Data is everywhere, but turning it into something useful isn't easy. That's where data integration tools come in. Two of the most popular options are AWS Glue and Apache Airflow, each with its strengths. But choosing the right one depends on what you need to achieve.AWS Glue: Brief OverviewAWS Glue is a serverless and fully managed ETL service launched by Amazon Web Services in 2020. It aims to simplify the automation of extracting, transforming, and loading data for analytics. It supports various data formats and sources, including lakes, relational databases, and streaming data. The key features of AWS Glue comprise data catalog, ETL job creation, serverless execution, and integration with AWS services. So, you will have flexible support for all workloads like ELT, ETL, and streaming in one platform with AWS Glue. Apache Airflow: Brief OverviewApache Airflow is an open-source workflow management tool launched by Airbnb in 2014. It is now offered as an open-source tool under the Apache Software Foundation. It helps users run tasks in order, either on a set schedule or whenever needed.Airflow uses Python to build workflows that are easy to schedule and track. Yet, the workflows are defined as Directed Acyclic Graphs (DAGs) in Apache Airflow. The tasks are organized here based on the dependencies and execution order. Some of the critical traits of Airflow include workflow orchestration, dynamic workflows, extensibility, and monitoring and managing workflows by viewing task execution logs, receiving alerts, and tracking progress.Key Differences: AWS Glue vs. AirflowAlthough AWS Glue and Apache Airflow are workflow management tools and may share some similarities, they are still quite different in certain aspects. So, let's first learn key differences that show when to use which workflow automation tool. DimensionAWS GlueApache AirflowPurposeAn all-in-one tool for data integration.A platform for managing and organizing data workflows.InfrastructureServerless, fully managed service.Needs to be installed on user-managed servers, but there are managed options for easy setup.LicensingPaid, cloud managed service.Open source or managed service.FlexibilityOnly supports the Spark framework for running transformation tasks.Supports multiple execution frameworks because Airflow is designed for task management.MonitoringNatively integrates with AWS CloudWatch.Requires separate configuration to support monitoring and logging.Choosing Between AWS Glue and Apache AirflowAs mentioned earlier, AWS Glue is a fully managed data integration service from Amazon. It helps data engineers discover, extract, combine, transform, and load data into warehouses or lakes. It serves as an all-in-one ETL or ELT tool. AWS Glue is a good choice if your ETL jobs are straightforward and you need a simple data transformation and migration solution.Use Apache Airflow if your organization has complex data pipelines with many dependencies. It's ideal for scheduling and orchestrating batch data jobs across different technologies. Airflow has built-in operators for popular ETL tools and lets developers write custom code to trigger any tool that works with Python.What are you looking for?Workflow OrchestrationAirflow is a workflow orchestration tool that helps developers automate complex tasks and visualize them through an easy-to-use interface. Unlike typical schedulers, it organizes complicated ETL workflow dependencies into directed acyclic graphs (DAGs) of tasks, making creating, running, and monitoring data pipelines easier. This allows users to rerun batch ETL pipelines that fail. Its flexibility lets you integrate and deploy single or multiple data sources and processing frameworks within larger workflows. For example, only specific jobs will run if an upstream job fails; if all upstream jobs succeed, a different set of tasks will run.ETL FrameworkWhile Airflow can serve as the backbone of a data integration system, actual data processing relies on external services like Spark and Snowflake. Airflow orchestrates tasks based on third-party data processing frameworks. Therefore, organizations with multiple data processing frameworks and complex routing logic should consider AWS Glue for workflow orchestration. Glue uses Apache Spark for all its data processing needs and is ideal for developers wanting a fully managed solution with custom PySpark scripts.AWS Glue's all-in-one ETL framework covers data discovery, transformation, and workflow management. It includes its processing framework, metadata management, and workflow management system. However, Glue's workflow management is not as generic as Airflow and is designed for Glue functions like Glue Data Catalog, Glue Studio, and Glue DataBrew. If you're not focused on open-source frameworks, consider using AWS Glue.What kind of infrastructure do you prefer?Server-BasedAirflow can be installed on on-premises servers or cloud virtual machines requiring maintenance—however, many cloud providers, like Amazon MWAA and Astronomer offer such easily managed Airflow services.ServerlessAWS Glue is a serverless ETL platform with no installation required and doesn't need infrastructure management. However, engineers must still set up network and security policies to ensure system security.How much flexibility do you need?Process jobs outside of the AWS ecosystemAs a facilitator of jobs (e.g., Spark, Hive, API calls, or custom applications), Apache Airflow offers more flexibility than AWS Glue for extraction and transformation tasks. Besides Spark, Airflow can orchestrate jobs with tools like Presto and managed services like Google Dataflow. In short, Airflow doesn't lock users into the AWS ecosystem.Process jobs within the AWS ecosystem onlyAWS Glue relies on Apache Spark for all data processing and is limited to AWS services. If you're comfortable with the AWS ecosystem and don't mind being tied to one cloud provider, Glue is a better choice. It easily integrates with AWS services like S3, RDS, Redshift, and external JDBC sources, simplifying data connections and offering a unified platform for managing data.How to create Workflow in AWS Glue?Steps to Create a Workflow in AWS Glue:Create an AWS Glue Job:Go to the AWS Glue Console.Navigate to Jobs and click Add Job.Set a name for your job and configure the necessary parameters (such as role, IAM permissions, script path, etc.).Define the job's script using either Python or Scala to transform your data.Save the job.Create AWS Glue Triggers:Triggers in AWS Glue are used to initiate jobs, crawlers, or other workflows.Navigate to Triggers in the Glue console and click Add Trigger.Define the trigger's schedule (on-demand, time-based, or event-based).Attach the trigger to one or more Glue jobs to control when they start.Create AWS Glue Crawlers (optional):Crawlers are used to scan data in a specified data store and create metadata tables in the AWS Glue Data Catalog.If your workflow requires reading new data from data sources, create a crawler that will identify the data schema.In the AWS Glue Console, navigate to Crawlers and click Add Crawler.Specify the data source and target (S3, JDBC, etc.), and configure the necessary connection options.Create a Workflow:In the AWS Glue Console, go to the Workflows section.Click Add Workflow and provide a name for the workflow.Once the workflow is created, you will be able to add the following components:Jobs: Add the jobs created earlier to the workflow.Triggers: Add triggers to specify the execution flow.Crawlers: Add any required crawlers to scan the data and update the catalog.Connect Components:Using the Graph Editor, connect the components (jobs, crawlers, triggers) in the order in which they should be executed.Set dependencies between jobs and triggers, ensuring jobs start only when required conditions are met (like a previous job completion).Monitor the Workflow:After the workflow is activated, you can monitor its execution through the AWS Glue Console.Logs and status updates will be available to track progress, detect issues, and troubleshoot failed steps.Steps to Create a Workflow in Apache Airflow1. Install Apache Airflowpip install apache-airflow2. Set Up Airflow Directoryairflow db init3. Next, create a directory for your DAG files:mkdir -p ~/airflow/dags4. Create a DAGfrom airflow import DAG from airflow.operators.python_operator import PythonOperator from datetime import datetime, timedelta # Define default arguments for the DAG default_args = { 'owner': 'airflow', 'depends_on_past': False, 'start_date': datetime(2024, 10, 21), 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } # Initialize the DAG dag = DAG( 'my_workflow', default_args=default_args, description='My first Apache Airflow workflow', schedule_interval=timedelta(days=1), # Run once a day ) # Define tasks def print_hello(): print("Hello from Airflow!") def print_goodbye(): print("Goodbye from Airflow!") # Task 1: A simple Python function hello_task = PythonOperator( task_id='hello_task', python_callable=print_hello, dag=dag, ) # Task 2: Another Python function goodbye_task = PythonOperator( task_id='goodbye_task', python_callable=print_goodbye, dag=dag, ) # Set task dependencies hello_task >> goodbye_task5. Configure AirflowBefore running your workflow, make sure that your Airflow environment is properly configured. Update the airflow.cfg file, usually located in ~/airflow/, with the correct configurations like database connection, scheduler settings, etc.6. Start Airflow Servicesairflow webserver --port 8080 airflow scheduler7. Access the Airflow UIhttp://localhost:8080Pricing Comparision of AWS Glue and Apache AirflowFeatureAWS GlueApache AirflowPricing ModelPay-as-you-goFree (Open Source), but infrastructure and operational costs applyCost StructureCharged based on Data Processing Units (DPU) per hour, and storageNo direct cost for the software, but costs for hosting (cloud or on-prem), maintenance, and scalingDPU Pricing$0.44 per DPU hour (in US-East region)N/AFree Tier1 million requests for crawlers and ETL jobs, 10 DPUs/month free for the first 2 monthsFree to use, but no free-tier hosting or managed servicesManaged ServiceFully managed, serverless ETLManaged solutions available (e.g., Google Composer, AWS Managed Workflows for Apache Airflow)Operational CostsIncluded in AWS Glue pricing (AWS handles infrastructure and scaling)Requires dedicated resources for setup, maintenance, and scalingInfrastructureServerless, no management requiredSelf-hosted or cloud-hosted (e.g., EC2, Kubernetes), requires managementScalingAuto-scaling based on workloadManual scaling or use of managed services for auto-scalingAdditional CostsGlue Data Catalog: $1 per 1,000,000 objects storedCloud infrastructure (VMs, storage, networking)Comparison of commonly used CLI commands for AWS Glue and Apache AirflowOperationAWS Glue CLI CommandsApache Airflow CLI CommandsList Jobsaws glue list-jobsairflow jobs listRun a Jobaws glue start-job-run --job-name <job_name>airflow dags trigger <dag_id>Check Job Statusaws glue get-job-run --job-name <job_name> --run-id <run_id>airflow tasks state <dag_id> <task_id> <execution_date>List Crawlersaws glue list-crawlersN/A (No direct equivalent)Create a Jobaws glue create-job --name <job_name> --role <role> --command <script_location>N/A (Jobs are created using Python files or DAGs, no direct CLI command for job creation)Delete a Jobaws glue delete-job --job-name <job_name>N/A (Jobs are removed by deleting the corresponding DAG file)View LogsIntegrated with AWS CloudWatch, logs can be accessed via AWS Consoleairflow logs <dag_id> <task_id> <execution_date>Manage Workflowsaws glue create-workflow --name <workflow_name>airflow dags list or airflow dags pause/unpause <dag_id> for workflow managementCheck Job Historyaws glue get-job-runs --job-name <job_name>airflow dag_state <dag_id> <execution_date>Create Database in Data Catalogaws glue create-database --database-input <database_info>N/A (Airflow does not provide a native data catalog, users typically integrate with external systems)Trigger a Workflowaws glue start-workflow-run --name <workflow_name>airflow dags trigger <dag_id>Kill a Jobaws glue batch-stop-job-run --job-name <job_name> --job-run-id <run_id>airflow tasks kill <dag_id> <task_id>Scheduler ManagementN/A (Managed service, no scheduler needed)airflow scheduler to start the Airflow schedulerCreate a Connectionaws glue create-connection --connection-input <connection_info>N/A (Connections are defined in the Airflow UI or configuration files)Manage Plugins/ExtensionsN/A (No need for plugins, serverless)airflow plugins list or managed through the Python environmentConclusionSome organizations prefer to use both AWS Glue and Apache Airflow together. Airflow can trigger Glue processes through its operators, hooks, and sensors. For example, you can use Glue's Crawlers to scan data, update the Glue Data Catalog, and access that catalog through an Airflow hook. This allows you to combine the strengths of both tools in a single workflow.Read Morehttps://devopsden.io/article/aws-data-engineer-interview-questionsFollow us onhttps://www.linkedin.com/company/devopsden/
