Data Pipelines With Apache Airflow Pdf Download
Liv Randzin
Apache Airflow is an easy-to-use orchestration tool making it easy to schedule and monitor data pipelines. With your knowledge of Python, you can write DAG scripts to schedule and monitor your data pipeline.
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Over the years, individuals and businesses have continuously become data-driven. The urge to implement data-driven insights into business processes has consequently increased the data volumes involved. Open source tools like Apache Airflow have been developed to cope with the challenges of handling voluminous data. This article comprehensively looks at what is Apache Airflow and evaluates whether it's the right tool of choice for data engineers and data scientists. We know you are enthusiastic about building data pipelines from scratch using Airflow. We will also dig into a practical Apache Airflow use case to get you started managing your workflows with Apache Airflow.
To understand Apache Airflow, it's essential to understand what data pipelines are. Data pipelines are a series of data processing tasks that must execute between the source and the target system to automate data movement and transformation. For example, if we want to build a small traffic dashboard that tells us what sections of the highway suffer traffic congestion. We will perform the following tasks:
Apache Airflow is a batch-oriented tool for building data pipelines. It is used to programmatically author, schedule, and monitor data pipelines commonly referred to as workflow orchestration. Airflow is an open-source platform used to manage the different tasks involved in processing data in a data pipeline.
A data pipeline in airflow is written using a Direct Acyclic Graph (DAG) in the Python Programming Language. By drawing data pipelines as graphs, airflow explicitly defines dependencies between tasks. In DAGs, tasks are displayed as nodes, whereas dependencies between tasks are illustrated using direct edges between different task nodes. If we apply the graph representation to our traffic dashboard, we can see that the directed graph provides a more intuitive representation of our overall data pipeline.
A quick glance at the graph view of the traffic dashboard pipeline indicates that the graph has direct edges with no loops or cycles (acyclic). The acyclic property is significant as it prevents data pipelines from having circular dependencies. As shown below, this can become problematic by introducing logical inconsistencies that lead to deadlock situations in data pipeline configuration in Apache Airflow as shown below -
In Apache airflow, a DAG is defined using Python code. The Python file describes the structure of the correlated DAG. Consequently, each DAG file typically outlines the different types of tasks for a given DAG, plus the dependencies of the various tasks. Apache Airflow then parses these to establish the DAG structure. In addition, DAGs Airflow files contain additional metadata that tells airflow when and how to execute the files.
The advantage of defining Airflow DAGs using Python code is that the programmatic approach provides users with much flexibility when building pipelines. For instance, users can utilize Python code for dynamic pipeline generation based on certain conditions. The flexibility offers great workflow customization, allowing users to fit Airflow to their needs.
Airflow is an excellent choice if you want a big data tool with rich features to implement batch-oriented data pipelines. Its ability to manage workflows using Python code enables users to create complex data pipelines. Also, its Python foundation makes it easy to integrate with many different systems, cloud services, databases, and so on.
Because of its rich scheduling capabilities, airflow makes it seamless for users to run pipelines regularly. Furthermore, its backfilling features make it easy for users to re-process historical data and recompute any derived datasets after making changes to the code, enabling dynamic pipeline generation. Additionally, its rich web UI makes it easy to monitor workflows and debug any failures.
As a data engineer, you'll be frequently tasked with cleaning up messy data before processing and analyzing it. So in our sample data pipeline example using airflow, we will build a data cleaning pipeline using Apache Airflow that will define and control the workflows involved in the data cleaning process.
As a data engineer, you will mainly be required to make raw data accessible and usable by other professionals such as data analysts and scientists. You can therefore experiment with a data lake pipeline DAG that authors, monitors, and schedules the capturing, storage, and processing of raw data using Python and PostgreSQL.
Apache Spark is a big data processing engine detailed with built-in data streaming modules, SQL, machine learning tools, graph processing features, and SQL. Its speed, support for sophisticated analytics, real-time stream processing, and scalability are some of the reasons why it's sought after by businesses handling big data.
Despite being designed for workflow management. Many teams also utilize Airflow for running their ETL pipelines. This can be linked to its extensive range of operators that can be configured with different systems that make it easier to implement.
Yes, they do. Apache Airflow is a reliable tool used by data scientists to repeatedly manage complex processes at every stage of a data science project. Its ability to run reproducible data pipelines and the reusability of DAGs particularly make it appealing to data scientists.
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