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@@ -0,0 +1,365 @@
+---
+title: "Azure Data Factory Pipeline: Build Your First ETL in 10 Minutes"
+authors: [Aditya-Singh-Rathore]
+sidebar_label: "Azure Data Factory — Build Your First ETL"
+tags: [azure-data-factory, adf, etl, data-pipeline, data-engineering, azure, blob-storage, adls, copy-activity, linked-service, dataset, trigger]
+date: 2026-05-06
+
+description: Azure Data Factory is Microsoft's cloud-native ETL service — a visual, no-code platform for moving and transforming data at scale. This step-by-step guide walks you through building your first real pipeline in under 10 minutes, explaining every concept along the way.
+
+draft: false
+canonical_url: https://www.recodehive.com/blog/azure-data-factory-build-first-etl
+
+meta:
+ - name: "robots"
+ content: "index, follow"
+ - property: "og:title"
+ content: "Azure Data Factory Pipeline: Build Your First ETL in 10 Minutes"
+ - property: "og:description"
+ content: "Azure Data Factory is Microsoft's cloud-native ETL service. Here's how to build your first real pipeline in under 10 minutes — step by step."
+ - property: "og:type"
+ content: "article"
+ - property: "og:url"
+ content: "https://www.recodehive.com/blog/azure-data-factory-build-first-etl"
+ - property: "og:image"
+ content: "/img/blogs/adf-cover_image.png"
+ - name: "twitter:card"
+ content: "summary_large_image"
+ - name: "twitter:title"
+ content: "Azure Data Factory Pipeline: Build Your First ETL in 10 Minutes"
+ - name: "twitter:description"
+ content: "Azure Data Factory is Microsoft's cloud-native ETL service. Here's how to build your first real pipeline in under 10 minutes."
+ - name: "twitter:image"
+ content: "/img/blogs/adf-cover_image.png"
+
+---
+
+
+
+# Azure Data Factory Pipeline: Build Your First ETL in 10 Minutes
+
+The first time someone asked me to "build an ETL pipeline," I nodded confidently and then quietly searched "what is ETL" on my second monitor.
+
+Extract. Transform. Load.
+
+Three words that describe something every data team does dozens of times a day — pulling data from somewhere, doing something to it, and putting it somewhere more useful. Simple idea. Historically, painful to implement.
+
+You'd write Python scripts that broke when the source schema changed. You'd schedule them with cron jobs that nobody monitored. You'd debug failures at 2am by reading raw logs.
+
+**Azure Data Factory** (ADF) exists to replace all of that with a visual, managed, scalable pipeline service, one where you can build a working ETL in minutes, not days, and monitor it from a dashboard instead of a terminal.
+
+This guide walks you through everything, the concepts, the components, and a complete step-by-step pipeline you can build right now.
+
+
+
+## What Is Azure Data Factory?
+
+Azure Data Factory is Microsoft's cloud-native ETL and data integration service. It lets you build **data pipelines**, workflows that move data from one place to another, transform it along the way, and load it into a destination where it's actually useful.
+
+The key word is *visual*. ADF gives you a drag-and-drop canvas where you connect activities, configure sources and destinations, and build complex workflows without writing infrastructure code.
+
+Under the hood, it handles:
+- Connecting to 90+ data sources (databases, APIs, files, SaaS apps)
+- Moving data at scale using managed compute
+- Scheduling and triggering pipeline runs
+- Monitoring, alerting, and retry logic
+
+Think of it as the **orchestration layer** of your Azure data stack, the thing that decides what data moves where, when, and how.
+
+
+
+
+## The 4 Concepts You Need to Know First
+
+Before you touch the UI, these four concepts need to click. Everything in ADF is built on them.
+
+### 1. Linked Service: The Connection
+
+A **Linked Service** is a connection string. It tells ADF how to connect to an external resource — a storage account, a database, an API.
+
+Think of it as the key to a door. Before ADF can read from your Blob Storage or write to your SQL database, it needs a Linked Service that holds the credentials and connection details for that resource.
+
+You create a Linked Service once, then reuse it across as many datasets and pipelines as you need.
+
+
+
+**Examples:**
+- `AzureStorageLinkedService` → connects to your ADLS Gen2 account
+- `AzureSqlLinkedService` → connects to your Azure SQL Database
+- `RestApiLinkedService` → connects to an external HTTP API
+
+### 2. Dataset: The Pointer
+
+A **Dataset** points to the specific data within a Linked Service.
+
+If the Linked Service is the key to the building, the Dataset is the directions to a specific room inside it. It tells ADF: *"The data I care about is in this container, in this folder, in this file format."*
+
+**Examples:**
+- A Dataset pointing to `bronze/sales/2024/jan/*.csv` in your ADLS Gen2 account
+- A Dataset pointing to the `[dbo].[orders]` table in your SQL database
+- A Dataset describing a Parquet file with a known schema
+
+### 3. Activity: The Work
+
+An **Activity** is a single step of work inside a pipeline. ADF has three categories:
+
+- **Data Movement** — Copy data from source to destination. The **Copy Activity** is the most common one you'll use.
+- **Data Transformation** — Transform data using Mapping Data Flows, Databricks notebooks, or stored procedures.
+- **Control Flow** — Logic and orchestration: If/Else conditions, ForEach loops, Wait activities, Execute Pipeline (call another pipeline).
+
+### 4. Pipeline — The Workflow
+
+A **Pipeline** is a logical grouping of activities that together perform a unit of work.
+
+Your pipeline might have three activities: a Copy Activity to land raw data, a Data Flow activity to clean it, and a Stored Procedure activity to update a watermark table. Together they form one repeatable workflow.
+
+
+
+## The ETL Flow in ADF: Visualised
+
+Here's how all four concepts connect in a real pipeline:
+
+
+
+
+
+
+
+## Build Your First Pipeline: Step by Step
+
+Let's build a real pipeline: copy a CSV file from Azure Blob Storage into ADLS Gen2, landing it in a `bronze/` folder.
+
+**What you need before starting:**
+- An Azure account (free trial works fine)
+- A Storage Account with hierarchical namespace enabled (ADLS Gen2)
+- A CSV file uploaded to a container called `source/`
+
+
+
+### Step 1: Create an Azure Data Factory
+
+1. Go to the [Azure Portal](https://portal.azure.com)
+2. Search for **Data Factory** → click **Create**
+3. Fill in the details:
+ - Resource Group: your existing one or create new
+ - Name: `sales-data-factory` (must be globally unique)
+ - Region: same as your storage account
+4. Click **Review + Create** → **Create**
+5. Once deployed, click **Launch Studio**
+
+You're now in **ADF Studio**, the visual authoring environment.
+
+
+
+
+
+### Step 2: Create a Linked Service for Your Storage Account
+
+1. In ADF Studio, click **Manage** (toolbox icon, left sidebar)
+2. Click **Linked Services** → **New**
+3. Search for **Azure Data Lake Storage Gen2** → Select → Continue
+4. Fill in:
+ - Name: `ADLSGen2LinkedService`
+ - Authentication: Account Key (simplest for now)
+ - Storage Account: select yours from the dropdown
+5. Click **Test Connection** — you should see ✅ Connection successful
+6. Click **Create**!
+
+
+
+
+
+
+### Step 3: Create the Source Dataset
+
+This dataset points to the CSV file in your `source/` container.
+
+1. Click **Author** (pencil icon, left sidebar)
+2. Click **+** → **Dataset**
+3. Search for **Azure Data Lake Storage Gen2** → Continue
+4. Select **Delimited Text** (CSV format) → Continue
+5. Fill in:
+ - Name: `SourceCSVDataset`
+ - Linked Service: `ADLSGen2LinkedService`
+ - File path: `source/` → browse and select your CSV file
+ - First row as header: ✅ checked
+6. Click **OK**
+
+
+
+
+
+### Step 4: Create the Sink Dataset
+
+This dataset points to where the file should land, your `bronze/` folder.
+
+1. Click **+** → **Dataset** again
+2. Same steps — **Azure Data Lake Storage Gen2** → **Delimited Text**
+3. Fill in:
+ - Name: `BronzeCSVDataset`
+ - Linked Service: `ADLSGen2LinkedService`
+ - File path: `bronze/sales/` (type this manually, it doesn't need to exist yet, ADF will create it)
+4. Click **OK**
+
+
+
+### Step 5: Build the Pipeline
+
+1. Click **+** → **Pipeline** → name it `CopySalesToBronze`
+2. From the **Activities** panel on the left, expand **Move & Transform**
+3. Drag **Copy data** onto the canvas
+4. Click the Copy Activity to open its settings:
+
+**Source tab:**
+- Source dataset: `SourceCSVDataset`
+
+**Sink tab:**
+- Sink dataset: `BronzeCSVDataset`
+- Copy behavior: `PreserveHierarchy`
+
+**Mapping tab:**
+- Click **Import schemas** - ADF reads your CSV headers and maps columns automatically
+
+5. Click **Validate** (toolbar) - you should see no errors
+6. Click **Debug** - this runs the pipeline immediately without publishing
+
+
+
+
+### Step 6: Publish and Add a Trigger
+
+Once Debug runs successfully:
+
+1. Click **Publish All** (top toolbar) - this saves everything to ADF
+2. Click **Add trigger** → **New/Edit**
+3. Click **New** → configure:
+ - Type: **Schedule**
+ - Start: today's date
+ - Recurrence: **Every 1 Day** at `02:00 AM`
+4. Click **OK** → **OK**
+5. Click **Publish All** again
+
+Your pipeline now runs automatically every night at 2am, copying new sales data into your bronze layer.
+
+
+
+### Step 7: Monitor Your Pipeline
+
+1. Click **Monitor** (chart icon, left sidebar)
+2. You'll see all pipeline runs - status, duration, rows copied
+3. Click any run to see activity-level details
+4. If something fails, click the error icon to see exactly which activity failed and why
+
+
+
+
+
+
+## What Just Happened: The Full Picture
+
+Let's step back and look at what you built:
+
+
+
+This is the **Extract and Load** part of ETL. The file is extracted from the source container and loaded into the bronze layer, untouched, exactly as it arrived.
+
+
+
+## What Comes Next: Transform
+
+The pipeline you built moves data. To transform it, you add one of two things after the Copy Activity:
+
+**Option 1 — Mapping Data Flow** (no-code)
+A visual transformation canvas inside ADF. Drag and drop Filter, Join, Aggregate, Derived Column activities. Runs on Spark under the hood. Great for teams that don't want to write code.
+
+**Option 2 — Databricks Notebook Activity**
+Call an existing Databricks notebook from your ADF pipeline. The notebook runs your Python/Spark transformation logic and writes cleaned data to the silver layer. Best for complex transformations that need code.
+
+The full Medallion Architecture flow in ADF looks like this:
+
+```
+Source API / Database
+ ↓
+Copy Activity → bronze/ (raw data, as-is)
+ ↓
+Mapping Data Flow / Databricks Notebook → silver/ (cleaned, validated)
+ ↓
+Mapping Data Flow / Databricks Notebook → gold/ (aggregated, business-ready)
+ ↓
+Power BI DirectLake → Dashboard
+```
+
+
+
+## Triggers: When Does Your Pipeline Run?
+
+ADF gives you three trigger types:
+
+| Trigger Type | When it fires | Use case |
+|---|---|---|
+| **Schedule** | At a fixed time/frequency | Nightly batch loads |
+| **Tumbling Window** | Fixed intervals with state | Hourly incremental loads |
+| **Storage Event** | When a file arrives in storage | File-arrival driven pipelines |
+| **Manual** | On demand | One-time loads, testing |
+
+For production pipelines, **Storage Event triggers** are the most powerful, your pipeline fires automatically the moment a new file lands in your container, with no polling or scheduling lag.
+
+
+
+## Common Mistakes Beginners Make
+
+**1. Using the same Linked Service for every environment**
+Create separate Linked Services for dev, staging, and production. Use ADF's **parameterisation** to swap them out without changing pipeline logic.
+
+**2. Not testing with Debug before publishing**
+Always Debug first. Publishing without testing means failures hit production. Debug runs don't count against your trigger history.
+
+**3. Hardcoding file paths in datasets**
+Parameterise your datasets so the same pipeline can process different files dynamically. One pipeline, many files, not one pipeline per file.
+
+**4. No monitoring alerts**
+Set up Azure Monitor alerts for pipeline failures. You shouldn't find out a pipeline failed when someone asks why last night's data is missing.
+
+
+
+## Key Takeaways
+
+**1. ADF is built on four concepts.** Linked Services (connections), Datasets (pointers), Activities (work), Pipelines (workflows). Everything else is a variation of these four.
+
+**2. The Copy Activity is your workhorse.** It supports 90+ source/sink combinations and handles schema mapping, file format conversion, and retry logic out of the box.
+
+**3. ADF is the orchestration layer, not the transformation layer.** For heavy transformations, ADF calls Databricks or Data Flows, it doesn't do the transformation itself.
+
+**4. Triggers make pipelines production-ready.** A pipeline without a trigger is just a script you run manually. Add a trigger and it becomes infrastructure.
+
+**5. ADF fits naturally into Medallion Architecture.** Copy Activity lands data in bronze. Data Flows or Databricks jobs process silver and gold. ADF orchestrates the whole sequence.
+
+
+## References & Further Reading
+
+- [Microsoft Docs: Introduction to Azure Data Factory](https://learn.microsoft.com/en-us/azure/data-factory/introduction)
+- [Microsoft Docs: Copy Activity in ADF](https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-overview)
+- [Microsoft Docs - ADF Tutorial: Copy data using Azure portal](https://learn.microsoft.com/en-us/azure/data-factory/tutorial-copy-data-portal)
+- [Microsoft Docs: Mapping Data Flows](https://learn.microsoft.com/en-us/azure/data-factory/concepts-data-flow-overview)
+- [Microsoft Docs: Triggers in ADF](https://learn.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers)
+- [RecodeHive - Azure Storage & ADLS Gen2: Where Does Your Data Actually Live?](https://www.recodehive.com/blog/azure-storage-options)
+- [RecodeHive - Microsoft Fabric: One Platform, One Lake](https://www.recodehive.com/blog/microsoft-fabric-explained)
+
+
+## About the Author
+
+I'm **Aditya Singh Rathore**, a Data Engineer passionate about building modern, scalable data platforms on Azure. I write about data engineering, cloud architecture, and real-world pipelines on [RecodeHive](https://www.recodehive.com/) breaking down complex concepts into things you can actually use.
+
+🔗 [LinkedIn](https://www.linkedin.com/in/aditya-singh-rathore0017/) | [GitHub](https://github.com/Adez017)
+
+📩 Stuck on a specific ADF activity or pipeline pattern? Drop your question in the comments.
+
+
diff --git a/src/database/blogs/index.tsx b/src/database/blogs/index.tsx
index de6b1049..a4829abb 100644
--- a/src/database/blogs/index.tsx
+++ b/src/database/blogs/index.tsx
@@ -144,6 +144,17 @@ const blogs: Blog[] = [
category: "data engineering",
tags: ["Azure", "Storage", "Data Lake", "ADLS Gen2", "Big Data", "Scalability", "Event Handling", "Technology", "Architecture", "Data Engineering"],
},
+ {
+ id: 13,
+ title: "Azure Data Factory Pipeline: Build Your First ETL in 10 Minutes",
+ image: "/img/blogs/adf-cover_image.png",
+ description:
+ "Azure Data Factory Pipeline is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation tasks. This article will guide you through the process of building your first ETL pipeline in Azure Data Factory.",
+ slug: "ETL-pipeline-tutorial",
+ authors: ["Aditya-Singh-Rathore"],
+ category: "data engineering",
+ tags: ["Azure", "Storage", "Data Lake", "ADLS Gen2", "Big Data", "Scalability", "Event Handling", "Technology", "Architecture", "Data Engineering"],
+ },
{
id: 14,
title: "Medallion Architecture: How to Stop Your Data Pipeline from Becoming a Nightmare",
diff --git a/static/img/blogs/adf-cover_image.png b/static/img/blogs/adf-cover_image.png
new file mode 100644
index 00000000..55c5e73c
Binary files /dev/null and b/static/img/blogs/adf-cover_image.png differ
.default})
+
+
+## Build Your First Pipeline: Step by Step
+
+Let's build a real pipeline: copy a CSV file from Azure Blob Storage into ADLS Gen2, landing it in a `bronze/` folder.
+
+**What you need before starting:**
+- An Azure account (free trial works fine)
+- A Storage Account with hierarchical namespace enabled (ADLS Gen2)
+- A CSV file uploaded to a container called `source/`
+
+
+
+### Step 1: Create an Azure Data Factory
+
+1. Go to the [Azure Portal](https://portal.azure.com)
+2. Search for **Data Factory** → click **Create**
+3. Fill in the details:
+ - Resource Group: your existing one or create new
+ - Name: `sales-data-factory` (must be globally unique)
+ - Region: same as your storage account
+4. Click **Review + Create** → **Create**
+5. Once deployed, click **Launch Studio**
+
+You're now in **ADF Studio**, the visual authoring environment.
+
+
+
+
+
+### Step 2: Create a Linked Service for Your Storage Account
+
+1. In ADF Studio, click **Manage** (toolbox icon, left sidebar)
+2. Click **Linked Services** → **New**
+3. Search for **Azure Data Lake Storage Gen2** → Select → Continue
+4. Fill in:
+ - Name: `ADLSGen2LinkedService`
+ - Authentication: Account Key (simplest for now)
+ - Storage Account: select yours from the dropdown
+5. Click **Test Connection** — you should see ✅ Connection successful
+6. Click **Create**!
+
+
+
+
+
+
+### Step 3: Create the Source Dataset
+
+This dataset points to the CSV file in your `source/` container.
+
+1. Click **Author** (pencil icon, left sidebar)
+2. Click **+** → **Dataset**
+3. Search for **Azure Data Lake Storage Gen2** → Continue
+4. Select **Delimited Text** (CSV format) → Continue
+5. Fill in:
+ - Name: `SourceCSVDataset`
+ - Linked Service: `ADLSGen2LinkedService`
+ - File path: `source/` → browse and select your CSV file
+ - First row as header: ✅ checked
+6. Click **OK**
+
+
+
+
+
+### Step 4: Create the Sink Dataset
+
+This dataset points to where the file should land, your `bronze/` folder.
+
+1. Click **+** → **Dataset** again
+2. Same steps — **Azure Data Lake Storage Gen2** → **Delimited Text**
+3. Fill in:
+ - Name: `BronzeCSVDataset`
+ - Linked Service: `ADLSGen2LinkedService`
+ - File path: `bronze/sales/` (type this manually, it doesn't need to exist yet, ADF will create it)
+4. Click **OK**
+
+
+
+### Step 5: Build the Pipeline
+
+1. Click **+** → **Pipeline** → name it `CopySalesToBronze`
+2. From the **Activities** panel on the left, expand **Move & Transform**
+3. Drag **Copy data** onto the canvas
+4. Click the Copy Activity to open its settings:
+
+**Source tab:**
+- Source dataset: `SourceCSVDataset`
+
+**Sink tab:**
+- Sink dataset: `BronzeCSVDataset`
+- Copy behavior: `PreserveHierarchy`
+
+**Mapping tab:**
+- Click **Import schemas** - ADF reads your CSV headers and maps columns automatically
+
+5. Click **Validate** (toolbar) - you should see no errors
+6. Click **Debug** - this runs the pipeline immediately without publishing
+
+
+
+
+### Step 6: Publish and Add a Trigger
+
+Once Debug runs successfully:
+
+1. Click **Publish All** (top toolbar) - this saves everything to ADF
+2. Click **Add trigger** → **New/Edit**
+3. Click **New** → configure:
+ - Type: **Schedule**
+ - Start: today's date
+ - Recurrence: **Every 1 Day** at `02:00 AM`
+4. Click **OK** → **OK**
+5. Click **Publish All** again
+
+Your pipeline now runs automatically every night at 2am, copying new sales data into your bronze layer.
+
+
+
+### Step 7: Monitor Your Pipeline
+
+1. Click **Monitor** (chart icon, left sidebar)
+2. You'll see all pipeline runs - status, duration, rows copied
+3. Click any run to see activity-level details
+4. If something fails, click the error icon to see exactly which activity failed and why
+
+
+
+
+
+
+## What Just Happened: The Full Picture
+
+Let's step back and look at what you built:
+
+.default})
+
+This is the **Extract and Load** part of ETL. The file is extracted from the source container and loaded into the bronze layer, untouched, exactly as it arrived.
+
+
+
+## What Comes Next: Transform
+
+The pipeline you built moves data. To transform it, you add one of two things after the Copy Activity:
+
+**Option 1 — Mapping Data Flow** (no-code)
+A visual transformation canvas inside ADF. Drag and drop Filter, Join, Aggregate, Derived Column activities. Runs on Spark under the hood. Great for teams that don't want to write code.
+
+**Option 2 — Databricks Notebook Activity**
+Call an existing Databricks notebook from your ADF pipeline. The notebook runs your Python/Spark transformation logic and writes cleaned data to the silver layer. Best for complex transformations that need code.
+
+The full Medallion Architecture flow in ADF looks like this:
+
+```
+Source API / Database
+ ↓
+Copy Activity → bronze/ (raw data, as-is)
+ ↓
+Mapping Data Flow / Databricks Notebook → silver/ (cleaned, validated)
+ ↓
+Mapping Data Flow / Databricks Notebook → gold/ (aggregated, business-ready)
+ ↓
+Power BI DirectLake → Dashboard
+```
+
+
+
+## Triggers: When Does Your Pipeline Run?
+
+ADF gives you three trigger types:
+
+| Trigger Type | When it fires | Use case |
+|---|---|---|
+| **Schedule** | At a fixed time/frequency | Nightly batch loads |
+| **Tumbling Window** | Fixed intervals with state | Hourly incremental loads |
+| **Storage Event** | When a file arrives in storage | File-arrival driven pipelines |
+| **Manual** | On demand | One-time loads, testing |
+
+For production pipelines, **Storage Event triggers** are the most powerful, your pipeline fires automatically the moment a new file lands in your container, with no polling or scheduling lag.
+
+
+
+## Common Mistakes Beginners Make
+
+**1. Using the same Linked Service for every environment**
+Create separate Linked Services for dev, staging, and production. Use ADF's **parameterisation** to swap them out without changing pipeline logic.
+
+**2. Not testing with Debug before publishing**
+Always Debug first. Publishing without testing means failures hit production. Debug runs don't count against your trigger history.
+
+**3. Hardcoding file paths in datasets**
+Parameterise your datasets so the same pipeline can process different files dynamically. One pipeline, many files, not one pipeline per file.
+
+**4. No monitoring alerts**
+Set up Azure Monitor alerts for pipeline failures. You shouldn't find out a pipeline failed when someone asks why last night's data is missing.
+
+
+
+## Key Takeaways
+
+**1. ADF is built on four concepts.** Linked Services (connections), Datasets (pointers), Activities (work), Pipelines (workflows). Everything else is a variation of these four.
+
+**2. The Copy Activity is your workhorse.** It supports 90+ source/sink combinations and handles schema mapping, file format conversion, and retry logic out of the box.
+
+**3. ADF is the orchestration layer, not the transformation layer.** For heavy transformations, ADF calls Databricks or Data Flows, it doesn't do the transformation itself.
+
+**4. Triggers make pipelines production-ready.** A pipeline without a trigger is just a script you run manually. Add a trigger and it becomes infrastructure.
+
+**5. ADF fits naturally into Medallion Architecture.** Copy Activity lands data in bronze. Data Flows or Databricks jobs process silver and gold. ADF orchestrates the whole sequence.
+
+
+## References & Further Reading
+
+- [Microsoft Docs: Introduction to Azure Data Factory](https://learn.microsoft.com/en-us/azure/data-factory/introduction)
+- [Microsoft Docs: Copy Activity in ADF](https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-overview)
+- [Microsoft Docs - ADF Tutorial: Copy data using Azure portal](https://learn.microsoft.com/en-us/azure/data-factory/tutorial-copy-data-portal)
+- [Microsoft Docs: Mapping Data Flows](https://learn.microsoft.com/en-us/azure/data-factory/concepts-data-flow-overview)
+- [Microsoft Docs: Triggers in ADF](https://learn.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers)
+- [RecodeHive - Azure Storage & ADLS Gen2: Where Does Your Data Actually Live?](https://www.recodehive.com/blog/azure-storage-options)
+- [RecodeHive - Microsoft Fabric: One Platform, One Lake](https://www.recodehive.com/blog/microsoft-fabric-explained)
+
+
+## About the Author
+
+I'm **Aditya Singh Rathore**, a Data Engineer passionate about building modern, scalable data platforms on Azure. I write about data engineering, cloud architecture, and real-world pipelines on [RecodeHive](https://www.recodehive.com/) breaking down complex concepts into things you can actually use.
+
+🔗 [LinkedIn](https://www.linkedin.com/in/aditya-singh-rathore0017/) | [GitHub](https://github.com/Adez017)
+
+📩 Stuck on a specific ADF activity or pipeline pattern? Drop your question in the comments.
+
+