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Session 1 - Module C: Complex State Management

⚠️ ADVANCED OPTIONAL MODULE Prerequisites: Complete Session 1 core content first.

In distributed data processing systems, an agent must simultaneously track the state of thousands of concurrent ETL jobs, maintain consistency across multiple data centers, and preserve critical pipeline metadata through system restarts and rolling updates. When a data quality agent identifies anomalous patterns in streaming data, it needs to correlate this with pipeline state from the past 48 hours, current batch processing status, and ongoing ML training workflows - all while managing state within pod memory limits and ensuring GDPR compliance for data retention.

This module explores sophisticated state management patterns essential for production data processing systems. Whether you're building pipeline orchestrators that need to track job dependencies across multiple clusters, ML agents that maintain training state through infrastructure changes, or quality monitors that preserve anomaly detection patterns across system upgrades, these patterns form the foundation of robust cloud-native data systems.

The distinction between a prototype data pipeline and a production-ready data processing system lies in sophisticated state management. We'll examine patterns that enable agents to maintain consistency across distributed processing, handle graceful degradation under resource pressure, and preserve critical operational state - essential capabilities for systems that must operate reliably in high-throughput cloud environments processing petabyte-scale data.

Part 1: Data Processing Memory Systems

Advanced Memory Architecture

🗂️ File: src/session1/conversation_memory.py - Advanced memory management systems

In data processing contexts, memory management extends beyond simple conversation tracking. Consider a pipeline orchestration agent that must correlate current batch status with historical processing patterns, or a cost optimization system that needs to maintain resource utilization trends through cluster scaling events. The foundation is a structured memory system with priority levels aligned to data engineering criticality:

from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import json
import sqlite3
from enum import Enum
import numpy as np
from sentence_transformers import SentenceTransformer

class DataProcessingMemoryPriority(Enum):
    LOW = 1        # Debug logs, temporary metrics, development queries
    MEDIUM = 2     # Processing history, performance trends, batch summaries
    HIGH = 3       # Pipeline state, resource allocations, data quality metrics
    CRITICAL = 4   # SLA commitments, compliance records, data lineage

The priority system aligns with data engineering operational impact levels, ensuring compliance and data lineage information is never discarded due to memory pressure.

@dataclass
class DataProcessingMemory:
    id: str
    content: str
    timestamp: datetime

The DataProcessingMemory structure forms the core unit of the memory system, designed to handle cloud-native data processing requirements:

```python priority: DataProcessingMemoryPriority context_tags: List

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