📚 Append Queries Explained: How Power BI Combines Tables to Build a Staging Layer

📚 Append Queries Explained for Beginners

Understanding One of the Most Important ETL Concepts in Power BI

No Technical Background Required

🤔 A Common Beginner Question

When learners reach the Append Queries step in Power BI, one question appears almost immediately:

Did Power BI join the tables together?

The answer is:

✅ No.

Append does not join tables.

Append simply places the rows of one table underneath another table.

📖 Let’s Understand with a Simple Example

Imagine you have two Excel sheets.

📄 Sheet 1

Invoice
1001
1002
1003

📄 Sheet 2

Invoice
1004
1005
1006

Now imagine Power BI performs an Append operation.

🚀 Result After Append

Invoice
1001
1002
1003
1004
1005
1006

The rows from Sheet 2 are simply added below the rows from Sheet 1.

🎯 The Easiest Way to Remember Append

Append = Add More Rows

✅ Append works vertically.

✅ It increases the number of rows.

❌ It does not add new columns.

⚠ Append vs Join (Merge)

Many beginners confuse Append with Merge.

Let’s simplify it forever.

Append Merge / Join
✅ Adds Rows ✅ Adds Columns
✅ Vertical Combination ✅ Horizontal Combination
✅ Same Structure Required ✅ Relationship Required
📄 Like Stacking Papers 📑 Like Combining Information

📊 What Happened in Our Retail ETL Project?

In the Online Retail II Dataset, we imported two worksheets:

  • 📅 Year 2009–2010
  • 📅 Year 2010–2011

Both tables had exactly the same columns:

  • Invoice
  • StockCode
  • Description
  • Quantity
  • InvoiceDate
  • Price
  • CustomerID
  • Country

Since the structure was identical, Append was the perfect choice.

Power BI simply took all rows from the first table and placed all rows from the second table underneath them.

💡 SQL Developers Will Recognize This

If you come from a SQL background, Append is very similar to:

SELECT * FROM retail_2009_2010
UNION ALL
SELECT * FROM retail_2010_2011;

Think of Append as the Power Query version of UNION ALL.

🏗 Why Do Data Engineers Use Append?

  • ✅ Combine yearly files
  • ✅ Combine monthly files
  • ✅ Combine multiple exports
  • ✅ Build staging layers
  • ✅ Prepare data for transformation
  • ✅ Create unified business datasets

This is one of the most common ETL operations used across:

  • Power BI
  • BigQuery
  • PostgreSQL
  • Snowflake
  • dbt
  • Airflow Pipelines
  • Enterprise Data Warehouses

❤️ DETLENG Tip for Beginners

If you understand how to combine two small Excel tables correctly, you are already learning the same concepts used to process millions of rows in modern data platforms.

The tools become bigger.

The data becomes larger.

But the core idea remains exactly the same.

🚀 One Dataset. Seven ETL Journeys. Endless Possibilities.

At DETLENG, we believe learning should feel practical, not complicated.

Whether you know enterprise cloud platforms or only understand a small 5×5 Excel sheet, every expert starts from the same place:

One Row.

One Table.

One Transformation at a Time.

💙 Build. 📚 Learn. ⚙ Engineer. 📈 Grow.

🌐 DETLENG.COM

Popular posts from this blog

Production ETL Pipeline Execution Overview

Image
🚀 Building a Production-Style ETL / ELT Pipeline on Google Cloud A practical engineering walkthrough of designing modern data pipelines using BigQuery, SQL transformations, incremental loading, and production-oriented cloud architecture. Recently, I explored how modern ETL / ELT workflows are designed professionally inside real-world analytics environments using Google Cloud technologies. Instead of focusing only on theoretical concepts, the goal was to understand how raw operational data becomes clean, analytics-ready business data through structured engineering practices. 📘 ETL / ELT Pipeline Overview The pipeline was designed to simulate a production-style analytics workflow where raw business data flows through multiple engineering layers before becoming usable for reporting and decision-making. Dataset / API ↓ Python Extraction Script ↓ Ra...
Read more

Choosing the Right Dataset for a Realistic ETL / ELT Pipeline Project

Image
📊 Choosing the Right Dataset for a Realistic ETL / ELT Pipeline Project Understanding why dataset selection matters in modern cloud data engineering workflows. While exploring Google Cloud Data Engineering workflows, one thing became very clear: a strong ETL / ELT project does not begin with SQL queries or dashboards. It starts with choosing the right dataset. For practical engineering simulations, portfolio development, and production-style pipeline learning, an E-commerce / Online Retail dataset proved to be one of the most effective choices because it naturally contains the kinds of operational problems real data engineers solve daily. 🛒 Example Dataset Structure Typical business columns explored inside the retail dataset included: order_id customer_id product_id category quantity price payment_method order_status city order_timestamp This structure allows realistic simulation of modern engineering workflows used inside analytics syste...
Read more

🔍 Power BI Retail ETL Part 4 | Investigating Data Quality Issues in Online Retail Data

Image
🚀 Power BI Retail ETL Project Part 4 🔍 Investigating Real-World Data Quality Issues in the Online Retail II Dataset Welcome back to the Retail ETL Pipeline Series . In Part 1, we imported the Online Retail II dataset into Power BI. In Part 2, we combined both yearly datasets into a unified staging layer. In Part 3, we performed data profiling to understand the overall health of the dataset. Now comes one of the most important responsibilities of every Data Engineer: 💡 Data Quality Assessment Before building dashboards, KPIs, reports, or business analytics, we must understand the quality of the data itself. Professional Data Engineers never assume data is correct. They investigate. They validate. They challenge assumptions. That is exactly what we did in this phase of the project. 📊 What We Discovered During Data Profiling After combining both years of retail data, our staging layer contained: ✅ More than 1 Million Transaction Records ✅ Multipl...
Read more