Tutorial: Analyze and visualize open data with Apache Spark

Life Expectancy Map

Many government agencies and public administrations offer access to data, contributing to open data. Using IBM Watson Studio with Jupyter Notebooks and Apache Spark it is simple to retrieve, combine and analyze data from different sources. The result can be easily visualized. Learn what it takes with this IBM Cloud solution tutorial.

Overview

In the tutorial, you are going to use IBM Watson Studio to organize all required resources. Watson Studio serves as glue around the data, cloud object storage, Apache Spark as compute platform, and Jupyter Notebooks. A notebook is an open-source web application that contains live code, equations, visualizations and narrative text.
You are going to combine open data about country population, life expectancy rates and country ISO codes. First, data is loaded into so-called data frames. Then, because data from different sources may have a different format, you tranform the frames. Thereafter, analyze the data using SQL. By utilizing the PixieDust library, even visualizations are easily done. The screenshot shows how life expectancy rate be country can be depicted on a zoomable map.

Conclusions

With few steps, you can retrieve open data sets from different sources. Then, combine and analyze them in a Jupyter Notebook in Watson Studio and visualize the data. Try it yourself by following this tutorial “Analyze and visualize open data with Apache Spark“. Also, check out the other IBM Cloud solution tutorials in the IBM Cloud documentation.

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(This blog entry was first published on the IBM Cloud blog)