I was recently tasked with ingesting several large zip files (over 5G) into our Anticipatory Intelligence tool workflow. When processing files for our tool we…
Author: Jim McHugh
An accomplished Sr. IT leader with over 27 years of professional experience applying technology solutions to business challenges. Mr. McHugh has expertise in data modeling, data governance, business intelligence, predictive analytics and data science. His responsibilities include establishing and executing a strategy that ensures the application of data management & analytics to enable an organization to strategically leverage and fully realize the value of their data.
Comparing CSV Files Using KNIME I was recently tasked with retrieving a file from a website for use in our anticipatory analysis tool. The file…
Not all cloud providers offer similar solutions. In fact, the differences between the leading cloud warehouse offerings can be quite drastic. So, what should your…
Data warehouses are moving to the cloud at an increasing pace. Each year the technology gets better and better and the costs become more affordable.…
Today it feels like everything is moving to the cloud. Even large enterprise data warehouses that many people thought would never leave the on-premise data…
There are a lot of definitions of Master Data Management out there today. “Master data management (MDM) is a technology-enabled discipline in which business and…
This is the third and final blog a part of my deep dive series for dimensional modeling. Today, I will focus on Bridge Tables in…
In this blog post, a part of my deep dive series, I will focus on Degenerate Dimensions in this post. When introducing the concept of…
In my previous post about Slowly Changing Dimensions (SCDs), I provided you an overview of the four most common SCDs types and how they are…
In my previous post about the Cloud Data Hub / Data Science Architecture, I focused on the data lake. In this post, I will be…
In my previous post about the Cloud Data Hub / Data Science Architecture, I focused on the interface between the Operational Data Store (ODS) and…
Have you ever imagined a world where transactional systems were able to focus solely on the tasks they were built to complete? Have you ever…
Data warehouses and modern cloud architectures go together better than chocolate and peanut butter. The “abilities” (scalability, flexibility, reliability, etc.) of the cloud allows one…
Traditional data warehouses were often created as custom reporting data structures using on-site servers. These on-prem servers need to have a team of DBAs, storage…
In my previous Operational Data Store (ODS) blog, we discussed how to design an ODS. In this post, I will describe how to optimize the…
Operational Data Stores (ODS) are entity-centric and should only contain data about that specific entity. When designing an ODS, you will use relational database design…
An Operational Data Store (ODS) is a powerful tool to help provide any company or organization operational reports with up-to-date 360-degree views of any and…
The definition of an Operational Data Store (ODS) is a subject-oriented, integrated, volatile collection of current data designed to support the tactical decision-making of an…
The three keys to warehouse project success are to quickly, efficiently and consistently refresh the data in the warehouse. Here are four ways to design…
You often hear “drilling down” and “drilling across” from business intelligence professionals, but what does it mean and how do you, as a data modeler,…