Data Engineering Fundamentals

Core data engineering concepts, patterns, and practices for building production data platforms.

Quick Start

# Production Data Pipeline Pattern
from dataclasses import dataclass
from datetime import datetime
from typing import Generator
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class PipelineConfig:
    source: str
    destination: str
    batch_size: int = 10000
    retry_count: int = 3

class DataPipeline:
    """Production-ready data pipeline with error handling."""

    def __init__(self, config: PipelineConfig):
        self.config = config
        self.metrics = {"extracted": 0, "transformed": 0, "loaded": 0, "errors": 0}

    def extract(self) -> Generator[dict, None, None]:
        """Extract data in batches from source."""
        logger.info(f"Extracting from {self.config.source}")
        offset = 0
        while True:
            batch = self._fetch_batch(offset, self.config.batch_size)
            if not batch:
                break
            self.metrics["extracted"] += len(batch)
            yield batch
            offset += self.config.batch_size

    def transform(self, batch: list[dict]) -> list[dict]:
        """Apply transformations with validation."""
        transformed = []
        for record in batch:
            try:
                cleaned = self._clean_record(record)
                enriched = self._enrich_record(cleaned)
                transformed.append(enriched)
            except Exception as e:
                logger.warning(f"Transform error: {e}")
                self.metrics["errors"] += 1
        self.metrics["transformed"] += len(transformed)
        return transformed

    def load(self, batch: list[dict]) -> None:
        """Load to destination with retry logic."""
        for attempt in range(self.config.retry_count):
            try:
                self._write_batch(batch)
                self.metrics["loaded"] += len(batch)
                return
            except Exception as e:
                if attempt == self.config.retry_count - 1:
                    raise
                logger.warning(f"Load attempt {attempt + 1} failed: {e}")

    def run(self) -> dict:
        """Execute full ETL pipeline."""
        start_time = datetime.now()
        logger.info("Pipeline started")

        for batch in self.extract():
            transformed = self.transform(batch)
            if transformed:
                self.load(transformed)

        duration = (datetime.now() - start_time).total_seconds()
        self.metrics["duration_seconds"] = duration
        logger.info(f"Pipeline completed: {self.metrics}")
        return self.metrics

Core Concepts

1. Data Architecture Patterns

┌─────────────────────────────────────────────────────────────────┐
│                    Modern Data Architecture                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Sources          Ingestion        Storage         Consumption  │
│  ────────         ─────────        ───────         ───────────  │
│  ┌──────┐        ┌─────────┐      ┌───────┐       ┌──────────┐ │
│  │ APIs │───────▶│ Airbyte │─────▶│ Raw   │──────▶│ BI Tools │ │
│  │ DBs  │        │ Fivetran│      │ Layer │       │ Dashboards│ │
│  │ Files│        │ Kafka   │      │ (S3)  │       └──────────┘ │
│  │ SaaS │        └─────────┘      └───┬───┘                    │
│  └──────┘                             │                         │
│                                       ▼                         │
│                               ┌───────────────┐                 │
│                               │  Transform    │                 │
│                               │  (dbt/Spark)  │                 │
│                               └───────┬───────┘                 │
│                                       │                         │
│                                       ▼                         │
│                               ┌───────────────┐   ┌──────────┐ │
│                               │   Warehouse   │──▶│ ML/AI    │ │
│                               │  (Snowflake)  │   │ Pipelines│ │
│                               └───────────────┘   └──────────┘ │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

2. ETL vs ELT

# ETL Pattern (Transform before load)
# Best for: Sensitive data, complex transformations, limited storage

def etl_pipeline():
    raw_data = extract_from_source()
    cleaned_data = transform_and_clean(raw_data)  # Transform first
    load_to_destination(cleaned_data)

# ELT Pattern (Load then transform)
# Best for: Cloud warehouses, large scale, exploratory analysis

def elt_pipeline():
    raw_data = extract_from_source()
    load_to_staging(raw_data)  # Load raw first
    # Transform in warehouse with SQL/dbt
    run_dbt_models()

3. Data Quality Framework

from dataclasses import dataclass
from typing import Callable, Any
import pandas as pd

@dataclass
class DataQualityCheck:
    name: str
    check_fn: Callable[[pd.DataFrame], bool]
    severity: str  # "error" or "warning"

class DataQualityValidator:
    def __init__(self, checks: list[DataQualityCheck]):
        self.checks = checks
        self.results = []

    def validate(self, df: pd.DataFrame) -> bool:
        all_passed = True
        for check in self.checks:
            passed = check.check_fn(df)
            self.results.append({
                "check": check.name,
                "passed": passed,
                "severity": check.severity
            })
            if not passed and check.severity == "error":
                all_passed = False
        return all_passed

# Define checks
checks = [
    DataQualityCheck(
        name="no_nulls_in_id",
        check_fn=lambda df: df["id"].notna().all(),
        severity="error"
    ),
    DataQualityCheck(
        name="positive_amounts",
        check_fn=lambda df: (df["amount"] > 0).all(),
        severity="error"
    ),
    DataQualityCheck(
        name="valid_dates",
        check_fn=lambda df: pd.to_datetime(df["date"], errors="coerce").notna().all(),
        severity="warning"
    ),
]

validator = DataQualityValidator(checks)
if not validator.validate(df):
    raise ValueError(f"Data quality checks failed: {validator.results}")

4. Idempotent Operations

from datetime import date

def idempotent_load(df: pd.DataFrame, table: str, partition_date: date):
    """
    Idempotent load: can be re-run safely without duplicates.
    Uses delete-then-insert pattern.
    """
    # Delete existing data for this partition
    db.execute(f"""
        DELETE FROM {table}
        WHERE partition_date = %(date)s
    """, {"date": partition_date})

    # Insert new data
    df["partition_date"] = partition_date
    df.to_sql(table, db, if_exists="append", index=False)

# Alternative: MERGE/UPSERT pattern
def upsert_records(df: pd.DataFrame, table: str, key_columns: list[str]):
    """Upsert: Update existing, insert new."""
    temp_table = f"{table}_staging"
    df.to_sql(temp_table, db, if_exists="replace", index=False)

    key_match = " AND ".join([f"t.{k} = s.{k}" for k in key_columns])

    db.execute(f"""
        MERGE INTO {table} t
        USING {temp_table} s ON {key_match}
        WHEN MATCHED THEN UPDATE SET ...
        WHEN NOT MATCHED THEN INSERT ...
    """)

Tools & Technologies

Tool	Purpose	Version (2025)
Python	Pipeline development	3.12+
SQL	Data transformation	-
Airflow	Orchestration	2.8+
dbt	SQL transformations	1.7+
Spark	Large-scale processing	3.5+
Airbyte	Data integration	0.55+
Great Expectations	Data quality	0.18+

Troubleshooting Guide

Issue	Symptoms	Root Cause	Fix
Duplicate Data	Count mismatch	Non-idempotent load	Use MERGE, add dedup
Schema Drift	Pipeline failure	Source schema changed	Schema validation, alerts
Data Freshness	Stale data	Pipeline delays	Monitor SLAs, alerting
Memory Error	OOM in pipeline	Large batch size	Chunked processing

Best Practices

# ✅ DO: Make pipelines idempotent
delete_and_insert(partition_date)

# ✅ DO: Add observability
logger.info(f"Processed {count} records", extra={"metric": "records_processed"})

# ✅ DO: Validate data at boundaries
validate_schema(df, expected_schema)
validate_constraints(df)

# ✅ DO: Use incremental processing
WHERE updated_at > last_run_timestamp

# ❌ DON'T: Process all data every run
# ❌ DON'T: Skip data quality checks
# ❌ DON'T: Ignore schema changes

Resources

• Data Engineering Wiki
• Fundamentals of Data Engineering (Book)
• dbt Best Practices

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Skill Certification Checklist:

• Can design end-to-end data pipelines
• Can implement idempotent data loads
• Can set up data quality validation
• Can choose appropriate ETL vs ELT patterns
• Can monitor and troubleshoot pipelines