Amazon EMR Studio now supports multi-language Jupyter-based notebooks for Spark workloads
EMR Studio is an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug big data and analytics applications written in R, Python, Scala, and PySpark. Today, we are excited to announce that from EMR 6.4.0 and later, you can use Python, Scala, SparkSQL, and R within the same Jupyter notebook in EMR Studio, providing flexibility to use different programming languages for Spark workloads.
EMR Studio is an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug big data and analytics applications written in R, Python, Scala, and PySpark. Today, we are excited to announce that from EMR 6.4.0 and later, you can use Python, Scala, SparkSQL, and R within the same Jupyter notebook in EMR Studio, providing flexibility to use different programming languages for Spark workloads.
Previously, you could only write code in one language within the same notebook for Spark workloads. With this feature enhancement to Jupyter notebooks, you can now switch between Python, Scala, SparkSQL, and R within the same Jupyter notebook and share data between cells via temporary tables. You can also use this feature from EMR Notebooks or from Jupyter notebooks talking to Jupyter Enterprise Gateway (JEG) on EMR 6.4.0 and later.
EMR Studio is available in US East (Ohio), US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo) regions.
To learn more about using multiple languages in the same Jupyter notebook in EMR studio see our documentation here.