jupyter - Rittman Mead

Getting Started with Spark Streaming, Python, and Kafka

Last month I wrote a series of articles [https://www.rittmanmead.com/blog/2016/12/etl-offload-with-spark-and-amazon-emr-part-5/] in which I looked at the use of Spark for performing data transformation and manipulation. This was in the context of replatforming an existing Oracle-based ETL and datawarehouse solution onto cheaper and more elastic

spark

ETL Offload with Spark and Amazon EMR - Part 2 - Code development with Notebooks and Docker

In the previous article [https://www.rittmanmead.com/blog/2016/12/etl-offload-with-spark-and-amazon-emr-part-1/] I gave the background to a project we did for a client, exploring the benefits of Spark-based ETL processing running on Amazon's Elastic Map Reduce (EMR) Hadoop platform. The proof of concept we ran was on

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