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<STRING>

) USING DELTA

TBLPROPERTIES ('delta.enableChangeDataFeed' = 'true');

-- User Interaction Logs

CREATE TABLE raw_data.interaction_logs (

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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,

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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

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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),

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]

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

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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(

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"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

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