and dependencies inside it. Resource of the Week: Apache Airflow Documentation — DAGs

📌Why it matters for data engineers Once you understand DAGs, Airflow becomes much easier to reason about. You're no longer just learning syntax; you're learning how to represent a real data workflow. 💡Take one of your existing pipelines and try to identify all the tasks

◉ A DAG represents a workflow ◉ Tasks represent individual units of work ◉ Dependencies determine how tasks relate to each other ◉ A DAG should not contain cycles ◉ A DAG defines the workflow, while a DAG run represents an execution

◉ A task represents one unit of work inside that workflow. 📌For example, a daily_sales_pipeline DAG could contain tasks for extracting sales data, validating it, transforming it and loading it into the warehouse. 📌Key Points ◉ DAG means Directed Acyclic Graph

tasks happen independently before coming together later. The important part is that the workflow has a direction and doesn't contain a cycle. 📌There is also an important distinction: ◉ A DAG is not a task. ◉ A DAG represents the overall workflow.

📌For example, if your transformation can only happen after extraction is complete, that dependency needs to be represented in the workflow. >>> A simple DAG might look like: Extract ↓ Transform ↓ Load You can also have branching workflows where different

What Exactly Is a DAG? You've probably heard: “Create a DAG in Airflow.” But what exactly is a DAG? DAG stands for Directed Acyclic Graph. The name sounds more complicated than the idea. A DAG is simply a way of representing tasks and the relationships between those tasks.

Happy Birthday, Chizoba! 🥳 With love from all of us at the Data Engineering Community

through your beautiful designs and for being a valuable part of our community. We truly appreciate your contributions. As you celebrate another year, we wish you greater achievements, endless joy, good health, and beautiful opportunities in the year ahead.

🎉 Happy Birthday, Chizoba Eze ! 🎂 Today, we celebrate you, not just for the amazing person you are, but also for the creativity and dedication you bring to the Data Engineering Community as our graphics designer and volunteer. Thank you for bringing our ideas to life

📅 Saturday, 17 October | 5–7 PM WAT 🔗 Register here: meetumo.ai/e/from-single-az-… Hope to see you there!

🚀 Reminder! Join us this Saturday at the Data Engineering Community as @akinyele_mayowa Akiny shares practical lessons from migrating a production Kafka cluster from Single-AZ to Multi-AZ on Confluent Cloud.

became difficult to manage once you needed to automate it? 📌Resource of the Week: Apache Airflow Documentation — Core Concepts

📌Why it matters for data engineers Modern data pipelines rarely consist of one script doing one thing. They involve multiple systems and dependencies, and orchestration helps bring those pieces together. ✨Have you ever had a pipeline that worked perfectly manually but

◉ It manages dependencies between tasks ◉ It helps automate pipeline execution ◉ It provides mechanisms for retries and failure handling ◉ It makes workflows easier to monitor ◉ Airflow, Prefect and Dagster are examples of orchestration tools

processing. ◉ Your Python code can still transform the data. ◉ Your database can still store it. ◉ Your warehouse can still run SQL. The orchestrator coordinates when and how these pieces run. 📌Key Points ◉ Orchestration coordinates data workflows

◉ Scheduling ◉ Task dependencies ◉ Retries ◉ Failures ◉ Logging ◉ Monitoring ◉ Backfills Tools such as Airflow, Prefect and Dagster are commonly used for this. 📌And here's an important distinction: The orchestrator doesn't necessarily do the actual data

◉ Extracting data from an API ◉ Validating the response ◉ Transforming the data ◉ Loading it into a warehouse ◉ Running dbt models ◉ Updating downstream reports Doing all of this manually doesn't scale very well. An orchestration tool helps manage things like:

to retry only the failed step? 📌This is where orchestration comes in. Orchestration is about coordinating different tasks in a workflow and making sure they run at the right time and in the right order. 📌For example, a daily data pipeline might involve: