Perfect. DAY 8 is where you officially step into real AWS Data Engineering.
Today we master metadata, which is the backbone of every scalable data platform.

📅 DAY 8 — AWS Glue Data Catalog (MASTER LEVEL)

Hive Metastore → Serverless, Shared, Governed Metadata

AWS Glue

🎯 DAY 8 GOAL

By the end of today, you will:

• Understand what metadata really is (and why it matters)
• Master Glue Databases, Tables, Partitions
• Know how Glue replaces Hive Metastore
• Design multi-team, production-grade data catalogs
• Avoid the most common Glue interview & design mistakes

🧠 PART 1 — WHAT METADATA ACTUALLY MEANS (FIRST PRINCIPLES)

❌ Wrong understanding

Metadata = schema only

✅ Correct understanding

Metadata = everything needed to understand and query data

Metadata answers:

• What is the table called?
• Where is the data stored?
• What are column names & types?
• How is data partitioned?
• Who can access it?

📌 Data lives in S3. Glue only describes it.

🧩 GLUE CATALOG ARCHITECTURE (MENTAL MODEL)

S3 (Parquet / CSV / JSON)
   ↓
Glue Crawler / DDL
   ↓
Glue Data Catalog
   ↓
Spark (EMR / Glue)
Athena
Redshift Spectrum

🧠 One catalog → many engines

🧠 PART 2 — GLUE CATALOG CORE OBJECTS

1️⃣ Glue Database

Logical namespace (like Hive DB)

Example:

analytics_db
raw_db
finance_db

📌 No data stored here — just organization

2️⃣ Glue Table

Maps to S3 location + schema

A table contains:

• Table name
• S3 path
• Columns & data types
• Partition keys
• SerDe info

📌 Exactly like Hive external tables

3️⃣ Partitions (VERY IMPORTANT)

Partitions are metadata pointers, not folders.

Example:

country=IN/date=2025-01-01

🧠 Query engines prune partitions → lower cost & faster queries

🧠 PART 3 — HOW TABLES ARE CREATED (2 WAYS)

🔹 Option 1: Glue Crawler (MOST COMMON)

Crawler:

• Scans S3
• Infers schema
• Creates/updates tables

Used when:

• Data arrives continuously
• Schema may evolve

🔹 Option 2: DDL (ADVANCED / CONTROLLED)

CREATE EXTERNAL TABLE sales (
  order_id int,
  amount double,
  country string
)
PARTITIONED BY (date string)
LOCATION 's3://company-data/curated/sales/';

Used when:

• Schema is fixed
• Strict governance required

📌 Senior teams prefer DDL for curated layers

🧠 PART 4 — SCHEMA EVOLUTION (REAL-LIFE PROBLEM)

What happens when schema changes?

🧠 Production strategy:

• Raw: flexible
• Curated: strict

🧠 PART 5 — GLUE CATALOG VS DATABASES (NO CONFUSION)

📌 Glue is metadata only

🧠 PART 6 — REAL-WORLD CATALOG DESIGN (VERY IMPORTANT)

🏗️ Enterprise Pattern

raw_db        → source-owned
cleansed_db   → engineering-owned
curated_db    → analytics-owned

Benefits:

• Ownership clarity
• Access control
• Fewer breaking changes

🧠 PART 7 — ACCESS CONTROL (HIGH-LEVEL)

Glue permissions are enforced via:

• IAM policies
• Lake Formation (advanced, later)

Example:

• Analysts → read curated
• Engineers → read/write cleansed
• No one touches raw

📌 Data governance starts at the catalog

🧠 PART 8 — GLUE CATALOG IN ACTION (REAL FLOWS)

🔹 Spark on EMR

spark.table("curated_db.sales")

🔹 Athena

SELECT * FROM curated_db.sales;

🔹 Glue Job

Uses same catalog automatically

🧠 One schema, many engines

🎤 INTERVIEW STATEMENTS (MASTER LEVEL)

✔ “Glue Data Catalog is a centralized, serverless Hive Metastore.”
✔ “Glue tables are pointers to S3 locations, not data containers.”
✔ “We manage schema evolution carefully between raw and curated layers.”
✔ “Partitions are metadata constructs that improve query performance.”

🧪 DAY 8 THINKING EXERCISE

Answer mentally:

1. Why is Glue Catalog shared across Athena and Spark?
2. Why do senior teams avoid crawlers on curated data?
3. What happens if S3 data changes but Glue metadata doesn’t?

🧠 DAY 8 MEMORY MAP (SAVE THIS)

S3 = Data
Glue = Metadata

Database  → Namespace
Table     → Schema + S3 path
Partition → Metadata pointer

One catalog → Many engines

⏭️ DAY 9 PREVIEW — ATHENA (SERVERLESS ANALYTICS)

Tomorrow we go deep into:

• Athena internals
• Cost model (VERY IMPORTANT)
• Partition pruning
• When Athena beats Spark
• Real analyst workflows

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DAY 9

You’re now firmly in core AWS data engineering territory 🔥