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Installing Apache Superset on Amazon EMR
Add data exploration and visualization to your analytics cluster
Introduction
AWS provides nearly twenty-five different open-source data analytics applications that can be automatically installed and configured on Amazon Elastic MapReduce (Amazon EMR). Of all those options, EMR doesn’t offer a general-purpose data exploration and visualization tool. However, with EMR, you can automate the installation of additional software as part of the cluster creation process or post cluster creation. This brief post will explore how to install, configure, and access Apache Superset, the modern data exploration and visualization platform on Amazon EMR’s Master Node, as a post-cluster creation step. You can use these same techniques to install other software packages on EMR as well, manually or as part of an automated Data Pipeline.
Amazon EMR
According to AWS, Amazon EMR is a cloud-based big data platform for processing vast amounts of data using open source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.
AWS currently offers 5.x and 6.x versions of Amazon EMR. The latest Amazon EMR releases are Amazon EMR Release 6.2.0 and Amazon EMR Release 5.32.0. Each version of Amazon EMR offers incremental major and minor releases of nearly 25 different, popular open-source big-data software packages to choose from, which Amazon EMR will install and configure when the cluster is created.
Apache Superset
According to its website, Apache Superset is a modern data exploration and visualization platform. Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts.
Superset natively supports over twenty-five data sources, including Amazon Athena and Redshift, Apache Drill, Druid, Hive, Impala, Kylin, Pinot, and…
