De Production Readiness

DE Production Readiness

When to Use

Load this skill when the user needs to:

• Make a data pipeline idempotent and safe to retry or re-run
• Implement exponential backoff, retry logic, or error classification in Python or Airflow
• Set up SLA monitoring, alerting (Slack, PagerDuty), or Prometheus metrics for a pipeline
• Define or enforce data freshness SLAs and automate freshness checks
• Add structured logging, OpenTelemetry tracing, or observability metrics to a pipeline
• Build or audit data reconciliation: row counts, checksums, duplicate detection
• Implement graceful degradation — circuit breakers, stale-data fallback, skipping bad partitions
• Perform a blue/green table swap, schema migration, or define a rollback procedure

1. Idempotency Patterns

An idempotent pipeline produces the same final state whether it runs once or ten times for the same logical batch. This is the single most important property of a reliable pipeline because it makes retries, backfills, and incident recovery safe.

Idempotent INSERT — ON CONFLICT DO NOTHING (PostgreSQL / Trino)

-- PostgreSQL: dedup on natural key
INSERT INTO orders (order_id, customer_id, amount, created_at)
SELECT order_id, customer_id, amount, created_at
FROM orders_staging
ON CONFLICT (order_id) DO NOTHING;

-- Trino / Iceberg: use MERGE as the equivalent
MERGE INTO orders AS t
USING orders_staging AS s
ON t.order_id = s.order_id
WHEN NOT MATCHED THEN
  INSERT (order_id, customer_id, amount, created_at)
  VALUES (s.order_id, s.customer_id, s.amount, s.created_at);

Idempotent MERGE — Upsert Pattern

-- Works for Spark SQL, Trino+Iceberg, Delta Lake, ClickHouse ReplacingMergeTree
MERGE INTO dim_customers AS t
USING (
    SELECT * FROM dim_customers_staging
) AS s
ON t.customer_id = s.customer_id
WHEN MATCHED AND t.updated_at < s.updated_at THEN
  UPDATE SET
    t.name        = s.name,
    t.email       = s.email,
    t.updated_at  = s.updated_at
WHEN NOT MATCHED THEN
  INSERT (customer_id, name, email, updated_at)
  VALUES (s.customer_id, s.name, s.email, s.updated_at);

Partition Overwrite Instead of Append

Overwriting an entire partition on each run is idempotent; appending blindly creates duplicates on retry.

# PySpark — dynamic partition overwrite (safe re-run)
spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")

df.write \
    .format("parquet") \
    .mode("overwrite") \
    .partitionBy("event_date") \
    .save("s3://datalake/events/")

-- Spark SQL equivalent
INSERT OVERWRITE TABLE events PARTITION (event_date = '2024-06-01')
SELECT * FROM events_staging WHERE event_date = '2024-06-01';

Deterministic Job IDs

Generate job run IDs deterministically from business keys so re-runs produce the same ID and idempotency guards in downstream systems work correctly.

import hashlib

def make_run_id(dag_id: str, logical_date: str) -> str:
    """Stable run ID — same inputs always produce the same ID."""
    key = f"{dag_id}::{logical_date}"
    return hashlib.sha256(key.encode()).hexdigest()[:16]

run_id = make_run_id("orders_etl", "2024-06-01")
# Insert guard: skip if run_id already recorded in audit table

Checkpointing for Streaming Jobs

# PySpark Structured Streaming — checkpoint enables exactly-once restart
query = (
    df.writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", "s3://checkpoints/orders_stream/")
    .trigger(processingTime="1 minute")
    .start("s3://datalake/orders_stream/")
)

2. Retry Strategies

Exponential Backoff with Jitter (Python)

Jitter prevents thundering-herd when many workers retry simultaneously after a shared outage.

import time
import random
import logging
from functools import wraps

logger = logging.getLogger(__name__)


class TransientError(Exception):
    """Retry this — network blip, lock timeout, rate limit."""

class PermanentError(Exception):
    """Do NOT retry — bad data, auth failure, schema mismatch."""


def retry_with_backoff(
    max_attempts: int = 5,
    base_delay: float = 1.0,
    max_delay: float = 60.0,
    jitter: bool = True,
    retryable: type = TransientError,
):
    """Decorator: exponential backoff with full jitter."""
    def decorator(fn):
        @wraps(fn)
        def wrapper(*args, **kwargs):
            for attempt in range(1, max_attempts + 1):
                try:
                    return fn(*args, **kwargs)
                except PermanentError:
                    raise  # never retry permanent errors
                except retryable as exc:
                    if attempt == max_attempts:
                        logger.error(
                            "max_retries_exceeded",
                            extra={"function": fn.__name__, "attempt": attempt, "error": str(exc)},
                        )
                        raise
                    delay = min(base_delay * (2 ** (attempt - 1)), max_delay)
                    if jitter:
                        delay = random.uniform(0, delay)  # full jitter
                    logger.warning(
                        "retrying",
                        extra={"function": fn.__name__, "attempt": attempt, "delay_s": round(delay, 2)},
                    )
                    time.sleep(delay)
        return wrapper
    return decorator


# Usage
@retry_with_backoff(max_attempts=5, base_delay=2.0, retryable=TransientError)
def fetch_api_data(url: str) -> dict:
    import requests
    resp = requests.get(url, timeout=10)
    if resp.status_code == 429:
        raise TransientError(f"Rate limited: {resp.status_code}")
    if resp.status_code == 401:
        raise PermanentError(f"Auth failure: {resp.status_code}")
    resp.raise_for_status()
    return resp.json()

Classify Transient vs Permanent Errors

Airflow Retry Configuration

from datetime import timedelta
import pendulum
from airflow.sdk import dag, task

@dag(
    dag_id="orders_etl",
    start_date=pendulum.datetime(2024, 1, 1, tz="UTC"),
    schedule="@daily",
    catchup=False,
    default_args={
        "owner": "data-eng",
        "retries": 3,
        "retry_delay": timedelta(minutes=5),
        "retry_exponential_backoff": True,   # 5m, 10m, 20m
        "max_retry_delay": timedelta(minutes=60),
        "execution_timeout": timedelta(hours=2),
        "sla": timedelta(hours=4),           # triggers sla_miss_callback
    },
)
def orders_etl():
    ...

Spark Stage Retry

# spark-defaults.conf or SparkSession config
spark = (
    SparkSession.builder
    .config("spark.task.maxFailures", "4")          # default 4 — retries per task
    .config("spark.stage.maxConsecutiveAttempts", "4")
    .config("spark.network.timeout", "300s")
    .config("spark.sql.broadcastTimeout", "300")
    .getOrCreate()
)

3. Alerting and SLA Monitoring

Airflow SLA Miss Callback

The sla parameter is measured from DAG start time. Callbacks fire after the scheduler's next heartbeat past the deadline.

import pendulum
from airflow.sdk import dag, task
from airflow.utils.email import send_email


def handle_sla_miss(dag, task_list, blocking_task_list, slas, blocking_tis):
    """Called by Airflow when any task in dag misses its SLA."""
    missed = ", ".join(t.task_id for t in task_list)
    message = (
        f"SLA missed in DAG `{dag.dag_id}`\n"
        f"Tasks: {missed}\n"
        f"Run at: {pendulum.now('UTC').isoformat()}"
    )
    # Slack
    _post_slack(message)
    # Email
    send_email(
        to=["data-oncall@company.com"],
        subject=f"[AIRFLOW SLA] {dag.dag_id}",
        html_content=f"<pre>{message}</pre>",
    )


def _post_slack(text: str):
    import requests, os
    requests.post(os.environ["SLACK_WEBHOOK_URL"], json={"text": text}, timeout=5)


@dag(
    dag_id="orders_etl",
    start_date=pendulum.datetime(2024, 1, 1, tz="UTC"),
    schedule="@daily",
    sla_miss_callback=handle_sla_miss,
    default_args={"sla": pendulum.duration(hours=4)},
)
def orders_etl():
    ...

Prometheus Alerts — Pipeline Latency (PromQL)

Expose a pipeline_last_success_timestamp_seconds gauge from each job (or via Pushgateway), then alert on staleness.

# prometheus/alerts/pipelines.yml
groups:
  - name: data_pipelines
    rules:
      # Alert when pipeline has not succeeded for more than 4 hours
      - alert: PipelineSLAMiss
        expr: |
          (time() - pipeline_last_success_timestamp_seconds{job="orders_etl"}) > 14400
        for: 5m
        labels:
          severity: critical
          team: data-eng
        annotations:
          summary: "Pipeline {{ $labels.job }} has not completed in 4h"
          runbook: "https://wiki.company.com/runbooks/orders_etl"

      # Alert on high error rate
      - alert: PipelineHighErrorRate
        expr: |
          rate(pipeline_records_failed_total[5m])
          / (rate(pipeline_records_processed_total[5m]) + 1) > 0.05
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Error rate > 5% in {{ $labels.job }}"

      # Alert on consumer lag (Kafka)
      - alert: KafkaConsumerLagHigh
        expr: kafka_consumer_lag_sum{consumer_group="orders-cg"} > 100000
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Consumer lag {{ $value }} for {{ $labels.consumer_group }}"

# Push metrics from Python job to Prometheus Pushgateway
from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
import time

def push_pipeline_metrics(job_name: str, records: int, errors: int):
    registry = CollectorRegistry()

    Gauge("pipeline_last_success_timestamp_seconds",
          "Epoch of last successful run", registry=registry).set(time.time())
    Gauge("pipeline_records_processed_total",
          "Records processed", registry=registry).set(records)
    Gauge("pipeline_records_failed_total",
          "Records failed", registry=registry).set(errors)

    push_to_gateway("pushgateway:9091", job=job_name, registry=registry)

PagerDuty Webhook Integration

import requests, os

def page_oncall(summary: str, severity: str = "critical", dag_id: str = ""):
    """Fire PagerDuty Events API v2 alert."""
    payload = {
        "routing_key": os.environ["PD_ROUTING_KEY"],
        "event_action": "trigger",
        "payload": {
            "summary": summary,
            "severity": severity,  # critical | error | warning | info
            "source": f"airflow:{dag_id}",
            "custom_details": {"dag_id": dag_id},
        },
    }
    resp = requests.post(
        "https://events.pagerduty.com/v2/enqueue",
        json=payload,
        timeout=5,
    )
    resp.raise_for_status()

Dead-Letter Queue Alerting

# Kafka DLQ consumer — alert if DLQ depth crosses threshold
from confluent_kafka import Consumer
from prometheus_client import Gauge

dlq_depth_gauge = Gauge("dlq_depth", "Messages in dead-letter queue", ["topic"])

def monitor_dlq(dlq_topic: str, bootstrap_servers: str):
    consumer = Consumer({
        "bootstrap.servers": bootstrap_servers,
        "group.id": "dlq-monitor",
        "auto.offset.reset": "earliest",
        "enable.auto.commit": False,
    })
    consumer.subscribe([dlq_topic])
    depth = 0
    while True:
        msg = consumer.poll(timeout=1.0)
        if msg is None:
            break
        if not msg.error():
            depth += 1
    dlq_depth_gauge.labels(topic=dlq_topic).set(depth)
    if depth > 100:
        page_oncall(f"DLQ {dlq_topic} has {depth} unprocessed messages", severity="error")
    consumer.close()

4. Data Freshness SLAs

Query-Based Freshness Check (SQL)

-- Check that the orders table has been loaded within the last 2 hours
SELECT
    MAX(event_time)                                        AS latest_event,
    NOW() - MAX(event_time)                                AS lag,
    CASE
        WHEN NOW() - MAX(event_time) > INTERVAL '2 hours'
        THEN 'STALE'
        ELSE 'FRESH'
    END                                                    AS freshness_status
FROM orders;

Soda Core Freshness Check

# checks/orders_freshness.yml
checks for orders:
  - freshness(event_time) < 2h:
      name: orders_loaded_within_2h
      fail:
        when freshness > 2h
      warn:
        when freshness > 1h
  - freshness(event_time) < 24h:
      name: orders_not_older_than_1_day

# Run freshness scan from Airflow
from airflow.sdk import task
from soda.scan import Scan

@task
def check_orders_freshness():
    scan = Scan()
    scan.set_data_source_name("trino_prod")
    scan.add_configuration_yaml_file("/opt/soda/configuration.yml")
    scan.add_sodacl_yaml_file("/opt/soda/checks/orders_freshness.yml")
    scan.set_scan_definition_name("orders_freshness")
    exit_code = scan.execute()
    if exit_code not in (0, 1):   # 0=pass, 1=warn; 2+=fail
        raise ValueError(f"Freshness check failed with exit code {exit_code}")
    return scan.get_scan_results()

Automated Freshness Monitoring Dashboard

# Expose freshness lag as a Prometheus metric for Grafana dashboard
import time
from prometheus_client import Gauge
import trino

freshness_lag_gauge = Gauge(
    "table_freshness_lag_seconds",
    "Seconds since last row was written",
    ["schema", "table"],
)

def update_freshness_metrics(tables: list[dict]):
    conn = trino.dbapi.connect(host="trino", port=8080, user="monitor")
    cursor = conn.cursor()
    for t in tables:
        cursor.execute(
            f"SELECT EXTRACT(EPOCH FROM (NOW() - MAX({t['ts_col']})))"
            f" FROM {t['schema']}.{t['table']}"
        )
        lag_s = cursor.fetchone()[0] or 0
        freshness_lag_gauge.labels(
            schema=t["schema"], table=t["table"]
        ).set(lag_s)

5. Observability

Structured Logging (JSON)

Every log line must carry correlation fields so logs from distributed workers can be joined.

import logging
import json
import sys
from datetime import datetime, timezone


class JsonFormatter(logging.Formatter):
    def format(self, record: logging.LogRecord) -> str:
        log = {
            "ts": datetime.now(timezone.utc).isoformat(),
            "level": record.levelname,
            "logger": record.name,
            "message": record.getMessage(),
        }
        # Merge extra fields passed by the caller
        for key in ("job_id", "run_id", "table", "partition", "row_count", "error"):
            if hasattr(record, key):
                log[key] = getattr(record, key)
        if record.exc_info:
            log["exception"] = self.formatException(record.exc_info)
        return json.dumps(log, ensure_ascii=False)


def get_logger(name: str) -> logging.Logger:
    logger = logging.getLogger(name)
    if not logger.handlers:
        handler = logging.StreamHandler(sys.stdout)
        handler.setFormatter(JsonFormatter())
        logger.addHandler(handler)
    logger.setLevel(logging.INFO)
    return logger


# Usage
logger = get_logger("orders_etl")

logger.info(
    "partition_loaded",
    extra={
        "job_id": "orders_etl",
        "run_id": "2024-06-01",
        "table": "orders",
        "partition": "2024-06-01",
        "row_count": 142_503,
    },
)

Sample output:

{
  "ts": "2024-06-01T06:05:12.003Z",
  "level": "INFO",
  "logger": "orders_etl",
  "message": "partition_loaded",
  "job_id": "orders_etl",
  "run_id": "2024-06-01",
  "table": "orders",
  "partition": "2024-06-01",
  "row_count": 142503
}

OpenTelemetry for Airflow and Spark

# requirements: opentelemetry-sdk opentelemetry-exporter-otlp
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Bootstrap once per process (call from DAG or job entrypoint)
def setup_tracing(service_name: str, otlp_endpoint: str = "http://otel-collector:4317"):
    provider = TracerProvider()
    exporter = OTLPSpanExporter(endpoint=otlp_endpoint, insecure=True)
    provider.add_span_processor(BatchSpanProcessor(exporter))
    trace.set_tracer_provider(provider)

tracer = trace.get_tracer("orders_etl")

# Wrap Airflow tasks as spans
def load_partition(run_id: str, partition: str):
    with tracer.start_as_current_span("load_partition") as span:
        span.set_attribute("run_id", run_id)
        span.set_attribute("partition", partition)
        span.set_attribute("table", "orders")
        try:
            rows = _do_load(partition)
            span.set_attribute("row_count", rows)
        except Exception as exc:
            span.record_exception(exc)
            span.set_status(trace.StatusCode.ERROR, str(exc))
            raise

# Airflow 3.x — enable built-in OTEL export via airflow.cfg
# [metrics]
# otel_on = True
# otel_host = otel-collector
# otel_port = 4318
# otel_prefix = airflow

Key Metrics to Expose

6. Data Reconciliation

Row Count Check — Upstream vs Downstream

import logging

logger = logging.getLogger("reconciliation")

def reconcile_row_counts(
    upstream_conn,
    downstream_conn,
    upstream_query: str,
    downstream_query: str,
    tolerance_pct: float = 0.01,  # 1%
) -> dict:
    with upstream_conn.cursor() as cur:
        cur.execute(upstream_query)
        upstream_count = cur.fetchone()[0]

    with downstream_conn.cursor() as cur:
        cur.execute(downstream_query)
        downstream_count = cur.fetchone()[0]

    diff = abs(upstream_count - downstream_count)
    diff_pct = diff / max(upstream_count, 1)
    passed = diff_pct <= tolerance_pct

    result = {
        "upstream_count": upstream_count,
        "downstream_count": downstream_count,
        "diff": diff,
        "diff_pct": round(diff_pct, 6),
        "passed": passed,
    }
    level = logging.INFO if passed else logging.ERROR
    logger.log(level, "row_count_reconciliation", extra=result)
    if not passed:
        raise AssertionError(
            f"Row count mismatch: upstream={upstream_count}, "
            f"downstream={downstream_count}, diff_pct={diff_pct:.2%}"
        )
    return result

Checksum Comparison

-- Upstream (source DB)
SELECT
    COUNT(*)                                        AS row_count,
    SUM(amount)                                     AS amount_sum,
    COUNT(DISTINCT order_id)                        AS distinct_orders,
    MD5(STRING_AGG(order_id::text ORDER BY order_id)) AS order_id_hash
FROM orders
WHERE order_date = '2024-06-01';

-- Downstream (data warehouse) — run same query, compare results
SELECT
    COUNT(*)                                        AS row_count,
    SUM(amount)                                     AS amount_sum,
    COUNT(DISTINCT order_id)                        AS distinct_orders,
    MD5(STRING_AGG(order_id::text ORDER BY order_id)) AS order_id_hash
FROM dw.fact_orders
WHERE order_date = '2024-06-01';

Duplicate Detection

-- Detect duplicates on natural key
SELECT order_id, COUNT(*) AS cnt
FROM orders
GROUP BY order_id
HAVING COUNT(*) > 1
ORDER BY cnt DESC
LIMIT 20;

# PySpark duplicate detection
from pyspark.sql import functions as F

def detect_duplicates(df, key_cols: list[str], label: str = "") -> int:
    dup_count = (
        df.groupBy(key_cols)
          .agg(F.count("*").alias("cnt"))
          .filter(F.col("cnt") > 1)
          .agg(F.sum("cnt").alias("total_dupes"))
          .collect()[0]["total_dupes"] or 0
    )
    if dup_count > 0:
        logger.error(
            "duplicates_detected",
            extra={"label": label, "key_cols": key_cols, "duplicate_rows": dup_count},
        )
    return dup_count

Automated Reconciliation Report (Airflow Task)

from airflow.sdk import task
import pandas as pd

@task
def reconciliation_report(run_date: str) -> dict:
    checks = [
        {
            "name": "orders_row_count",
            "upstream_sql": f"SELECT COUNT(*) FROM source.orders WHERE order_date='{run_date}'",
            "downstream_sql": f"SELECT COUNT(*) FROM dw.fact_orders WHERE order_date='{run_date}'",
        },
        {
            "name": "orders_amount_sum",
            "upstream_sql": f"SELECT ROUND(SUM(amount),2) FROM source.orders WHERE order_date='{run_date}'",
            "downstream_sql": f"SELECT ROUND(SUM(amount),2) FROM dw.fact_orders WHERE order_date='{run_date}'",
        },
    ]
    rows = []
    for check in checks:
        upstream_val = _query_scalar(SOURCE_CONN, check["upstream_sql"])
        downstream_val = _query_scalar(DW_CONN, check["downstream_sql"])
        passed = abs(upstream_val - downstream_val) / max(abs(upstream_val), 1) <= 0.001
        rows.append({"check": check["name"], "upstream": upstream_val,
                      "downstream": downstream_val, "passed": passed})

    report = pd.DataFrame(rows)
    failures = report[~report["passed"]]
    if len(failures):
        _post_slack(f"Reconciliation FAILED for {run_date}:\n{failures.to_string()}")
        raise ValueError(f"{len(failures)} reconciliation checks failed")
    return report.to_dict()

7. Graceful Degradation

Circuit Breaker Pattern

import time
import threading
from enum import Enum

class State(Enum):
    CLOSED = "closed"         # normal — requests pass through
    OPEN = "open"             # tripped — requests fail-fast with fallback
    HALF_OPEN = "half_open"   # probing — one test request allowed


class CircuitBreaker:
    def __init__(
        self,
        failure_threshold: int = 5,
        recovery_timeout: float = 60.0,
        success_threshold: int = 2,
    ):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.success_threshold = success_threshold
        self._state = State.CLOSED
        self._failures = 0
        self._successes = 0
        self._opened_at: float = 0.0
        self._lock = threading.Lock()

    @property
    def state(self) -> State:
        with self._lock:
            if self._state == State.OPEN:
                if time.monotonic() - self._opened_at >= self.recovery_timeout:
                    self._state = State.HALF_OPEN
            return self._state

    def call(self, fn, *args, fallback=None, **kwargs):
        st = self.state
        if st == State.OPEN:
            return fallback() if callable(fallback) else fallback

        try:
            result = fn(*args, **kwargs)
            with self._lock:
                self._failures = 0
                if st == State.HALF_OPEN:
                    self._successes += 1
                    if self._successes >= self.success_threshold:
                        self._state = State.CLOSED
                        self._successes = 0
            return result
        except Exception as exc:
            with self._lock:
                self._failures += 1
                self._successes = 0
                if self._failures >= self.failure_threshold:
                    self._state = State.OPEN
                    self._opened_at = time.monotonic()
                    logger.error("circuit_breaker_opened", extra={"error": str(exc)})
            raise


# Usage — wrap external API call
enrichment_cb = CircuitBreaker(failure_threshold=5, recovery_timeout=120.0)

def enrich_record(record: dict) -> dict:
    def fetch():
        return requests.get(f"https://api.enrichment.io/v1/{record['id']}", timeout=3).json()

    def stale_fallback():
        # Return last known-good data from cache
        return cache.get(record["id"]) or {}

    return enrichment_cb.call(fetch, fallback=stale_fallback)

Fallback to Cached / Stale Data

import diskcache

cache = diskcache.Cache("/tmp/pipeline_cache")

def get_reference_data(key: str, max_age_s: int = 3600) -> dict:
    """Return fresh data if API is up; stale cached data if it is down."""
    try:
        data = fetch_api_data(key)
        cache.set(key, data, expire=max_age_s * 24)  # cache for 24x SLA
        return data
    except TransientError as exc:
        cached = cache.get(key)
        if cached:
            logger.warning("using_stale_cache", extra={"key": key, "reason": str(exc)})
            return cached
        raise  # no cache — propagate error

Partial Pipeline Execution — Skip Failed Partitions

from airflow.sdk import task
import pendulum

@task
def load_partitions(partitions: list[str]) -> dict:
    results = {"success": [], "failed": []}
    for partition in partitions:
        try:
            _load_partition(partition)
            results["success"].append(partition)
        except PermanentError as exc:
            logger.error(
                "partition_skipped",
                extra={"partition": partition, "reason": str(exc)},
            )
            results["failed"].append(partition)
            # Continue with remaining partitions; alert at the end
    if results["failed"]:
        _post_slack(
            f"WARNING: {len(results['failed'])} partitions skipped: "
            f"{results['failed']}"
        )
    return results

8. Change Management

Blue/Green Table Swap

Write to a staging table (_new suffix), validate, then atomically rename. Production reads are never interrupted.

-- Step 1: create new version of the table (outside a transaction)
CREATE TABLE dim_products_new AS SELECT * FROM dim_products WHERE 1=0;

-- Step 2: load data into the new table
INSERT INTO dim_products_new SELECT ... FROM source;

-- Step 3: validate
SELECT COUNT(*) FROM dim_products_new;   -- must match expected row count

-- Step 4: atomic swap (one DDL statement — no window where table is missing)
ALTER TABLE dim_products RENAME TO dim_products_old;
ALTER TABLE dim_products_new RENAME TO dim_products;

-- Step 5: keep _old for 24h rollback window, then drop
-- DROP TABLE dim_products_old;

# Python helper for blue/green swap with validation gate
def blue_green_swap(
    conn,
    table: str,
    new_table: str,
    min_row_count: int,
):
    with conn.cursor() as cur:
        cur.execute(f"SELECT COUNT(*) FROM {new_table}")
        count = cur.fetchone()[0]
        if count < min_row_count:
            raise ValueError(
                f"Validation failed: {new_table} has {count} rows, "
                f"expected >= {min_row_count}"
            )
        old_table = f"{table}_old"
        # Atomic swap — both renames in a single transaction
        cur.execute(f"ALTER TABLE {table} RENAME TO {old_table}")
        cur.execute(f"ALTER TABLE {new_table} RENAME TO {table}")
    logger.info("blue_green_swap_complete",
                extra={"table": table, "row_count": count})

Schema Migration Checklist

Run migrations in the expand-contract pattern — never drop columns in the same deploy that removes code that reads them.

Phase 1 — Expand (backward-compatible, run before code deploy)

• Add new nullable columns with ALTER TABLE ... ADD COLUMN ... DEFAULT NULL
• Add new tables, indexes, or views
• Deploy code that writes to BOTH old and new columns/tables

Phase 2 — Migrate (backfill data if needed)

• Backfill new column from old: UPDATE table SET new_col = old_col WHERE new_col IS NULL
• Verify row counts and checksums post-backfill

Phase 3 — Contract (cleanup, run only after Phase 2 is verified in production)

• Remove old code paths
• Drop deprecated columns: ALTER TABLE ... DROP COLUMN ...
• Drop deprecated tables

Backward-Compatible Schema Changes

Rollback Procedure

-- Rollback a blue/green swap within the 24h window
ALTER TABLE dim_products RENAME TO dim_products_broken;
ALTER TABLE dim_products_old RENAME TO dim_products;

-- Rollback a schema migration (contract phase not yet applied)
-- Simply redeploy previous application version — old column still exists

-- Rollback a partition overwrite (if Delta Lake / Iceberg)
-- Delta Lake: RESTORE TABLE events TO VERSION AS OF 42;
-- Iceberg: CALL catalog.system.rollback_to_snapshot('schema.table', <snapshot_id>);

# Airflow rollback task — run manually or via TriggerDagRunOperator
from airflow.sdk import dag, task
import pendulum

@dag(dag_id="dim_products_rollback", schedule=None,
     start_date=pendulum.datetime(2024, 1, 1, tz="UTC"))
def dim_products_rollback():

    @task
    def swap_back():
        # Assumes _old table exists within rollback window
        _execute_sql("""
            ALTER TABLE dim_products RENAME TO dim_products_broken;
            ALTER TABLE dim_products_old  RENAME TO dim_products;
        """)
        logger.info("rollback_complete", extra={"table": "dim_products"})

    swap_back()

dim_products_rollback()

Anti-Patterns

References to Consult When Needed

• docs/specs/ — Vertica, Trino+Iceberg, Spark SQL specs for engine-specific DDL/DML syntax
• skills/airflow_dags/SKILL.md — Full Airflow operator/sensor/callback reference
• skills/pyspark_streaming/SKILL.md — Checkpointing, watermarks, exactly-once in Structured Streaming
• skills/soda_core/SKILL.md — Full SodaCL check syntax, scan API, Airflow gate integration
• skills/great_expectations/SKILL.md — Expectation suites, Checkpoint actions, Slack integration
• skills/openlineage/SKILL.md — Run/Job/Dataset lineage events, Marquez, column-level lineage
• skills/delta_lake/SKILL.md — RESTORE, Change Data Feed, OPTIMIZE, Time Travel syntax
• skills/apache_kafka/SKILL.md — DLQ config, consumer lag monitoring, exactly-once semantics
• Data Engineering Design Patterns — Idempotency (O'Reilly)
• Airflow SLA and Retries — Orchestra Guide
• OpenTelemetry for Data Pipelines — bix-tech.com
• Circuit Breaker Pattern — Azure Architecture Center
• Soda Data Reconciliation — docs.soda.io