Edge-Based Engineering Architecture Patterns

Introduction

Edge-based engineering represents a fundamental paradigm shift in distributed systems architecture. Unlike traditional cloud architecture which follows a "center-out" model, edge-based engineering adopts an "edge-in" approach that prioritizes local autonomy, smart persistence, and resilience under constraint.

The edge refers to computing that happens physically close to the data source—medical devices, smartphones, factory sensors, or local servers—rather than in centralized cloud infrastructure. This proximity enables systems that are faster, more private, and more reliable than cloud-only models.

Core Challenges

Edge-based systems must solve three primary distributed systems challenges:

• Latency: Minimize response time by processing data locally
• Intermittent Connectivity: Maintain full functionality during network disruptions
• Massive Data Volume: Handle large-scale data with limited hardware resources

Architectural Paradigm: From ETL to SQL-First ELT

Traditional ETL (Legacy Approach)

Workflow: Data is extracted, transformed using external scripts (Python, Spark, Java), then loaded into storage.

Edge Challenges:

• Requires significant middleware on constrained devices
• Complex, opaque transformation logic makes debugging difficult in remote locations
• Poor data lineage and reproducibility
• Heavy resource requirements unsuitable for edge hardware

SQL-First ELT (Modern Edge Standard)

Workflow: Raw data is loaded immediately into a local engine. All transformations happen afterward using SQL inside the database.

Key Advantages:

• Portability: SQL files + raw data = complete database state reproducible anywhere
• Transparency: Transformation logic is the "source of truth" alongside the data
• Universality: SQL is widely understood and standardized
• Simplicity: Reduces dependency on external tooling and middleware

Tools like surveilr use this approach to ensure deterministic, reproducible data pipelines at the edge.

Core Data Engineering Patterns

The LTP Pattern (Load → Transform → Persist)

LTP is a specialized, high-efficiency variant of ELT optimized for constrained environments and "local-first" software.

-- Load raw data into DuckDB
CREATE TABLE raw_data AS 
SELECT * FROM read_csv_auto('sensor_data.csv');

-- Complex analytical transformation
WITH windowed_data AS (
    SELECT 
        timestamp,
        sensor_id,
        value,
        AVG(value) OVER (
            PARTITION BY sensor_id 
            ORDER BY timestamp 
            ROWS BETWEEN 10 PRECEDING AND CURRENT ROW
        ) AS moving_avg
    FROM raw_data
)
SELECT * FROM windowed_data WHERE value > moving_avg * 1.2;

-- Persist refined results to SQLite
ATTACH 'sensor_analytics.db' AS sqlite_db (TYPE SQLITE);

INSERT INTO sqlite_db.sensor_alerts 
SELECT * FROM windowed_data WHERE value > moving_avg * 1.2;

Strategic Advantages

• Prevents Database Bloat: Massive raw files remain outside the operational database
• Performance: Edge devices stay fast and responsive by avoiding unnecessary disk I/O
• Efficiency: Only store what end-users or dashboards actually need to see
• Resource Optimization: Ideal for constrained hardware environments

The Multi-Engine Strategy (DuckDB + SQLite)

Sophisticated edge architectures leverage different database engines for different lifecycle stages:

Integration Pattern: DuckDB crunches massive raw datasets and streams refined results directly into SQLite for permanent storage and application access.

Dynamic SQL via JSON Pattern

This pattern enables schema-agnostic, adaptive data pipelines that work across varying data structures without code changes.

Core DuckDB Functions

-- Convert SQL string to structured JSON AST
json_serialize_sql(string)

-- Convert JSON AST back to valid SQL
json_deserialize_sql(json)

-- Execute logic from JSON AST directly
json_execute_serialized_sql(json)

Implementation Workflow

ATTACH 'persistent_store.db' AS sqlite_db (TYPE SQLITE);

WITH mapping_logic AS (
    SELECT 
        printf(
            'INSERT INTO sqlite_db.target_table 
             SELECT %s AS standard_col1, %s AS standard_col2 
             FROM read_csv_auto(''raw_source.csv'')',
            'input_field_a',
            'input_field_b'
        ) AS generated_query
)

SELECT * FROM json_execute_serialized_sql(
    json_serialize_sql((SELECT generated_query FROM mapping_logic))
);

Pattern Benefits

• Schema Agnosticism: Handle datasets with 5 or 500 columns using the same script
• Security: Query structure validation before execution reduces injection risks
• Efficiency: Eliminates intermediate application layers (Python, Node.js)
• Generic Data Hub: Supports Parquet, CSV, JSON inputs with automatic mapping

Infrastructure Patterns

The Hybrid Edge-Cloud Pattern

No edge device operates in complete isolation. This pattern defines intelligent interaction between edge and cloud.

Workload Partitioning:

• Edge: Real-time, safety-critical tasks (e.g., emergency shutdowns based on sensor data)
• Cloud: Long-term trend analysis, ML model training, historical analytics

Gated Ingress/Egress:

• Data sent to cloud in controlled "bursts" or "summaries"
• Reduces bandwidth consumption and maintains security
• Prevents continuous streaming of raw data

The Hub-and-Spoke Pattern

Common in large facilities like hospitals, factories, or distributed sensor networks.

Architecture:

• Spokes: Individual sensors or low-power IoT devices (e.g., CGM sensors, temperature monitors)
• Hub: More powerful local gateway or edge server

Mechanism:

• Hub collects data from multiple spokes
• Performs LTP processing locally
• Provides unified API or dashboard for the location
• Acts as intermediary between spokes and cloud

Key Design Principles

Real-World Application: Medical Data Processing

These patterns solve concrete problems in healthcare edge computing:

-- Process locally, persist only anonymized results
WITH patient_metrics AS (
    SELECT 
        hash(patient_id) AS anon_id,
        AVG(glucose_level) AS avg_glucose,
        date
    FROM raw_cgm_data
    GROUP BY patient_id, date
)
INSERT INTO metrics SELECT * FROM patient_metrics;

-- Process millions of rows in-memory
SELECT 
    timestamp,
    glucose_value,
    LEAD(glucose_value) OVER (ORDER BY timestamp) - glucose_value AS delta
FROM read_parquet('cgm_data/*.parquet')
WHERE patient_id = ?;

Implementation in Spry

The Spry project exemplifies these patterns:

• SQL-First ELT: All transformation logic in portable SQL
• LTP Pattern: DuckDB for processing, SQLite for persistence
• Multi-Engine: Leverages strengths of both database systems
• Dynamic SQL: Schema-agnostic processing of varying CGM data formats
• Local-First: Full functionality without internet connectivity
• Privacy-Preserving: Sensitive patient data processed locally

Summary

Edge-based engineering isn't about deploying "smaller servers"—it's about fundamentally smarter architectural patterns. By shifting from ETL to SQL-First ELT/LTP and leveraging multi-engine strategies, we build systems that are:

• Faster: Local processing eliminates network latency
• More Private: Sensitive data processed and stored locally
• More Reliable: Function independently of cloud connectivity
• More Efficient: Optimized for constrained hardware resources
• More Maintainable: SQL-based transformations are portable and transparent

These patterns represent the future of distributed systems architecture, enabling powerful computing capabilities at the network edge where data originates and decisions must be made in real-time.