Data Lakehouse Team Productivity —

Data Lakehouse Productivity

Data Lakehouse Team Productivity Architecture Catalog Query Optimization dbt Self-service Cost Management Delta Lake Iceberg

Feature	Data Lake	Data Warehouse	Data Lakehouse
Data Types	ทุกรูปแบบ	Structured เท่านั้น	ทุกรูปแบบ
ACID Transactions	ไม่มี	มี	มี (Delta/Iceberg)
Schema Enforcement	ไม่มี (Schema on Read)	มี (Schema on Write)	ทั้งสองแบบ
BI Support	จำกัด	ดีมาก	ดีมาก
ML Support	ดีมาก	จำกัด	ดีมาก
Cost	ต่ำ (Storage)	สูง (Compute+Storage)	กลาง (แยก Compute)

Architecture and Tools

# === Lakehouse Architecture ===

from dataclasses import dataclass

@dataclass
class LakehouseLayer:
    layer: str
    tools: str
    owner: str
    purpose: str

layers = [
    LakehouseLayer("Ingestion",
        "Airbyte, Fivetran, Kafka, Debezium, AWS DMS",
        "Data Engineer",
        "ดึงข้อมูลจาก Source เข้า Raw Layer"),
    LakehouseLayer("Raw / Bronze",
        "S3/GCS + Delta Lake/Iceberg, Parquet",
        "Data Engineer",
        "เก็บข้อมูลดิบ ไม่แก้ไข Append-only"),
    LakehouseLayer("Cleaned / Silver",
        "dbt, Spark, SQL, Great Expectations",
        "Analytics Engineer",
        "ทำความสะอาด Deduplicate Join Validate"),
    LakehouseLayer("Curated / Gold",
        "dbt, Spark SQL, Materialized Views",
        "Analytics Engineer",
        "Aggregate Business Metrics KPI Ready"),
    LakehouseLayer("Serving",
        "Trino, Presto, Databricks SQL, DuckDB",
        "Data Analyst / Scientist",
        "Query Layer สำหรับ BI และ Ad-hoc Analysis"),
    LakehouseLayer("Consumption",
        "Tableau, Looker, Power BI, Superset, Notebooks",
        "Business / Analyst / Scientist",
        "Dashboard Report Notebook Exploration"),
]

print("=== Lakehouse Layers ===")
for l in layers:
    print(f"  [{l.layer}] Owner: {l.owner}")
    print(f"    Tools: {l.tools}")
    print(f"    Purpose: {l.purpose}")

# Team structure
team = {
    "Data Engineer (2-3)": "Ingestion, Raw layer, Infrastructure, Pipeline reliability",
    "Analytics Engineer (2-3)": "dbt models, Silver/Gold layers, Data quality, Documentation",
    "Data Analyst (3-5)": "Dashboards, Ad-hoc analysis, Business insights, Self-service",
    "Data Scientist (1-2)": "ML models, Feature engineering, Experimentation",
    "Data Platform (1-2)": "Infrastructure, Cost optimization, Security, Governance",
}

print(f"\n\nTeam Structure:")
for k, v in team.items():
    print(f"  [{k}]: {v}")

dbt and Data Quality

# === dbt Workflow ===

# dbt project structure
# lakehouse/
# ├── models/
# │   ├── staging/        # Bronze → Silver
# │   │   ├── stg_orders.sql
# │   │   └── stg_customers.sql
# │   ├── intermediate/   # Silver transforms
# │   │   └── int_order_items.sql
# │   └── marts/          # Gold / Business
# │       ├── fct_daily_revenue.sql
# │       └── dim_customers.sql
# ├── tests/
# │   └── assert_positive_revenue.sql
# ├── macros/
# │   └── generate_schema_name.sql
# └── dbt_project.yml

# dbt commands
# dbt run                    # Run all models
# dbt test                   # Run all tests
# dbt build                  # Run + Test
# dbt run --select staging   # Run specific folder
# dbt docs generate          # Generate documentation
# dbt docs serve             # Serve documentation UI

# Great Expectations example
# import great_expectations as gx
# context = gx.get_context()
# batch = context.get_batch("orders_table")
# batch.expect_column_values_to_not_be_null("order_id")
# batch.expect_column_values_to_be_between("amount", 0, 1000000)
# results = context.run_validation()

@dataclass
class QualityCheck:
    check: str
    tool: str
    layer: str
    frequency: str
    action_on_fail: str

checks = [
    QualityCheck("Schema validation", "dbt schema tests",
        "Bronze → Silver", "Every run",
        "Block pipeline, alert Data Engineer"),
    QualityCheck("Null check", "dbt not_null test",
        "Silver", "Every run",
        "Block if critical column, warn if optional"),
    QualityCheck("Uniqueness", "dbt unique test",
        "Silver + Gold", "Every run",
        "Block pipeline, investigate duplicates"),
    QualityCheck("Freshness", "dbt source freshness",
        "Bronze", "Hourly",
        "Alert if data > 2 hours stale"),
    QualityCheck("Row count anomaly", "Great Expectations / Soda",
        "All layers", "Daily",
        "Alert if ±50% from expected"),
    QualityCheck("Business rule", "Custom dbt test / GE",
        "Gold", "Every run",
        "Alert Business + Analytics team"),
]

print("\n=== Data Quality Checks ===")
for c in checks:
    print(f"  [{c.layer}] {c.check}")
    print(f"    Tool: {c.tool} | Freq: {c.frequency}")
    print(f"    On fail: {c.action_on_fail}")

Cost Optimization

# === Cost Management ===

@dataclass
class CostStrategy:
    strategy: str
    saving: str
    implementation: str
    risk: str

strategies = [
    CostStrategy("Spot/Preemptible Instances",
        "60-90% compute cost reduction",
        "Use for batch Spark jobs, dbt runs",
        "Low (retry on preemption)"),
    CostStrategy("Auto-scaling to zero",
        "100% when idle",
        "Serverless SQL (Databricks/Athena) or auto-stop clusters",
        "Cold start delay 30s-2min"),
    CostStrategy("Partition Pruning",
        "50-90% scan reduction",
        "Partition by date, region; filter in WHERE clause",
        "None"),
    CostStrategy("Z-ordering / Clustering",
        "30-70% scan reduction for filtered queries",
        "OPTIMIZE table ZORDER BY (column)",
        "None (maintenance overhead minimal)"),
    CostStrategy("Caching Layer",
        "80-95% for repeated queries",
        "Materialized views, Databricks result cache",
        "Stale data (set TTL)"),
    CostStrategy("Lifecycle Policy",
        "40-60% storage cost",
        "Move data > 90 days to IA, > 365 to Glacier",
        "Retrieval delay for cold data"),
    CostStrategy("Query Cost Monitoring",
        "Awareness → 20-30% reduction",
        "Tag queries by team, set per-team budget alerts",
        "None"),
]

print("=== Cost Strategies ===")
for s in strategies:
    print(f"  [{s.strategy}] Saving: {s.saving}")
    print(f"    How: {s.implementation}")
    print(f"    Risk: {s.risk}")

เคล็ดลับ

• Catalog: ใช้ Data Catalog ให้ทุกู้คืนค้นหาข้อมูลได้เอง ลดคอขวด
• dbt: ใช้ dbt สร้าง Transformation ที่ทดสอบได้ มี Documentation
• Self-service: สร้าง Gold Layer ให้ Analyst Query ได้เลย ไม่ต้องรอ Engineer
• Cost: Monitor Query Cost ต่อทีม ตั้ง Budget Alert ป้องกัน Surprise Bill
• Quality: ใส่ Data Quality Test ทุก Layer ตรวจจับปัญหาก่อนถึง Dashboard

Data Lakehouse คืออะไร

Data Lake + Warehouse ทุกรูปแบบ ACID Transactions Open Format Parquet Delta Iceberg BI ML Storage เดียว ลด Copy ประหยัด Databricks