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What is Apache airflow EMR Serverless?

The Apache Airflow Elastic MapReduce (EMR) Serverless is an AWS deployment option for Apache Airflow that makes use of AWS EMR and AWS Glue.

We can execute Airflow processes on AWS utilizing serverless infrastructure using Airflow EMR Serverless. Airflow has always required us to provide and operate our own infrastructure, such as virtual machines or containers.

With the serverless method, however, we can focus on creating and operating the operations without having to worry about infrastructure administration.

How does the Apache Airflow EMR Serverless setup typically works?

The Serverless configuration commonly works as follows:

• AWS Glue Data Catalog: The Glue Data Catalog serves as a store for the data assets’ metadata. It keeps track of data sources, tables, schemas, and transformations.
• AWS Glue ETL Jobs: AWS Glue ETL jobs can execute data transformations and processing. Airflow workflows can trigger these processes as part of the data pipeline.
• AWS Glue Crawlers: Glue Crawlers find the schema and structure of the data sources automatically, making it easier to work with various data formats and sources.
• Airflow DAGs: In Apache Airflow, workflows; using Direct Acyclic Graphs (DAGs). Each DAG has a set of jobs that run sequentially or in parallel depending on their dependencies. These tasks can cause AWS Glue ETL jobs to run and process data.
• AWS EMR: EMR is used to process enormous amounts of data. It provides managed Hadoop clusters for operations such as data input, processing, and analysis. As part of the workflow, Airflow may plan and manage EMR clusters.

We may develop complex data pipelines using Apache Airflow and exploit the serverless features of AWS EMR and AWS Glue by integrating these components. Scalability, cost optimization, and decreased infrastructure management overhead are all advantages of this strategy.

We may grow the EMR clusters dynamically based on the workload and pay solely for the resources required during execution.

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