Databricks Unity Catalog AR VR Development

Databricks Unity Catalog AR VR

Unity Catalog Centralized Data Governance Catalog Schema Table Access Control Lineage Audit AR VR 3D Model Spatial Data Real-time Processing Multi-cloud

Unity Catalog Setup

# === Databricks Unity Catalog Setup ===

# SQL — Create Catalog and Schema
# CREATE CATALOG ar_vr_project;
# USE CATALOG ar_vr_project;
#
# CREATE SCHEMA raw_data;
# CREATE SCHEMA processed_data;
# CREATE SCHEMA ml_models;
# CREATE SCHEMA analytics;

# -- 3D Model Assets Table
# CREATE TABLE raw_data.model_assets (
#   asset_id STRING,
#   name STRING,
#   format STRING,  -- glTF, FBX, OBJ, USD
#   file_path STRING,
#   file_size_mb DOUBLE,
#   vertex_count BIGINT,
#   polygon_count BIGINT,
#   texture_resolution STRING,
#   created_by STRING,
#   created_at TIMESTAMP,
#   tags ARRAY
# ) USING DELTA
# TBLPROPERTIES ('delta.enableChangeDataFeed' = 'true');

# -- User Interaction Logs
# CREATE TABLE raw_data.interaction_logs (
#   session_id STRING,
#   user_id STRING,
#   device_type STRING,  -- Quest3, VisionPro, HoloLens
#   event_type STRING,   -- gaze, grab, teleport, menu
#   position_x DOUBLE,
#   position_y DOUBLE,
#   position_z DOUBLE,
#   rotation_x DOUBLE,
#   rotation_y DOUBLE,
#   rotation_z DOUBLE,
#   timestamp TIMESTAMP,
#   fps INT,
#   latency_ms DOUBLE
# ) USING DELTA
# PARTITIONED BY (date_trunc('day', timestamp));

# -- Access Control
# GRANT USE CATALOG ON CATALOG ar_vr_project TO `developers`;
# GRANT USE SCHEMA ON SCHEMA raw_data TO `developers`;
# GRANT SELECT ON TABLE raw_data.model_assets TO `developers`;
# GRANT ALL PRIVILEGES ON SCHEMA processed_data TO `data_engineers`;

# -- Row-level Security
# CREATE FUNCTION raw_data.team_filter(team STRING)
# RETURN IF(IS_ACCOUNT_GROUP_MEMBER(team), true, false);
#
# ALTER TABLE raw_data.model_assets
# SET ROW FILTER raw_data.team_filter ON (created_by);

from dataclasses import dataclass, field
from typing import List, Dict

@dataclass
class CatalogObject:
    catalog: str
    schema: str
    table: str
    owner: str
    row_count: int
    size_gb: float

objects = [
    CatalogObject("ar_vr_project", "raw_data", "model_assets", "3d_team", 15000, 2.5),
    CatalogObject("ar_vr_project", "raw_data", "interaction_logs", "analytics", 50000000, 120.0),
    CatalogObject("ar_vr_project", "raw_data", "point_clouds", "3d_team", 500, 500.0),
    CatalogObject("ar_vr_project", "processed_data", "user_heatmaps", "analytics", 10000, 5.0),
    CatalogObject("ar_vr_project", "ml_models", "gaze_predictor", "ml_team", 50, 0.5),
]

print("Unity Catalog Objects:")
for obj in objects:
    print(f"  {obj.catalog}.{obj.schema}.{obj.table}")
    print(f"    Owner: {obj.owner} | Rows: {obj.row_count:,} | Size: {obj.size_gb}GB")

AR/VR Data Pipeline

# === AR/VR Data Processing Pipeline ===

# PySpark — Process Interaction Logs
# from pyspark.sql import SparkSession
# from pyspark.sql.functions import *
# from pyspark.sql.window import Window
#
# spark = SparkSession.builder.appName("ar_vr_analytics").getOrCreate()
#
# # Read interaction logs
# logs = spark.table("ar_vr_project.raw_data.interaction_logs")
#
# # Session Analytics
# session_stats = logs.groupBy("session_id", "user_id", "device_type").agg(
#     count("*").alias("event_count"),
#     avg("fps").alias("avg_fps"),
#     avg("latency_ms").alias("avg_latency"),
#     min("timestamp").alias("session_start"),
#     max("timestamp").alias("session_end"),
#     countDistinct("event_type").alias("unique_events"),
# ).withColumn(
#     "session_duration_min",
#     (unix_timestamp("session_end") - unix_timestamp("session_start")) / 60
# )
#
# session_stats.write.mode("overwrite").saveAsTable(
#     "ar_vr_project.processed_data.session_analytics"
# )
#
# # Heatmap Generation
# heatmap = logs.filter(col("event_type") == "gaze").groupBy(
#     round(col("position_x"), 1).alias("x"),
#     round(col("position_y"), 1).alias("y"),
#     round(col("position_z"), 1).alias("z"),
# ).agg(
#     count("*").alias("gaze_count"),
#     avg("latency_ms").alias("avg_latency"),
# )
#
# heatmap.write.mode("overwrite").saveAsTable(
#     "ar_vr_project.processed_data.gaze_heatmap"
# )

# Device Performance Analysis
device_metrics = {
    "Meta Quest 3": {"avg_fps": 72, "avg_latency": 20, "resolution": "2064x2208"},
    "Apple Vision Pro": {"avg_fps": 90, "avg_latency": 12, "resolution": "3660x3200"},
    "HoloLens 2": {"avg_fps": 60, "avg_latency": 25, "resolution": "2048x1080"},
    "PSVR 2": {"avg_fps": 120, "avg_latency": 15, "resolution": "2000x2040"},
}

print("\nAR/VR Device Performance:")
for device, metrics in device_metrics.items():
    print(f"  [{device}]")
    print(f"    FPS: {metrics['avg_fps']} | Latency: {metrics['avg_latency']}ms | "
          f"Res: {metrics['resolution']}")

# 3D Data Formats
formats = {
    "glTF/GLB": {"size": "เล็ก", "quality": "ดี", "use": "Web, Mobile AR"},
    "FBX": {"size": "ปานกลาง", "quality": "ดีมาก", "use": "Game Engine"},
    "USD": {"size": "ใหญ่", "quality": "ดีมาก", "use": "Film, Apple Vision Pro"},
    "OBJ": {"size": "ปานกลาง", "quality": "พื้นฐาน", "use": "Simple 3D Models"},
    "Point Cloud (PLY)": {"size": "ใหญ่มาก", "quality": "สูง", "use": "LiDAR, 3D Scan"},
}

print(f"\n\n3D Data Formats:")
for fmt, info in formats.items():
    print(f"  {fmt}: Size={info['size']} | Quality={info['quality']} | {info['use']}")

Data Governance

# === Data Governance for AR/VR ===

governance_framework = {
    "Access Control": {
        "level": "Row & Column Level Security",
        "tools": "Unity Catalog GRANT/REVOKE",
        "policy": "Least Privilege — ให้สิทธิ์เท่าที่จำเป็น",
    },
    "Data Lineage": {
        "level": "Table & Column Level",
        "tools": "Unity Catalog Lineage Graph",
        "policy": "ติดตามที่มาข้อมูลตั้งแต่ Raw ถึง Dashboard",
    },
    "Audit Logging": {
        "level": "ทุกการเข้าถึง",
        "tools": "Unity Catalog Audit Logs + SIEM",
        "policy": "บันทึกและตรวจสอบการเข้าถึงข้อมูลสำคัญ",
    },
    "Data Quality": {
        "level": "Table Level",
        "tools": "Delta Live Tables Expectations",
        "policy": "ตรวจสอบคุณภาพข้อมูลก่อนใช้งาน",
    },
    "Privacy": {
        "level": "PII Detection",
        "tools": "Column Masking, Dynamic Views",
        "policy": "ซ่อน PII สำหรับผู้ไม่มีสิทธิ์ GDPR Compliance",
    },
}

print("Data Governance Framework:")
for area, info in governance_framework.items():
    print(f"\n  [{area}]")
    for k, v in info.items():
        print(f"    {k}: {v}")

# Delta Sharing
sharing_config = {
    "Internal Teams": "แชร์ Processed Data ให้ Analytics Team",
    "Partner Studios": "แชร์ 3D Assets ผ่าน Delta Sharing",
    "ML Team": "แชร์ Training Data สำหรับ Model Development",
    "QA Team": "แชร์ Performance Metrics สำหรับ Testing",
}

print(f"\n\nDelta Sharing Use Cases:")
for recipient, desc in sharing_config.items():
    print(f"  {recipient}: {desc}")

เคล็ดลับ

• Partitioning: Partition Interaction Logs ตาม Date ลดเวลา Query
• Z-Ordering: Z-ORDER BY session_id สำหรับ Lookup Queries
• Delta Lake: ใช้ Change Data Feed ติดตามการเปลี่ยนแปลง
• Streaming: ใช้ Structured Streaming สำหรับ Real-time Analytics
• Compression: ใช้ glTF/GLB สำหรับ 3D Assets ขนาดเล็ก โหลดเร็ว

Databricks Unity Catalog คืออะไร

Centralized Data Governance Catalog Schema Table Access Control Lineage Audit Multi-cloud AWS Azure GCP Fine-grained Row Column Security

สรุป

Databricks Unity Catalog Data Governance AR VR Development 3D Assets Interaction Logs Delta Lake Spark MLflow Delta Sharing Access Control Lineage Audit Point Cloud Real-time Processing