airflow

What is Kubernetes vs Airflow? Understanding Two Complementary Technologies

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When you’re building modern data infrastructure or deploying applications at scale, you’ll inevitably encounter both Kubernetes and Apache Airflow. These technologies often appear together in architecture diagrams and job postings, leading to confusion about their relationship. Are they competitors? Alternatives? Complementary tools? The answer is that Kubernetes and Airflow serve fundamentally different purposes—Kubernetes is a … Read more

Orchestrating Machine Learning Training Jobs with Airflow and Kubernetes

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When you’re moving machine learning models from experimental Jupyter notebooks to production-grade training pipelines, you need robust orchestration that handles complexity, scales with your computational needs, and provides visibility into every step of the process. Apache Airflow combined with Kubernetes offers a powerful solution for orchestrating ML training jobs—Airflow provides workflow management and scheduling, while … Read more

ML Model Monitoring for Data Drift in Airflow Pipelines

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Machine learning models in production face a silent threat that gradually degrades their performance: data drift. Unlike software bugs that announce themselves through errors and crashes, data drift operates insidiously—your model continues making predictions with high confidence while its accuracy quietly erodes. The incoming data distribution shifts from what the model learned during training, whether … Read more

Integrating CockroachDB with Airflow and dbt

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Modern data engineering workflows demand robust orchestration, reliable transformations, and databases that can scale with growing data volumes. Integrating CockroachDB with Apache Airflow and dbt (data build tool) creates a powerful stack for building production-grade data pipelines that combine the best of distributed databases, workflow orchestration, and analytics engineering. This integration enables data teams to … Read more

Airflow vs Step Functions: Choosing the Right Orchestration Tool

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Orchestrating complex data pipelines and workflows has become a critical capability for modern data engineering and machine learning operations. Two prominent solutions have emerged as leaders in this space: Apache Airflow, the open-source workflow management platform originally developed at Airbnb, and AWS Step Functions, Amazon’s fully managed serverless orchestration service. While both tools solve workflow … Read more

Real-Time CDC Data Pipeline Using Airflow and Postgres

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Building a Change Data Capture (CDC) pipeline with Apache Airflow and PostgreSQL creates a powerful data integration solution that balances real-time requirements with operational simplicity. While Airflow is traditionally known for batch orchestration, its extensible architecture and support for sensors, custom operators, and dynamic DAG generation make it surprisingly capable for near real-time CDC workloads. … Read more

How to Orchestrate Databricks DLT Pipelines with Airflow

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Orchestrating Delta Live Tables pipelines within a broader data ecosystem requires integrating DLT’s declarative framework with external workflow management systems. Apache Airflow has emerged as the de facto standard for complex data orchestration, providing sophisticated scheduling, dependency management, and monitoring capabilities that complement DLT’s pipeline execution strengths. While DLT excels at managing internal pipeline dependencies … Read more

Orchestrating ML Workflows Using Airflow or Dagster

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Machine learning workflows are complex beasts. They involve data extraction, validation, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring—all of which need to run reliably, often on schedules, and with proper handling of failures and dependencies. This is where workflow orchestration tools become essential. Apache Airflow and Dagster have emerged as two leading solutions, … Read more

How to Build End-to-End ML Pipelines with Airflow and DBT

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Building production-ready machine learning pipelines requires orchestrating complex workflows that transform raw data into model predictions. Apache Airflow and dbt (data build tool) have emerged as a powerful combination for this task—Airflow handles workflow orchestration and dependency management, while dbt brings software engineering best practices to data transformation. Together, they enable teams to build maintainable, … Read more

Airflow vs Prefect for Machine Learning Pipelines

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Building robust machine learning pipelines requires orchestration tools that can handle complex workflows, manage dependencies, and scale with your data science operations. Apache Airflow and Prefect have emerged as two leading contenders in this space, each bringing distinct philosophies and capabilities to ML pipeline orchestration. Understanding their core differences is essential for data science teams … Read more