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Apache Flink became generally available for Amazon EMR on EKS from the EMR 6.15.0 releases. As it is integrated with the Glue Data Catalog, it can be particularly useful if we develop real time data ingestion/processing via Flink and build analytical queries using Spark (or any other tools or services that can access to the Glue Data Catalog). In this post, we will discuss how to set up a local development environment for Apache Flink and Spark using the EMR container images. After illustrating the environment setup, we will discuss a solution where data ingestion/processing is performed in real time using Apache Flink and the processed data is consumed by Apache Spark for analysis.

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The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services - Redshift, Glue, EMR and Athena. In part 4 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Amazon EMR on EKS. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.

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The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services - Redshift, Glue, EMR and Athena. In part 3 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Amazon EMR. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.

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The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services - Redshift, Glue, EMR and Athena. In part 2 of the dbt on AWS series, we discuss data transformation pipelines using dbt on AWS Glue. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.

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We will discuss how to set up a remote dev environment on an EMR cluster deployed in a private subnet with VPN and the VS Code remote SSH extension. Typical Spark development examples will be illustrated while sharing the cluster with multiple users. Overall it brings an effective way of developing Spark apps on EMR, which improves developer experience significantly.

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We'll discuss how to implement data warehousing ETL using Iceberg for data storage/management and Spark for data processing. A Pyspark ETL app will be used for demonstration in an EMR local environment. Finally the ETL results will be queried by Athena for verification.

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Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. In this post, we'll build a Hudi DeltaStramer app on Amazon EMR and use the resulting Hudi table with Athena and Quicksight to build a dashboard.