Simple Data Pipelines Without the Big Data Theater

🎯 Start Simple

Most data pipelines don't need big data tools. Start with simple tools, add complexity only when you need it.

✅ Simple Pipeline Stack

1. Extract: Direct Queries

Query your source database directly. No need for complex ETL tools unless you have multiple sources or complex transformations.

2. Transform: Python Scripts

Use Python with pandas for transformations. It's simple, readable, and handles most data work. No need for Spark unless you're processing terabytes.

3. Load: Direct Inserts

Insert into your destination database. Use batch inserts for efficiency, but keep it simple.

🛠️ My Typical Pipeline

# Extract
source_data = query_source_db("SELECT * FROM table WHERE date > ?", yesterday)

# Transform
cleaned_data = clean_and_transform(source_data)

# Load
insert_into_destination(cleaned_data)

That's it. No Kafka, no Spark, no Hadoop. Just simple code that works.

⚠️ When You Need Big Tools

You might need big data tools if:

• You're processing terabytes of data
• You need real-time processing (sub-second latency)
• You have complex event streaming requirements
• You're doing machine learning at scale

But most pipelines don't need this. Start simple.

💡 Scaling Up

When you outgrow simple scripts:

1. Add scheduling (cron or a simple scheduler)
2. Add error handling and retries
3. Add monitoring and alerts
4. Consider a simple workflow tool (like Airflow) if you have many pipelines

But don't jump to Kafka or Spark unless you actually need them.

💭 My Take

Big data tools are for big data. Most data work isn't big data—it's just data.

Start with simple tools. Add complexity only when you hit real limits, not theoretical ones.