The following page is the home to all anchored, referenced and used resources that have helped and contributed to the continued creation of this site and its content. Each section has a title, date, link to published post and is ordered via roman numerals counted from i forward and on.:
iii. Python resources, for use with Power BI, Azure services and Azure Bot Framework:Date: 09.2018
Several posts, articles, how-to’s with the incorporation of Python within the Azure and Power platforms from Microsoft.
Contained within this section of the resource page are useful resources for working with Python. Specifically for Python targeted within Microsoft Power BI, Azure Services (ie: Data Factory, Azure Databricks, etc.) and finally the Azure Bot Framework.
These resources have / are / will be used in the course of writing content for GoPowerBIPro.com – Oppia.co course-ware, etc.
- How to use Service Bus Queues with Python
- Anaconda3 download and information page
- PyData.org home page
- Pandas and Dataframe information and tips
- Python zero to hero article on Medium
- A-to-Z Useful Python Tricks on Medium
- Web Scraping using Python on Medium
Further some of the above resources where used in the crafting of this blogs first post relating to Python + Power BI:
- GoPowerBIPro: Unleash Python within Microsoft Power BI Desktop
- Medium: Medium posted – Unleash Python within Microsoft Power BI Desktop
For those items that have Medium / @ Medium / on Medium referenced – Medium is becoming a large part of the marketing funnel and pipeline for GoPowerBIPro.com – Oppia.co. Through the use of Medium a larger audience is able to engage with the target content that we are provided. Further the course-ware that is / being developed for Oppia.co learning journey platform is also being targeted for landing and driving awareness through Medium.com.
ii. The history, origin and evolution of the phrase and technology / concept ‘data warehouse’:Date: 07.2018
Considered by many to be the Father of Data Warehousing, Bill Inmon first began to discuss the principles around the Data Warehouse and even coined the term in the 1970s, as mentioned earlier. In 2007, Inmon was named by Computerworld as one of the “Ten IT People Who Mattered in the Last 40 Years.”Aug 23, 2012. (resource: data warehouse home on DATAVERSITY)
Though Inmon has been credited for coining the phrase ‘data warehouse’ Ralph Kimball approach to data warehouse design, including application and execution of an agile approach has won out over the past 15 years. Of late however, through the advent of ‘the cloud’ Inmon design focus around specific and compartment concepts of data marts that extend from / to a data warehouse has started to gain some traction back. Specific use cases show such Inmon based designs prove better suited in the cloud age.
The following are resources / pages that can be referenced and which you can find greater details relating to data warehouses, their origin and the great data religious wars between the Inmon and Kimball camps.
- Bill Inmon Wikipedia Home
- Bill Inmon vs. Ralph Kimball
- Data Warehouse Wikipedia Home
- Zentut Ralph Kimball Data Warehouse Architecture Home
i. Azure Modern Data Warehouse – Architecture elements and details:Date: 07.2018
Microsoft Azure ~ Solutions > Architectures > Modern Data Warehouse – Implementation guidance:
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Brandon George |
Master Data Story TellerAmy George |
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