Turnkey Apache Airflow Workflow Engine Development

Your data volume is growing, but manual script execution and cron jobs can no longer keep up? We've encountered situations where N+1 queries in an ETL pipeline caused hours of delays, with no monitoring in place. A pipeline loading 5 million orders on LocalExecutor took 4 hours, and during peak loads, downtime reached 30 minutes. We solve this problem with Apache Airflow—a mature platform for orchestrating and automating data processing workflows. Our engineers have built dozens of DAG pipelines for companies in e-commerce, fintech, and logistics. Each pipeline undergoes load testing: a typical DAG processes up to 10 million records overnight, meeting the SLA with 99.9% execution time. Stability is confirmed by 50+ projects. Get a consultation for your project—we'll evaluate it in 1 day.

Airflow vs. Temporal and Camunda

Airflow is optimized for batch data processing:

• ETL/ELT pipelines (PostgreSQL → transformation → Data Warehouse)
• Daily reports and exports
• ML pipelines (data preparation → training → model deployment)
• Periodic aggregations and synchronizations

If you have event-driven business processes with human tasks, consider Temporal or Camunda. Airflow cannot wait for users for hours. But for data engineers, it's the best choice: it is 3–5 times faster than Temporal in batch scenarios.

Why We Choose KubernetesExecutor

KubernetesExecutor provides isolation at the task level: each task runs in its own Pod with dedicated CPU and memory. Under peak load, Kubernetes automatically spins up Pods and scales down afterward. We use this approach in production and consider it the standard for modern data pipelines.

How KubernetesExecutor Speeds Up Data Processing

Under peak load, KubernetesExecutor scales resources horizontally: instead of one node, a cluster of 10+ Pods is used. ETL pipeline execution time is reduced by 3–5 times compared to LocalExecutor. For example, a pipeline loading 5 million orders on LocalExecutor takes 4 hours; on KubernetesExecutor, it takes 50 minutes. The difference is clear.

How We Build an ETL Pipeline: Detailed Case Study

Consider an example for an online store: daily loading of orders from PostgreSQL into a DWH (PostgreSQL) with transformation and aggregation. Below is a DAG we deployed for a customer in 2 days.

Installation via Helm

helm repo add apache-airflow https://airflow.apache.org
helm upgrade --install airflow apache-airflow/airflow \
  --namespace airflow \
  --create-namespace \
  --set executor=KubernetesExecutor \
  --set postgresql.enabled=true \
  --set redis.enabled=true \
  --values airflow-values.yaml

# airflow-values.yaml
aiflow:
  image:
    repository: apache/airflow
    tag: 2.8.0
  config:
    AIRFLOW__CORE__DAGS_FOLDER: /opt/airflow/dags
    AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG: "3"
    AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL: "30"

dags:
  gitSync:
    enabled: true
    repo: https://github.com/your-org/airflow-dags.git
    branch: main
    subPath: dags/

DAG — Example ETL Pipeline

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.postgres.operators.postgres import PostgresOperator
from airflow.providers.postgres.hooks.postgres import PostgresHook
from datetime import datetime, timedelta
import pandas as pd

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
    'email_on_failure': True,
    'email': ['[email protected]'],
}

with DAG(
    'daily_orders_etl',
    default_args=default_args,
    schedule_interval='0 2 * * *',
    catchup=False,
    tags=['etl', 'orders'],
    description='Load and transform orders into DWH',
) as dag:

    def extract_orders(**context):
        hook = PostgresHook(postgres_conn_id='production_db')
        ds = context['ds']
        df = hook.get_pandas_df(f"""
            SELECT o.id, o.customer_id, o.total, o.status,
                   o.created_at, c.email, c.country
            FROM orders o
            JOIN customers c ON c.id = o.customer_id
            WHERE o.created_at::date = '{ds}'
              AND o.status IN ('paid', 'shipped', 'delivered')
        """)
        context['ti'].xcom_push(key='orders_count', value=len(df))
        df.to_parquet(f'/tmp/orders_{ds}.parquet')
        return len(df)

    def transform_orders(**context):
        ds = context['ds']
        df = pd.read_parquet(f'/tmp/orders_{ds}.parquet')
        df['order_date'] = pd.to_datetime(df['created_at']).dt.date
        df['revenue_usd'] = df['total'] / 100
        df['is_international'] = df['country'] != 'RU'
        df['customer_tier'] = df['revenue_usd'].apply(
            lambda x: 'vip' if x >= 500 else 'regular'
        )
        df.to_parquet(f'/tmp/orders_transformed_{ds}.parquet')

    def load_to_dwh(**context):
        ds = context['ds']
        df = pd.read_parquet(f'/tmp/orders_transformed_{ds}.parquet')
        hook = PostgresHook(postgres_conn_id='datawarehouse')
        engine = hook.get_sqlalchemy_engine()
        df.to_sql('fact_orders', engine, schema='dwh',
                  if_exists='append', index=False,
                  method='multi', chunksize=1000)

    aggregate_metrics = PostgresOperator(
        task_id='aggregate_metrics',
        postgres_conn_id='datawarehouse',
        sql="""
        INSERT INTO dwh.daily_metrics (date, total_revenue, orders_count, avg_order)
        SELECT
          '{{ ds }}'::date,
          SUM(revenue_usd),
          COUNT(*),
          AVG(revenue_usd)
        FROM dwh.fact_orders
        WHERE order_date = '{{ ds }}'
        ON CONFLICT (date) DO UPDATE SET
          total_revenue = EXCLUDED.total_revenue,
          orders_count = EXCLUDED.orders_count,
          avg_order = EXCLUDED.avg_order;
        """,
    )

    extract = PythonOperator(task_id='extract_orders', python_callable=extract_orders)
    transform = PythonOperator(task_id='transform_orders', python_callable=transform_orders)
    load = PythonOperator(task_id='load_to_dwh', python_callable=load_to_dwh)

    extract >> transform >> load >> aggregate_metrics

Parallel Execution and Sensors

from airflow.utils.task_group import TaskGroup

with TaskGroup('process_regions') as process_regions:
    for region in ['EU', 'US', 'APAC']:
        PythonOperator(
            task_id=f'process_{region.lower()}',
            python_callable=process_region_data,
            op_kwargs={'region': region}
        )

extract >> process_regions >> aggregate_all

To wait for external events, we use Sensors: FileSensor for files, HttpSensor for APIs. This is a standard production pattern.

KubernetesExecutor in Action

executor_config = {
    'KubernetesExecutor': {
        'request_memory': '2Gi',
        'request_cpu': '500m',
        'limit_memory': '4Gi',
        'image': 'custom-airflow:2.8.0-pandas',
    }
}

heavy_transform = PythonOperator(
    task_id='heavy_transform',
    python_callable=transform_large_dataset,
    executor_config=executor_config
)

How DAG Failures Are Handled

When a task fails, Airflow automatically retries with exponential backoff. We configure alerts in Telegram/Slack for every failure and long-running task. Additionally, we integrate metrics into Prometheus/Grafana: dashboards show execution time, number of successful/failed tasks, and resource utilization. This enables rapid incident response.

Minimum infrastructure requirements:

• Kubernetes cluster version 1.24+
• PostgreSQL 13+ for metadata database
• Redis (optional, for CeleryExecutor)
• Storage capacity: from 100 GB for logs and artifacts

Process Overview

1. Analysis: We study your data sources, volumes, SLA, current issues, Airflow configuration, and infrastructure.
2. Design: We outline DAGs, choose an Executor, plan error handling and alerts.
3. Implementation: We write DAG code, Helm configs, CI/CD, and tests.
4. Deployment: We set up infrastructure (Kubernetes or Docker), configure GitSync and monitoring.
5. Testing: We run backfill on historical data and verify correctness.
6. Documentation and Training: We deliver a runbook and train your team.
7. Support: 2 weeks post-launch for stabilization.

What's Included in the Work

• Developed DAGs (Python code ready for production)
• Airflow configuration (values.yaml, variables, connections)
• CI/CD pipeline for automatic DAG deployment via Git
• Docker image with dependencies (libraries, drivers)
• Documentation: DAG descriptions, run instructions, troubleshooting
• Team training (2–3 sessions)
• Monitoring: alerts in Telegram/Slack, Grafana dashboards

Timeline Estimates

Our Experience and Guarantees

We have 10+ years of experience in developing data infrastructure. We have delivered 50+ Airflow projects for e-commerce, fintech, and logistics. We use best practices: GitSync, KubernetesExecutor, alerting, DAG versioning. We guarantee stable pipeline operation and enterprise-level documentation.

We are ready to build a similar pipeline for your data. Contact us for an evaluation—we'll respond within 1 day. Or request a consultation to discuss details without obligation.

Apache Airflow Documentation

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.