Jupyter Notebook is an open source web application for Machine Learning and Data Exploration.
In this post I will show you how to connect a Jupyter Notebook to Oracle Autonomous Database and explore the data using Python.
The assumption is we already have a Jupyter notebook sandbox running on Oracle Cloud compute instance.
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Over the past few years, in everything from personal photo storage to enterprise development there has been an incredible uptake of cloud storage buckets which provide a simple, low-cost mechanism for storing unstructured data. For much longer than that, there has been a need to consume, stage and produce unstructured data in enterprise integration scenarios. File servers accessible via protocols such as FTP/SFTP, etc. are commonly used to meet the staging requirements of traditional file-based integration use cases.
The practice of using an FTP/SFTP server alongside Oracle’s cloud integration platform – Oracle Integration Cloud (henceforth, OIC), is nothing new and is well documented.
In a series of upcoming blogs, I will explore how we can use Oracle’s cloud storage buckets (Oracle Cloud Infrastructure – Object Storage) instead of a traditional FTP server in cloud-based integrations. In this initial blog I will focus on:
- Connecting to Object Storage with Oracle Integration Cloud
- Writing Files to Object Storage with Oracle Integration Cloud
- Enforcing an archival policy for files at rest