Process 10+ Million Records Daily Using Azure Databricks

🚀 How We Process 10+ Million Records Daily Using Azure Databricks

Many people ask:

“How do you handle 10 million+ records every day without performance issues?”

Here’s the approach we follow in our Data Engineering project.

📥 Data Sources
• PostgreSQL
• CSV Files
• JSON Files
• Real-time Events (Kafka)

Instead of loading everything at once, we use incremental processing.

Bronze Layer

✔ Ingest raw data using Auto Loader
✔ Store data in Delta Lake
✔ Maintain schema evolution
✔ Capture audit columns

Silver Layer

✔ Data validation
✔ Remove duplicate records
✔ Handle null values
✔ Standardize formats
✔ Apply business rules

Example:

df = df.dropDuplicates([“order_id”])

Gold Layer

✔ Create fact and dimension tables
✔ Build business KPIs
✔ Daily Revenue
✔ Customer Analytics
✔ Product Performance

Performance Optimizations

🔹 Partitioning on transaction_date

🔹 Z-ORDER on frequently filtered columns

OPTIMIZE sales
ZORDER BY (customer_id, product_id);

🔹 Adaptive Query Execution (AQE)

🔹 Broadcast joins for small lookup tables

🔹 Delta Lake optimization

🔹 Caching reusable datasets

Results

📈 Processed 10+ Million records daily

⚡ Reduced pipeline runtime from 2 hours to 45 minutes

💰 Lower compute cost

📊 Faster dashboard refreshes

🎯 Key Learning

Processing large volumes of data is not about having a bigger cluster.

It’s about:
✔ Efficient architecture
✔ Incremental processing
✔ Data quality checks
✔ Spark optimization
✔ Proper data modeling

A well-designed pipeline can process millions of records efficiently while keeping costs under control.

How many records does your biggest Spark pipeline process daily?

👇 Share your experience in the comments.

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