Data Pipeline Architecture

Integrating Agentic AI with Data Engineering

Document ID: C7-DATA-PIPELINE-ARCHITECTURE
Version: 1.0
Category: Technical Deep Dive

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Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    DATA SOURCES                              │
│   (Databases, APIs, Files, Streams, Events)                 │
└───────────────────────────┬─────────────────────────────────┘
                            │
┌───────────────────────────▼─────────────────────────────────┐
│                  INGESTION LAYER                             │
│            (Batch, Streaming, CDC)                          │
└───────────────────────────┬─────────────────────────────────┘
                            │
┌───────────────────────────▼─────────────────────────────────┐
│              PROCESSING LAYER                                │
│     (Transform, Enrich, Validate, AI Augment)               │
└───────────────────────────┬─────────────────────────────────┘
                            │
┌───────────────────────────▼─────────────────────────────────┐
│                STORAGE LAYER                                 │
│        (Data Lake, Warehouse, Vector Store)                 │
└───────────────────────────┬─────────────────────────────────┘
                            │
┌───────────────────────────▼─────────────────────────────────┐
│              SERVING LAYER                                   │
│     (Agents, Analytics, APIs, Applications)                 │
└─────────────────────────────────────────────────────────────┘

---

Batch Processing with Agentic AI

Document Processing Pipeline

from prefect import flow, task
from typing import List, Dict

@task
def extract_documents(source_path: str) -> List[bytes]:
    """Extract raw documents from source"""
    documents = []
    for file_path in glob.glob(f"{source_path}/*"):
        with open(file_path, 'rb') as f:
            documents.append({
                'path': file_path,
                'content': f.read(),
                'metadata': extract_metadata(file_path)
            })
    return documents

@task
def parse_documents(documents: List[Dict]) -> List[Dict]:
    """Parse documents using appropriate parsers"""
    parsed = []
    for doc in documents:
        parser = get_parser(doc['metadata']['type'])
        parsed.append({
            **doc,
            'text': parser.parse(doc['content']),
            'structure': parser.extract_structure(doc['content'])
        })
    return parsed

@task
def ai_enrich_documents(documents: List[Dict]) -> List[Dict]:
    """Use LLM to enrich document metadata"""
    enriched = []
    for doc in documents:
        # Batch for efficiency
        enrichment = llm_client.batch_complete([
            {
                'task': 'classify',
                'prompt': f"Classify this document: {doc['text'][:1000]}"
            },
            {
                'task': 'extract_entities',
                'prompt': f"Extract key entities: {doc['text'][:2000]}"
            },
            {
                'task': 'summarize',
                'prompt': f"Summarize in 2 sentences: {doc['text'][:3000]}"
            }
        ])
        
        enriched.append({
            **doc,
            'category': enrichment['classify'],
            'entities': enrichment['extract_entities'],
            'summary': enrichment['summarize']
        })
    return enriched

@task
def generate_embeddings(documents: List[Dict]) -> List[Dict]:
    """Generate vector embeddings for semantic search"""
    texts = [d['text'] for d in documents]
    embeddings = embedding_model.encode(texts, batch_size=32)
    
    for doc, embedding in zip(documents, embeddings):
        doc['embedding'] = embedding.tolist()
    
    return documents

@task
def load_to_stores(documents: List[Dict]):
    """Load to data warehouse and vector store"""
    # Load structured data to warehouse
    warehouse_records = [{
        'id': doc['metadata']['id'],
        'path': doc['path'],
        'category': doc['category'],
        'summary': doc['summary'],
        'entities': json.dumps(doc['entities']),
        'processed_at': datetime.utcnow()
    } for doc in documents]
    
    warehouse.bulk_insert('documents', warehouse_records)
    
    # Load to vector store
    vector_records = [{
        'id': doc['metadata']['id'],
        'embedding': doc['embedding'],
        'text': doc['text'][:10000],  # Truncate for storage
        'metadata': doc['metadata']
    } for doc in documents]
    
    vector_store.upsert(vector_records)

@flow
def document_processing_pipeline(source_path: str):
    """Main pipeline flow"""
    raw_docs = extract_documents(source_path)
    parsed_docs = parse_documents(raw_docs)
    enriched_docs = ai_enrich_documents(parsed_docs)
    embedded_docs = generate_embeddings(enriched_docs)
    load_to_stores(embedded_docs)

---

Streaming Architecture

Real-Time Event Processing

from kafka import KafkaConsumer, KafkaProducer
import asyncio

class StreamingAgentProcessor:
    def __init__(self, kafka_config, agent_config):
        self.consumer = KafkaConsumer(
            'incoming_events',
            **kafka_config,
            value_deserializer=lambda m: json.loads(m.decode('utf-8'))
        )
        self.producer = KafkaProducer(
            **kafka_config,
            value_serializer=lambda m: json.dumps(m).encode('utf-8')
        )
        self.agent = create_agent(agent_config)
    
    async def process_stream(self):
        """Process events from Kafka stream"""
        for message in self.consumer:
            event = message.value
            
            # Determine if AI processing needed
            if self.needs_ai_processing(event):
                result = await self.agent.process(event)
                enriched_event = {
                    **event,
                    'ai_analysis': result,
                    'processed_at': datetime.utcnow().isoformat()
                }
            else:
                enriched_event = event
            
            # Route to appropriate output topic
            output_topic = self.route_event(enriched_event)
            self.producer.send(output_topic, enriched_event)
    
    def needs_ai_processing(self, event: Dict) -> bool:
        """Determine if event needs AI analysis"""
        # Process high-priority or complex events
        return (
            event.get('priority') == 'high' or
            event.get('requires_analysis') or
            len(event.get('text', '')) > 500
        )
    
    def route_event(self, event: Dict) -> str:
        """Route event to appropriate output topic"""
        category = event.get('ai_analysis', {}).get('category', 'default')
        return f"processed_{category}"

Flink Integration

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.functions import ProcessFunction

class AIEnrichmentFunction(ProcessFunction):
    def __init__(self, model_config):
        self.model_config = model_config
        self.llm_client = None
    
    def open(self, runtime_context):
        # Initialize LLM client (once per task)
        self.llm_client = create_llm_client(self.model_config)
    
    def process_element(self, value, ctx):
        # Process element with AI
        if value['needs_analysis']:
            analysis = self.llm_client.analyze(value['content'])
            value['ai_result'] = analysis
        
        yield value

# Pipeline definition
env = StreamExecutionEnvironment.get_execution_environment()

source = env.add_source(kafka_source)
enriched = source.process(AIEnrichmentFunction(model_config))
enriched.add_sink(kafka_sink)

env.execute("AI Enrichment Pipeline")

---

ETL with AI Augmentation

dbt + LLM Integration

# dbt model with AI augmentation
# models/enriched_customers.sql

{{ config(materialized='incremental') }}

WITH raw_customers AS (
    SELECT * FROM {{ ref('stg_customers') }}
    {% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
    {% endif %}
),

ai_enriched AS (
    SELECT 
        customer_id,
        {{ ai_classify('customer_description', 'segment') }} as predicted_segment,
        {{ ai_extract('customer_notes', 'interests') }} as extracted_interests,
        {{ ai_sentiment('feedback_text') }} as sentiment_score
    FROM raw_customers
)

SELECT 
    r.*,
    e.predicted_segment,
    e.extracted_interests,
    e.sentiment_score
FROM raw_customers r
LEFT JOIN ai_enriched e ON r.customer_id = e.customer_id

# dbt macro for AI classification
# macros/ai_classify.sql

{% macro ai_classify(column, classification_type) %}
    (
        SELECT result 
        FROM TABLE(
            ai_service.classify(
                input => {{ column }},
                type => '{{ classification_type }}'
            )
        )
    )
{% endmacro %}

---

Data Quality with Agentic AI

Automated Data Validation

from great_expectations.core import ExpectationSuite
from typing import List, Dict

class AIDataValidator:
    def __init__(self, llm_client):
        self.llm = llm_client
    
    def validate_with_ai(self, dataframe, context: str) -> List[Dict]:
        """Use AI to identify data quality issues"""
        
        # Get statistical profile
        profile = dataframe.describe().to_dict()
        
        # Sample for AI analysis
        sample = dataframe.sample(min(100, len(dataframe))).to_dict('records')
        
        prompt = f"""
        Analyze this data for quality issues.
        
        Context: {context}
        
        Statistical Profile:
        {json.dumps(profile, indent=2)}
        
        Sample Records:
        {json.dumps(sample[:10], indent=2)}
        
        Identify:
        1. Missing value patterns
        2. Outliers and anomalies
        3. Format inconsistencies
        4. Logical errors
        5. Referential integrity issues
        
        Return as JSON with severity (high/medium/low) for each issue.
        """
        
        analysis = self.llm.complete(prompt)
        return json.loads(analysis)
    
    def generate_expectations(self, analysis: List[Dict]) -> ExpectationSuite:
        """Generate Great Expectations suite from AI analysis"""
        suite = ExpectationSuite(name="ai_generated")
        
        for issue in analysis:
            if issue['type'] == 'missing_value':
                suite.add_expectation(
                    expectation_type="expect_column_values_to_not_be_null",
                    column=issue['column']
                )
            elif issue['type'] == 'outlier':
                suite.add_expectation(
                    expectation_type="expect_column_values_to_be_between",
                    column=issue['column'],
                    min_value=issue['expected_min'],
                    max_value=issue['expected_max']
                )
            # ... additional expectation types
        
        return suite

---

Architecture Patterns

Lambda Architecture with AI

          ┌─────────────────────────────────────┐
          │          DATA SOURCES               │
          └─────────────────┬───────────────────┘
                            │
          ┌─────────────────▼───────────────────┐
          │         MESSAGE QUEUE               │
          │           (Kafka)                   │
          └─────────────────┬───────────────────┘
                  ┌─────────┴─────────┐
                  │                   │
       ┌──────────▼─────────┐ ┌──────▼──────────┐
       │    BATCH LAYER     │ │  SPEED LAYER    │
       │ (Deep AI Analysis) │ │(Fast AI Enrichm)│
       │   Spark + LLM      │ │  Flink + LLM    │
       └──────────┬─────────┘ └──────┬──────────┘
                  │                   │
       ┌──────────▼─────────────────▼──────────┐
       │            SERVING LAYER               │
       │     (Query across batch + speed)       │
       └───────────────────────────────────────┘

Medallion Architecture

BRONZE (Raw)          SILVER (Cleaned)       GOLD (Aggregated)
┌────────────┐       ┌────────────┐         ┌────────────┐
│ Raw events │──────►│AI Validated│────────►│ AI Insights│
│ Raw docs   │ Parse │AI Enriched │ Agg    │ AI Summary │
│ Raw data   │ Clean │AI Classified│ Join  │ AI Predict │
└────────────┘       └────────────┘         └────────────┘
     │                     │                      │
     │                     │                      │
  Append              Upsert/Merge            Overwrite
   Only                                       or Append

---

Performance Optimization

Batch Size Optimization

class OptimalBatchProcessor:
    def __init__(self, llm_client, max_tokens_per_batch=100000):
        self.llm = llm_client
        self.max_tokens = max_tokens_per_batch
    
    def process_in_optimal_batches(self, items: List[Dict]):
        """Process items in optimally-sized batches"""
        batches = self.create_batches(items)
        
        results = []
        for batch in batches:
            batch_result = self.llm.batch_complete(batch)
            results.extend(batch_result)
        
        return results
    
    def create_batches(self, items: List[Dict]) -> List[List[Dict]]:
        """Create batches that maximize throughput"""
        batches = []
        current_batch = []
        current_tokens = 0
        
        for item in items:
            item_tokens = estimate_tokens(item)
            
            if current_tokens + item_tokens > self.max_tokens:
                batches.append(current_batch)
                current_batch = [item]
                current_tokens = item_tokens
            else:
                current_batch.append(item)
                current_tokens += item_tokens
        
        if current_batch:
            batches.append(current_batch)
        
        return batches

---

Quick Reference

Pattern	Use Case	AI Role
Batch ETL	Document processing	Classification, enrichment
Streaming	Real-time events	Routing, anomaly detection
CDC	Database changes	Impact analysis
Data Quality	Validation	Issue detection, fixing
Lakehouse	Analytics	Summarization, insights

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Document maintained by CODITECT Data Engineering Team