Big Data Using R with Jupyter Notebooks and Oracle Big Data Discovery Oracle's Big Data Discovery [https://www.oracle.com/big-data/big-data-discovery/index.html] encompasses a good amount of exploration, transformation, and visualisation capabilities for datasets residing in your organisation’s data reservoir. Even with this though, there may come a time when your data scientists want to unleash their
Big Data Using SparkSQL and Pandas to Import Data into Hive and Big Data Discovery Big Data Discovery [https://www.oracle.com/big-data/big-data-discovery/index.html] (BDD) is a great tool for exploring, transforming, and visualising data stored in your organisation’s Data Reservoir. I presented a workshop on it at a recent conference [https://speakerdeck.com/rmoff/unlock-the-value-in-your-big-data-reservoir-using-oracle-big-data-discovery-and-oracle-big-data-spatial-and-graph] , and got an interesting question from
Big Data Using Jupyter Notebooks with Big Data Discovery 1.2 New in Big Data Discovery [https://www.oracle.com/big-data/big-data-discovery/index.html] 1.2 is the addition of BDD Shell, an integration point with Python. This exposes the datasets and BDD functionality in a Python and PySpark environment, opening up huge possibilities for advanced data science work on BDD
Big Data Running Big Data Discovery Shell and Jupyter Notebook on Big Data Lite VM 4.5 New in Big Data Discovery [https://www.oracle.com/big-data/big-data-discovery/index.html] 1.2 is the addition of BDD Shell, an integration point with Python. This exposes the datasets and BDD functionality in a Python and PySpark environment, opening up huge possibilities for advanced data science work on BDD
