Data Ingestion with dlt – Week 3 Bonus

This content originally appeared on DEV Community and was authored by Blessing Angus

🧙‍♂️ Data Doesn’t Just Appear—Engineers Make It Happen!

Have you ever opened a dataset and thought, “Wow, this is so clean and structured”? Well, someone worked really hard to make it that way! Welcome to data ingestion—the first step in any powerful data pipeline.

Why Data Pipelines Matter

A data pipeline is more than just moving data from point A to point B. It ensures that raw, unstructured data becomes something usable, reliable, and insightful.

Here’s what happens under the hood:

1️⃣ Extract: Fetch data from APIs, databases, and files
2️⃣ Normalize: Clean and structure messy, inconsistent formats
3️⃣ Load: Store it in data warehouses/lakes for analysis
4️⃣ Optimize: Use incremental loading to refresh data efficiently ⚡

Becoming the Data Magician 🧙‍♂️

During our dlt workshop, we explored how to build scalable, self-maintaining pipelines that handle:

• Real-time and batch ingestion

• Automated schema detection and normalization

• Governance and best practices for high-quality data

🚀 Key takeaway? If you want to work in data, mastering ingestion pipelines is a game-changer! Whether you’re dealing with messy JSON, SQL databases, or REST APIs, a strong pipeline ensures that data is always ready when you need it.

💬 What are your favorite tricks for handling messy data? Drop them in the comments! 👇

DataEngineering #DLT #ETL #BigData #Python #DataPipelines

This content originally appeared on DEV Community and was authored by Blessing Angus

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