Data Lake Architecture: Level 3 Maturity

In Level 3 of the Data Maturity Lifecycle, 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:

# 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.

---

See Also:

• Data Warehouse Design — The predecessor to lakes.
• Apache Spark Fundamentals — Processing lake data.
• Data Lakehouse — Bringing ACID to the lake.

---