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📖 3 min read 663 words
BACKEND

🔄 Data Pipelines

Building data pipelines that process millions of events reliably

⏱️ 5+ Years
📦 12+ Projects
✓ Available for new projects
Experience at: Spiio• OPERR• FinanceBuzz• Anaqua• Flowrite

🎯 What I Offer

Real-time Stream Processing

Build pipelines that process events as they happen with sub-second latency.

Deliverables
  • Kafka/RabbitMQ architecture
  • Stream processing applications
  • Real-time aggregations
  • Event-driven microservices
  • Exactly-once semantics

Batch ETL Pipelines

Design reliable batch processing for large-scale data transformations.

Deliverables
  • Data extraction from multiple sources
  • Transformation and validation
  • Data quality checks
  • Scheduled pipelines
  • Failure recovery

Data Integration & Sync

Connect disparate systems with reliable data synchronization.

Deliverables
  • API integrations
  • Database replication
  • Change data capture (CDC)
  • Schema mapping
  • Conflict resolution

🔧 Technical Deep Dive

Data Pipeline Challenges

Data pipelines fail because of:

  • Data quality issues: Garbage in, garbage out
  • Ordering problems: Events arriving out of sequence
  • Scale surprises: 10x traffic spikes breaking assumptions
  • Silent failures: Data loss without anyone noticing

My pipelines handle these from day one:

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class ReliablePipeline:
    async def process(self, event: Event):
        # Validate at ingestion
        if not self.validator.is_valid(event):
            await self.dead_letter.send(event)
            return
        
        # Deduplicate
        if await self.seen_before(event.id):
            return
        
        # Process with idempotency
        async with self.exactly_once(event.id):
            enriched = await self.enrich(event)
            await self.transform(enriched)
            await self.load(enriched)
        
        # Checkpoint for recovery
        await self.checkpoint(event.offset)

Streaming vs Batch: When to Use Each

Use Streaming (Kafka, Faust):

  • Real-time dashboards and alerts
  • Event-driven architectures
  • IoT sensor data
  • User activity tracking

Use Batch (Airflow, Celery):

  • Daily/hourly aggregations
  • Historical reprocessing
  • Complex transformations
  • Cross-system sync jobs

Use Hybrid:

  • Lambda architecture (streaming + batch)
  • Real-time for hot data, batch for cold

📋 Details & Resources

Data Pipeline Architecture

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┌─────────────────────────────────────────────────────────────┐
│                    Data Sources                              │
│    (APIs, Databases, IoT, Events, Files, Webhooks)          │
└─────────────────────────────────────────────────────────────┘
                              │
                         Ingestion
                              │
┌─────────────────────────────▼───────────────────────────────┐
│                   Message Broker                             │
│              (Kafka, RabbitMQ, Redis)                       │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
┌───────▼───────┐   ┌─────────▼─────────┐   ┌───────▼───────┐
│   Streaming   │   │     Batch         │   │   Real-time   │
│   Processors  │   │   Processors      │   │   Alerts      │
└───────────────┘   └───────────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
┌───────▼───────┐   ┌─────────▼─────────┐   ┌───────▼───────┐
│  Time-Series  │   │   Data Warehouse  │   │   Analytics   │
│ (TimescaleDB) │   │   (PostgreSQL)    │   │   (ClickHouse)│
└───────────────┘   └───────────────────┘   └───────────────┘

Stream Processing with Kafka

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# Real-time event processing
from faust import App, Record
from datetime import datetime

app = App('data-pipeline', broker='kafka://localhost:9092')

class SensorReading(Record):
    device_id: str
    value: float
    timestamp: datetime

sensor_topic = app.topic('sensors', value_type=SensorReading)
validated_topic = app.topic('sensors-validated', value_type=SensorReading)

@app.agent(sensor_topic)
async def process_readings(readings):
    async for reading in readings:
        # Validate
        if not is_valid(reading):
            await dead_letter.send(value=reading)
            continue
        
        # Enrich with metadata
        reading.zone = await get_zone(reading.device_id)
        reading.plant_type = await get_plant_type(reading.zone)
        
        # Check for anomalies
        if await is_anomaly(reading):
            await alerts.send(value=Alert(reading))
        
        # Forward to validated topic
        await validated_topic.send(value=reading)

# Windowed aggregations
@app.agent(validated_topic)
async def aggregate_readings(readings):
    async for reading in readings.group_by(SensorReading.zone):
        await update_hourly_aggregate(reading)

ETL Patterns I Implement

PatternUse CaseTechnology
Stream-firstReal-time requirementsKafka, Faust, Spark Streaming
Batch ETLHistorical processingAirflow, Celery, dbt
CDCDatabase replicationDebezium, Kafka Connect
LambdaMixed latency needsStreaming + batch
ELTTransform in warehousedbt, SQL transforms

Data Quality Framework

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class DataQualityPipeline:
    def validate(self, record: dict) -> ValidationResult:
        checks = [
            # Schema validation
            self.check_schema(record),
            # Required fields
            self.check_required_fields(record),
            # Range validation
            self.check_value_ranges(record),
            # Referential integrity
            self.check_references(record),
            # Business rules
            self.check_business_rules(record)
        ]
        
        failures = [c for c in checks if not c.passed]
        
        if failures:
            return ValidationResult(
                valid=False,
                errors=failures,
                action='dead_letter' if critical(failures) else 'flag'
            )
        
        return ValidationResult(valid=True)

Technologies for Data Pipelines

  • Streaming: Kafka, RabbitMQ, Redis Streams
  • Processing: Faust, Spark, Celery
  • Orchestration: Airflow, Dagster, Prefect
  • Storage: TimescaleDB, ClickHouse, PostgreSQL
  • Quality: Great Expectations, custom validators
  • Monitoring: Prometheus, Grafana, custom dashboards

Frequently Asked Questions

What is data pipeline development?

Data pipeline development involves building systems that extract data from various sources, transform it for analysis, and load it into data warehouses or lakes. This includes batch ETL, real-time streaming, data quality checks, and orchestration.

How much does data pipeline development cost?

Data pipeline development typically costs $110-160 per hour. A basic ETL pipeline starts around $15,000-30,000, while enterprise data platforms with multiple sources, real-time processing, and data quality governance range from $75,000-250,000+.

ETL vs ELT: which approach should I use?

Use ETL (transform before loading) when: you need to clean data before storage, have limited warehouse compute, or need to redact sensitive data. Use ELT (load then transform) when: you have powerful warehouses (Snowflake, BigQuery) and want flexibility. ELT is now more common.

What tools do you use for data pipelines?

I work with: Airflow (orchestration), dbt (transformation), Airbyte/Fivetran (ingestion), Spark (large-scale processing), and cloud-native tools (AWS Glue, GCP Dataflow). The choice depends on data volume, team skills, and existing infrastructure.

How do you ensure data pipeline reliability?

I implement: idempotent operations, data quality checks, schema validation, alerting on failures, backfill capabilities, lineage tracking, and proper retry logic. Production pipelines need observability and the ability to recover from failures.

Experience:

Case Studies:

Related Technologies: Kafka, Celery, PostgreSQL, Python, Redis

💼 Real-World Results

Agricultural IoT Pipeline

Spiio
Challenge

Process 50M+ daily sensor readings from 1,000+ devices with real-time anomaly detection.

Solution

Kafka streaming with Faust processors, TimescaleDB for time-series storage, ML-powered anomaly detection. Continuous aggregates for dashboards.

Result

100x query performance improvement, 30% water savings for clients through data-driven insights.

Content Analytics Pipeline

FinanceBuzz
Challenge

Track content performance across 5M+ monthly readers with real-time dashboards for editors.

Solution

Celery-based ETL pulling from Google Analytics, custom event tracking, Elasticsearch for analytics. Real-time metrics in CMS.

Result

300% increase in content velocity, editors see performance in real-time.

AI Embedding Pipeline

Anaqua
Challenge

Process millions of legal documents into vector embeddings for RAG system.

Solution

Batch pipeline with intelligent chunking, parallel embedding generation, incremental updates. PGVector storage with hybrid search.

Result

50% faster search, processing 10,000+ daily AI queries.

⚡ Why Work With Me

  • ✓ Built pipeline processing 50M+ daily events at Spiio
  • ✓ TimescaleDB expertise, 100x query performance improvements
  • ✓ Stream processing with Kafka and real-time analytics
  • ✓ Data quality focus, validation, anomaly detection
  • ✓ Full pipeline ownership, ingestion to visualization

Build Your Data Pipeline

Within 24 hours

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