Microsoft Fabric Lakehouse စတင် တည်ဆောက်ခြင်း

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Create Your First Lakehouse တည်ဆောက်ခြင်း

Load CSV, Parquet, JSON Files များတင်ခြင်း

Microsoft Fabric Tutorial


📘 Microsoft Fabric မှာ Lakehouse ဆိုတာဘာလဲ?

Lakehouse ဆိုတာ Microsoft Fabric မှာ scalability and flexibility ဒီ ၂ခုကို ပေါင်းစပ်ထားတဲ့ data lake ကိုမှ structured querying power ပါဝင်တဲ့ data warehouse တစ်ခုဖြစ်တယ်။ မတူညီတဲ့ ဖိုင်တွေ အများကြီးကို တနေရာတည်းမှာ သိမ်းထားလို့ရတယ် လိုအပ်တဲ့ စမ်းစစ်ချက်တွေကို တိုက်ရိုက် raw data ကော structured data ပေါ်မှာ SQL, notebooks, or Power BI စသည်တို့နဲ့ analytics လုပ်လို့ရအောင် ပြုလုပ်ပေးထားတယ်.

✅ ဘာတွေ သင်ယူရမှာလဲ? (What You'll Learn in This Tutorial ?)

  • What a Lakehouse is and its role in Microsoft Fabric
  • Step-by-step process to create a new Lakehouse
  • How to upload and manage CSV, Parquet, and JSON files
  • How Microsoft Fabric unifies data lake and data warehouse capabilities
  • Practical tips to structure your Lakehouse for analytics workloads

🛠️ Step-by-Step: Creating Your First Lakehouse

  1. Log in to Microsoft Fabric Portal
  2. Go to your workspace and click + New → Lakehouse
  3. Give your Lakehouse a name and hit Create

Once created, you'll land in the Lakehouse explorer which allows you to manage files, tables, and notebooks.

📂 Upload CSV, Parquet, and JSON Files

Inside your Lakehouse, switch to the Files tab:

  • Click on Upload and select one or more files (CSV, Parquet, or JSON)
  • Uploaded files are stored in /Files folder
  • You can preview and open these files in notebooks or convert them into managed Delta tables

📊 Unifying Data Lake and Warehouse

Microsoft Fabric allows you to treat your Lakehouse like a warehouse using DirectLake and SQL endpoints:

  • Run SQL queries on files/tables using SQL analytics endpoint
  • Use Power BI for visualizations without importing data
  • Query Delta tables using Spark notebooks

💡 Tips to Structure Your Lakehouse

  • Use folders like /raw, /processed, and /curated to stage data
  • Convert CSV and JSON into Delta tables for analytics
  • Tag or name files consistently: e.g., sales_2025_Q2.csv

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