Vinod Kumar’s Post

🚀 How to Create an ADF Pipeline (Beginner’s Guide) Building pipelines in Azure Data Factory (ADF) is easier than you might think — even if you’re just starting! Here’s a quick step-by-step overview: 1️⃣ Open ADF Studio → Select “Create Pipeline” 2️⃣ Define your source and destination data stores 3️⃣ Drag and drop activities like Copy Data, Databricks Notebook, or Stored Procedure 4️⃣ Chain activities to create sequential or parallel workflows 5️⃣ Use parameters to make your pipelines reusable and dynamic 6️⃣ Add triggers to schedule runs or launch on specific events 7️⃣ Debug your pipeline before publishing to production 8️⃣ Monitor executions via ADF dashboards for success/failure alerts 9️⃣ Configure linked services for secure connections to data sources ✨ With ADF, you can build modular, automated, and scalable pipelines that minimize manual work and improve reliability. Error handling and retry policies make them even more resilient. 🔹 Once you master pipeline creation, you’ve learned one of the core skills of a modern Cloud Data Engineer! #AzureDataFactory #DataEngineering #MicrosoftAzure #ADF #CloudData #ETL #DataPipelines

💡 Today’s ADF Learning – Dynamic CSV Processing! 🚀\ Today, I explored how to make my Azure Data Factory pipelines dynamic using Get Metadata, ForEach, and IF Condition activities. 📂 Get Metadata – Fetched all files from a folder dynamically, no hardcoding needed. 🔄 ForEach – Looped through each file; @item().name gave the current file name in each iteration. ✅ IF Condition – Checked if the file is a CSV using @endswith(item().name, '.csv'), processing only relevant files. 💾 Copy Data – Processed the CSV files using the dynamic file name directly, keeping the pipeline clean and efficient. ✨ Scenario implemented: Folder has multiple file types; only CSV files are copied. Works dynamically for any new files added. #AzureDataFactory #ADF #DataEngineering #ETL #DataPipelines #CloudData #TechLearning #DynamicPipelines #DataAutomation #BigData

⭕ 𝗬𝗼𝘂 𝗺𝗮𝘆 𝗴𝗲𝘁 𝗮𝗻 𝘂𝗻𝗳𝗮𝗶𝗿 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗶𝗻 𝗔𝗭𝗨𝗥𝗘 𝗗𝗔𝗧𝗔 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘁𝗵𝗶𝘀 𝗣𝗟𝗔𝗬𝗟𝗜𝗦𝗧 [𝟭𝟬 𝗛𝗢𝗨𝗥𝗦] ⭕ This is a 10+ Hour Complete Azure Data Factory Bootcamp..... - A single playlist that takes you from beginner to pro - step by step. Here’s what you’ll learn ✅ Azure Data Factory Fundamentals - What is ADF & how it fits in the Azure ecosystem - Create Free Azure Account & ADF Workspace - Dataset & Linked Service setup - Data Ingestion into Data Lake - Copy Activity & REST API Integration - Get Metadata, IF Condition, For Each - Expression Builder & Parameterized Pipelines -Data Flow & Transformations - Triggers (Schedule & Event-based) - Debugging & Real-time Scenarios - Execute Pipeline Activity - End-to-End Data Pipeline using ADF ✅ Real-Time Scenarios - CI/CD Using ADF - Incremental Loading (Modern Architecture) - Dynamic Ingestion - Spark Data Flows ✅ End-to-End Project - Complete On-Prem to Azure Migration with CI/CD 📍 Find the complete video link in the COMMENTS ♻️ HAPPY LEARNING ♻️ #azure #azuredatafactory #azuredataengineer

one of the important part as a data engineer on which one's has to focus and Hand-ons is data factory. ETL/ELT Pipelines + Dynamic Expressions make your work easy.

𝐌𝐨𝐬𝐭 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐟𝐚𝐢𝐥 𝐭𝐡𝐞𝐬𝐞 𝐀𝐳𝐮𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 — 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐡𝐞𝐦? 1️⃣ How does Azure Data Factory handle incremental loads, and what are the different approaches to implement watermarking? (Hint: Think about LastModifiedDate and dynamic parameters!) 2️⃣ What’s the real difference between Dataflow and Databricks notebooks in ADF — and when would you not use one over the other? 3️⃣ Why do many pipelines break when switching from Dev to Prod in Azure Data Factory, and how can Linked Services and Integration Runtimes prevent that? 4️⃣ What are the performance tuning techniques for Delta Lake in Azure Databricks, and how do ZORDER and OPTIMIZE commands differ in impact? 5️⃣ How do you design a data lake architecture in Azure to avoid the biggest mistake — creating a data swamp? (Hint: Think about naming conventions, folder hierarchy, and governance.) #DataEngineer #SQL #CareerGrowth #Azure

🚀 Metadata-Driven Ingestion Framework in Azure Data Factory (ADF) Over the past few days, I’ve been working on building a fully dynamic ingestion framework using Azure Data Factory, Azure SQL, and ADLS — designed to scale seamlessly across multiple data sources and targets without touching a single pipeline parameter. 💡 🔧 Key Highlights: Dynamic ingestion using a metadata control table (ingestion_config) Automated data flow from Azure SQL → ADLS (or Delta) Single reusable pipelines: pl_MASTER → Orchestrator pl_COPY_GENERIC → Dynamic worker Parameterized datasets for schema, table, format, delimiter, header, and path Type-safe expression handling with bit → boolean conversion Error-free JSON parsing using @string(item()) and @json(variables('vConfig')) 🧠 What I Learned: How to make pipelines metadata-driven and fully reusable How to handle dynamic JSON parameters and ADF’s quirks with string vs object types How to simplify onboarding of new datasets by just inserting a row in SQL 📈 Result: 💥 Reduced ingestion onboarding time from hours to minutes. 💥 Same pipeline now handles multiple source-target combinations automatically. 🔗 I’ve documented the entire process (including SQL schema, expressions, dataset mappings, and copy logic) in this PDF 👇

===> AWS Glue DynamicFrame Vs Spark DataFrame<=== Understanding the difference between AWS Glue DynamicFrame and Spark DataFrame can make or break your ETL workflow efficiency. ==>DynamicFrame — designed for semi-structured data, schema evolution, and AWS-native transformations. Ideal for ingesting JSON, CSV, or logs from S3 where schema may evolve over time. ==>DataFrame — perfect for advanced Spark transformations, performance tuning, and custom logic once your schema is stable. You can convert between DynamicFrame ↔ DataFrame when needed for complex Spark operations. ==>A real-world combo: Use DynamicFrame to read raw data → clean and validate → convert to DataFrame for heavy transformations → write back as optimized Parquet. ==> Make quick decision =>Use DynamicFrame for flexibility and resilience against schema drift (JSON, logs, evolving CSVs). =>Use DataFrame for performance-critical, stable-schema, or SQL-based analytics tasks. =>Combine both when needed — AWS Glue makes it seamless. #AWS #Glue #ETL #DataEngineering #BigData #Spark #DynamicFrame #DataFrame #learnwithmmi

🚀 Understanding “Import Schema” in Azure Data Factory — and the Best Practice You Should Follow If you’ve ever created a dataset in Azure Data Factory (ADF), you’ve probably noticed a small dropdown called “Import schema” — and wondered what the right choice is. 🤔 Let’s make it simple 👇 🔹 What does Import Schema mean? In ADF, schema simply means the structure of your data — the list of columns and their data types (like PatientID INT, Name STRING, Age INT). When you create a dataset, ADF gives you three options for Import schema: ➡️ Option: From connection/store Meaning: ADF reads schema directly from the table in your database When to Use: Use for SQL, Synapse, or Snowflake tables ➡️ Option: From sample file Meaning: ADF reads schema from a sample file (CSV, JSON, etc.) When to Use: Use for Blob/ADLS files when a sample is available ➡️ Option: None Meaning: No schema imported (schema-less) When to Use: Use for dynamic or parameterized pipelines 💡 Best Practice ✅ For stable sources or targets (SQL, Snowflake, Synapse) → Use From connection/store — it ensures ADF always has the latest schema structure. ✅ For file-based datasets (CSV, Parquet, JSON) → Use From sample file — helps ADF understand column names and data types for mapping. ✅ For dynamic pipelines (schema may vary) → Use None — handle schema drift dynamically in your Copy Activity mapping. 🧠 Pro Tip If you’re building generic pipelines that handle multiple tables or file types — avoid hardcoding schema. Instead, use “Import schema = None” and rely on Auto Mapping or dynamic schema drift. It makes your pipeline reusable and future-proof. 🔄 📌 In short: “Import Schema” helps ADF understand what your data looks like — choose wisely based on whether your pipeline is static or dynamic. #AzureDataFactory #DataEngineering #ETL #Azure #ADF #DataPipeline #CloudData #DataIntegration #BestPractices

🚀 How to Create an Azure Data Factory (ADF) Pipeline Building pipelines in Azure Data Factory is simpler than it seems — even if you’re just getting started. Here’s how you can create one step-by-step 👇 1️⃣ Open ADF Studio → Click “Create Pipeline” 2️⃣ Define source & destination data stores 3️⃣ Drag and drop activities like Copy Data, Databricks Notebook, or Stored Procedure 4️⃣ Chain activities to build sequential or parallel workflows 5️⃣ Use parameters to make your pipelines dynamic and reusable 6️⃣ Add triggers to automate runs based on schedules or events 7️⃣ Debug & publish once tested successfully 8️⃣ Monitor execution through the ADF dashboard for alerts and performance insights ✨ Why it matters: --> Modular, automated, and scalable workflows --> Built-in error handling and retry policies --> Reusability reduces manual work and speeds up onboarding new data sources Learning to design efficient ADF pipelines is a core skill for every cloud data engineer. Once you master it, ADF becomes the backbone of your data workflow automation. #Azure #DataFactory #AzureDataFactory #DataEngineering #CloudComputing #ETL #DataPipelines #MicrosoftAzure

🚀 Add a file, and it’s in Snowflake within seconds! I set up a basic Snowpipe with auto-ingest and transformations to load people data from Azure Blob Storage. 🎬 Demo highlights: Loaded a CSV with 100 records → table populated ✅ Added another CSV with 1,000 records → table updated seamlessly to 1,100 records ✅ Loaded another CSV with 10,000 records → all records ingested in seconds ✅ 💡 Key takeaways: Snowpipe handles incremental loads reliably Transformations like TRY_TO_DATE and computed columns can be applied on the fly 💻 I’ll be updating the GitHub link soon with the transformations I used and DDL scripts for creating the storage integration objects. Tools used: SQL, Snowflake trial version and Azure Data Factory trial version #DataEngineering #Snowflake #Snowpipe #ETL #Azure #SQL