Data Lakehouse Team Productivity —

Data Lakehouse Productivity

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

เนื้อหาเกี่ยวข้อง — แนะนำให้อ่าน gen z เกิดปีไหน — ข้อมูลครบถ้วน 2026

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}")