Building Data Pipelines with AWS Glue - Cloud Data Assembly Line

Bread-and-Butter AWS Engineering

Typical AWS Glue Data Pipeline

A common modern AWS data engineering workflow looks like this:

🏛️ Oracle/MS SQL/Teradata Database Extract
          ↓
📄 Raw CSV lands in Amazon S3
          ↓
⚙️ AWS Glue (PySpark) Cleanse / Transform
          ↓
🪶 Write Partitioned Parquet Files
          ↓
📚 AWS Glue Catalog Update
          ↓
🔎 Amazon Athena / 🟥 Amazon Redshift Query

📦 Amazon S3

S3 stands for Simple Storage Service.

A giant object warehouse, where files of all kinds are stored:

• CSV files
• Parquet datasets
• Images
• Logs
• Backups
• Machine learning artifacts

Example object:

s3://voltedge-data/curated/member/2026/01/09/part-0001.parquet

Breaking it apart:

🎭 The Truth About “Folders” in S3

In S3, folders are mostly an illusion.

There are no real nested directories like:

/curated/member/2026/01/09/

Under the hood, S3 stores everything in a flat namespace of keys.

For example:

curated/member/2026/01/09/part-0001.parquet

is simply one long string key, not a file inside real folders.

AWS Console makes it look like folders exist because humans like hierarchy:

📁 curated
   └── 📁 member
       └── 📁 2026
           └── 📁 01
               └── 📁 09
                   └── 📄 part-0001.parquet

And we engineers happily work with that illusion every day.

⚙️ The Pipeline Separates Concerns Cleanly

• Oracle → source system 🏛️
• S3 → landing / storage zone 📦
• Glue + PySpark → transformation engine ⚙️
• Parquet → analytics-friendly format 🪶
• Glue Catalog → metadata layer 📚
• Athena / Redshift → query & reporting 🔎

Result:

Raw operational data → clean analytical dataset → business insights

🧠 The classic cloud data assembly line

Extract → Land → Clean → Compress → Catalog → Query