We build with Apache Airflow
We use Apache Airflow to orchestrate complex data pipelines, scheduled jobs, and cross-system workflows that support your business goals. From DAG design and task dependencies to monitoring and failure recovery, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTDAG-Based Orchestration
We define workflows as code with clear task dependencies.
Built-In Observability
We monitor DAG runs, logs, and retries from one UI.
Flexible Executors
We scale task execution across workers as pipelines grow.
BENEFITS OF APACHE AIRFLOW technology
We use Apache Airflow to orchestrate complex data pipelines, automate ETL workflows, and monitor task execution reliably.
BUILD
[01]- Design DAG workflows
- Define task dependencies
- Configure executors
- Set retry policies
ENGAGE
[02]- Orchestrate ETL pipelines
- Schedule recurring jobs
- Monitor task execution
- Trigger cross-system syncs
GROW
[03]- Scale across workers
- Extend with custom operators
- Add pipeline observability
- Integrate new data sources
Our Apache Airflow Technology Stack
We combine Apache Airflow with data warehouses like Snowflake and BigQuery, object stores, and orchestration-adjacent tools like dbt for scheduling, monitoring, and reliable execution across your ETL and ELT pipelines.
Custom Apache Airflow development company
With our Apache Airflow development services, we build reliable data pipeline orchestration for ETL/ELT workflows, machine learning training schedules, and cross-system data synchronization, tailored to your boldest business goals. Having years of experience with Airflow, our engineers harness its full potential to deliver observable, maintainable pipelines across industries and company sizes. Whether it's designing new DAGs from scratch or modernizing legacy batch jobs into scheduled workflows, we build orchestration that scales with your data volume. Our range of Apache Airflow development services spans consulting, DAG design, development, and ongoing pipeline maintenance. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep data engineering expertise and a pragmatic approach to pipeline architecture and problem-solving. All this to make sure your data pipelines stay reliable and observable as complexity grows, and moves your business forward.
OUR APACHE AIRFLOW SERVICES
We build, modernize, and support Apache Airflow pipelines around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Custom DAGs, task dependencies, and scheduling logic built around your data pipeline requirements.
ETL and ELT workflows connecting your data warehouses, object stores, and transformation tools.
Observability into DAG runs, task retries, and failures across your production pipelines.
Migrate legacy cron jobs and batch scripts into scheduled, observable Airflow workflows.
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Apache Airflow and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Apache Airflow?
We use Apache Airflow to orchestrate ETL/ELT pipelines, schedule recurring data jobs, and monitor workflow execution. We design DAGs and select executors around your data volume and reliability requirements.
What types of pipelines do you build with Airflow?
We build ETL and ELT pipelines, machine learning training schedules, and cross-system data synchronization jobs that move and transform data reliably between your source systems and warehouses.
Can Airflow integrate with our existing data stack?
Yes. Airflow connects natively with data warehouses like Snowflake, BigQuery, and Redshift, object stores like S3 and GCS, and transformation tools like dbt through its library of operators and hooks.
Do you work with Celery or Kubernetes executors?
Yes. We configure the executor that fits your scale, from Celery for distributed task queues to Kubernetes for containerized, isolated task execution.
How do you handle pipeline failures and retries?
We configure retry policies, alerting, and logging within Airflow's UI so failed tasks are caught early and pipeline history stays visible for debugging.
Can you modernize our existing batch jobs with Airflow?
Yes. We migrate legacy cron jobs and manual scripts into version-controlled DAGs, giving you scheduling, dependency management, and observability you didn't have before.
How do you decide whether Airflow fits a project?
We look at your data volume, pipeline complexity, existing infrastructure, and team experience, then confirm Airflow is the right fit before starting development.
