Seamless Data Analytics Workflow: From Dockerized JupyterLab and MinIO to Insights with Spark SQL

The tutorial provides comprehensive guidance on an analytics use case, detailing the process of analyzing semi-structured data with Spark SQL and utilizing Docker to set up the environment. It covers data engineering, data retrieval from an API, storage in MinIO, data transformation using PySpark, and data analysis with Spark SQL. The tutorial offers practical insights and instructions for working with various technologies.

 Seamless Data Analytics Workflow: From Dockerized JupyterLab and MinIO to Insights with Spark SQL

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Seamless Data Analytics Workflow

Understanding the building blocks

Setting up Docker Desktop

Configuring MinIO

Getting started with JupyterLab

Data pipeline: The ETL process

Analysing semi-structured data

Cleanup of resources

Conclusion

References



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