUPs.

DAX examples (in Power Pivot):

Measure: Total Sales = SUM(Sales[Sales Amount])
Time intelligence: YTD Sales = TOTALYTD([Total Sales], Dates[Date])

VBA automation handles repetitive tasks, e.g., monthly report generation.

Sample VBA (in Visual Basic Editor):

Sub GenerateReport()
    Dim ws As Worksheet
    Set ws = Sheets("Dashboard")
    ws.PivotTables(1).RefreshTable
    MsgBox "Report updated!"
End Sub

A business analyst case (e.g., from documented Excel-for-business scenarios) automated invoice processing for 5,000 records, reducing manual effort by 90%.

Real-World Case Studies and Best Practices

Retail forecasting: A mid-sized e-commerce firm used Excel's FORECAST.ETS and PivotTables on 2 years of data to optimize stock levels, cutting overstock by 25%.
Finance modeling: Banks employ scenario analysis with Data Tables (What-If) and Goal Seek for NPV calculations: ( =NPV(0.08, C2:C20) ).
Healthcare: Patient outcome tracking via PivotTables and statistical functions for A/B testing treatments.

Best practices:

Always use tables (Ctrl+T) for dynamic ranges.
Version control with OneDrive.
Document with cell comments and named ranges.
Limitations: Excel caps at ~1M rows natively; pair with Power BI for scale.

Conclusion

Excel transforms raw data into actionable intelligence across real-world analytics. From basic SUMIFS to advanced DAX and VBA, it equips analysts to drive decisions efficiently. Mastery of these tools—formulas, Power Query, PivotTables, and modeling—remains a high-ROI skill. As datasets grow, Excel evolves with AI features like Copilot, ensuring its relevance for years ahead. Practice with sample datasets to build proficiency; the next breakthrough analysis starts in your spreadsheet.
Since I started my journey in data science and analytics, excel has helped me to clean data faster and helped me analyze disorganized data into meaningful data.

This article is also available on:

Medium: ngugiauraa
Linkedin: Jesse Ngugi
Github: ngugiauraa

A Beginner’s Guide to Installing PostgreSQL and Connecting It to DBeaver in minutes

Jesse Ngugi — Mon, 16 Mar 2026 18:50:02 +0000

If you’re a beginner developer, this setup will become part of your daily workflow.

Databases are the silent engines powering most modern applications. Whether you’re building a web app, analyzing data or learning backend development. Understanding how to install and manage a database is an essential skill.

But many beginners get stuck during installation or when trying to connect it to a database client.

In this guide, you’ll learn how to install PostgreSQL and connect it to DBeaver in just a few minutes — even if you’ve never worked with databases before.

Two powerful tools that many developers use are PostgreSQL and DBeaver.

PostgreSQL is a powerful open-source relational database system known for its reliability and scalability.

DBeaver, on the other hand, is a universal database management tool that provides a graphical interface for working with databases.

In this guide, I’ll walk you through how to install PostgreSQL and connect it to DBeaver so you can start managing your databases efficiently.

Why Use PostgreSQL and DBeaver?

Before diving into installation, it’s helpful to understand why these tools are commonly used together.

PostgreSQL is trusted by developers worldwide because it is:

Open source and free
Highly reliable
Feature-rich
Suitable for both small projects and large systems

Dbeaver complements PostgreSQL by providing:

A visual database interface
SQL query editing
Database structure exploration
Support for multiple database systems

Together, they create a powerful environment for database development and management.

Step 1: Installing PostgreSQL

Download PostgreSQL
First, download PostgreSQL from the official website PostgreSQL download

Visit the PostgreSQL download page and select the installer for your operating system (Windows, macOS, or Linux).

The installer will include everything needed to run PostgreSQL locally on your computer.

Run the Installer

Once the download is complete:

Launch the installer.
Follow the setup wizard.

Accept the default installation components.

The default installation typically includes:

PostgreSQL Server
pgAdmin (database management tool)
Command line tools

Set the Superuser Password

During installation, you will be asked to create a password for the postgres superuser account.

Example configuration:

Username: postgres
Password: yourpassword

Make sure to save this password securely, because it will be required when connecting to the database later.

The password dosn’t have to be anything complex, just use something simple since nobody is interested in your account and you don’t want to lose your account.

Configure the Default Port

PostgreSQL runs on a default port:

In most cases, you should leave this as the default setting unless you have another service using that port.

Complete Installation

Finish the setup process and allow PostgreSQL to initialize the database cluster.

After installation is complete, PostgreSQL will run automatically in the background.

Step 2: Installing DBeaver

Now that PostgreSQL is installed, the next step is installing DBeaver.

Download the Community Edition of DBeaver from the official website DBeaver download make sure to choose the correct software you’re using before downloading.

The interface may look complex at first, but it becomes intuitive once you start working with databases.

After downloading:

Run the installer.
Follow the installation instructions.
Launch DBeaver once installation is complete.

The interface may look complex at first, but it becomes intuitive once you start working with databases.

Step 3: Connecting PostgreSQL to DBeaver

With both tools installed, the next step is creating a database connection.

Create a New Database Connection

Open DBeaver and follow these steps:

Click Database in the menu.
Select New Database Connection.
Choose PostgreSQL from the list of supported databases (make sure it’s named PostgreSQL, since they are two and the other one goes by “PostgreSQL old”)

Enter Connection Details

You will need to enter the PostgreSQL connection information.

Use the following settings:

Host — localhost

Port — 5432

Database — postgres

Username — postgres

Password — (the password you created on PostgreSQL)

These settings allow Dbeaver to connect to the PostgreSQL server running locally on your computer.

Install the PostgreSQL Driver

The first time you connect, Dbeaver may ask to download the PostgreSQL JDBC driver.

Simply click Download, and the driver will be installed automatically.

Test the Connection

Before finishing the setup, click Test Connection.

If everything is configured correctly, you should see a confirmation message indicating the connection was successful.

Finally, click Finish.

Your PostgreSQL database will now appear in the Database Navigator panel.

Step 4: Verifying the Connection

To ensure everything is working:

Expand the PostgreSQL connection in DBeaver.
Navigate to:

Databases → postgres → Schemas → public → Table

Try creating a new table or running a simple SQL query.

If this works without errors, the connection has been successfully established.

Final Thoughts

Installing PostgreSQL and connecting it to DBeaver is one of the first practical steps in learning database management.

With PostgreSQL handling the data and DBeaver providing a clean graphical interface, developers can easily design databases, write SQL queries, and manage their data efficiently.

Whether you’re building applications, exploring data, or learning backend development, mastering this setup is a valuable skill that will serve you throughout your tech journey.

This article is also available on:

Medium: ngugiauraa
LinkedIn: Ngugi Gichuki