April 2022 Summaries

Data scientists play a crucial role in organizations by leveraging data to identify trends, develop insights, and build models that inform decision-making, distinguishing themselves from data engineers who primarily focus on pipeline design. The evolution of data science has positioned data scientists at the intersection of business, computer science, and statistics, making them essential for navigating the complexities of modern data environments. They face challenges such as handling data from multiple sources, ensuring reproducibility, defining KPIs, and coordinating across teams. Apache Airflow® is highlighted as a valuable tool for data scientists, offering a flexible workflow solution that enhances reproducibility, reusability, and collaboration by providing a stable pipeline platform with features like monitoring, alerts, and data quality checks. Airflow simplifies workflow management, allowing data scientists to focus on exploration and development rather than operational details, and Astronomer's Astro further enhances Airflow's capabilities by offering a fully managed service that allows data teams to focus on strategic tasks. Despite advancements in automation, the unique value of data scientists lies in their ability to interpret data and provide insights that align with business needs, a function that cannot be fully automated.

This article outlines 10 best practices for modern data orchestration using Apache Airflow. Standardizing production and development environments, getting current and keeping current, designing DAGs to take advantage of Airflow's built-in parallel processing, pushing workload processing "out" closer to where the data lives, designing Airflow environments for micro-orchestration, maximizing reuse and reproducibility, integrating Airflow with CI/CD tools and processes, using Airflow's Taskflow API to move data between tasks, and focusing on observability and modern data orchestration are key components. By following these best practices, organizations can create a sustainable enterprise data integration ecosystem that accelerates the flow of trusted data across their organization.

Airflow and dbt are complementary tools that enhance collaboration across data teams by addressing data orchestration and transformation, respectively, forming an integral part of modern data stacks. They can be seamlessly integrated, with options depending on whether teams are using dbt Core or dbt Cloud. Airflow's flexibility allows for custom data ingestion workflows, while dbt Core is used for data modeling, supported by methods like the dbt CLI with BashOperator or KubernetesPodOperator. The recent introduction of dbt 1.0 simplifies rerunning jobs from failure, enhancing workflow efficiency. Meanwhile, the new dbt Cloud Provider, developed by Astronomer and dbt Labs, offers an operator, hook, and sensor to orchestrate and monitor dbt Cloud jobs using Airflow, eliminating the overhead of dbt Core and providing a unified interface for data engineers and analysts. This integration allows data teams to focus on designing data pipelines and models without being burdened by infrastructure management.