Data Lake Architecture: Level 3 Maturity

In Level 3 of the [Data Maturity Lifecycle](DataMaturityLifecycle), organizations decouple storage from compute. By moving data into a **Data Lake** (S3, GCS, Azure Blob), they achieve infinite scalability and the ability to store raw, unstructured data.

1. The "Data Swamp" Failure Mode

Without a structural framework, a Data Lake quickly becomes a "Data Swamp"—a collection of unidentifiable files with no schema, no ownership, and no quality guarantees. Level 3 maturity is defined by the implementation of the **Medallion Architecture**.

2. The Medallion Architecture (Bronze/Silver/Gold)

Bronze (Raw)

- **State:** Ingestion fidelity.

- **Goal:** Capture source data as-is (JSON, Avro, Parquet).

- **Structure:** Partitioned by ingestion date (e.g., `s3://bucket/bronze/orders/year=2026/month=05/`).

Silver (Cleansed)

- **State:** Conformed and validated.

- **Goal:** Apply schema enforcement, filter nulls, and deduplicate.

- **Structure:** Usually stored as Parquet with defined types.

Gold (Curated)

- **State:** Business-ready aggregations.

- **Goal:** High-performance tables for BI/ML.

- **Structure:** Modeled for specific use cases (e.g., `s3://bucket/gold/monthly_revenue/`).

3. Concrete Example: Pipeline Implementation

Using Apache Spark to move data from Bronze to Silver:

```python

Spark logic for Bronze to Silver transition

df_raw = spark.read.json("s3://bronze/orders/2026/05/*")

Cleaning: Cast types and filter invalid orders

df_cleansed = df_raw.select(

col("order_id").cast("string"),

col("amount").cast("double"),

to_timestamp(col("ts")).alias("event_time")

).filter(col("amount") > 0).dropDuplicates(["order_id"])

Write to Silver as Parquet

df_cleansed.write.partitionBy("event_date") \

.mode("overwrite") \

.parquet("s3://silver/orders/")

```

4. The Transition to Level 4

Level 3 lakes are still "append-only" and lack ACID transactions. Updating a single row requires rewriting an entire partition. To solve this, organizations move to Level 4, the [Data Lakehouse](DataLakehouse).

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**See Also:**

- [Data Warehouse Design](DataWarehouseDesign) — The predecessor to lakes.

- [Apache Spark Fundamentals](ApacheSparkFundamentals) — Processing lake data.

- [Data Lakehouse](DataLakehouse) — Bringing ACID to the lake.

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