I often work with organizations that have a need to move data out of their transactional operational systems to a repository in real-time. These organizations usually have a need to reduce query, reporting, and analytics loads on operational systems while increasing the real-time query and analytics applications to make better data-driven decisions. This operational data […]
Chuck Brooks
Dr. Chuck is a Senior Data Strategist / Solution Architect. He is a technology leader and visionary in big data, data lakes, analytics, and data science. Over a career that spans more than 40 years, Dr. Chuck has developed many large data repositories based on advancing data technologies. Dr. Chuck has helped many companies become data-driven and develop comprehensive data strategies. The cloud is the modern ecosystem for data and data lakes. Dr. Chuck’s expertise lies in the Google Cloud Platform, Advanced Analytics, Big Data, SQL and NoSQL Databases, Cloud Data Management Engines, and Business Management Development technologies such as SQL, Python, Data Studio, Qlik, PowerBI, Talend, R, Data Robot, and more. The following sales enablement and data strategy results from 40 years of Dr. Chuck’s career in the data space. For more information or to engage Dr. Chuck in an engagement, contact him at chuck.brooks@perficient.com.
Blogs from this Author
The Data Lake and Data Zones
I believe there is confusion about what a data Lake is based on a 10-year-old definition that is not accurate anymore so, in this post, I wanted to provide some clarity of what a Data Lake is in today’s technology terms. A data lake is a centralized repository that allows you to store all your […]
Data Lake, Data Warehouse, Data Lakehouse?
I have read several articles that explain that the Data Lake, Data Warehouse, and Data Lakehouse are different repositories and that you may need several or all of them. Frankly, I don’t agree, and I thought it was time to provide a different point of view. In this post, I will explain why I believe […]
From Data Swamp to Data Lake: Data Zones
This is the final blog in a series that explains how organizations can prevent their Data Lake from becoming a Data Swamp, with insights and strategy from Perficient’s Senior Data Strategist and Solutions Architect, Dr. Chuck Brooks. Read the previous blog, here. In the first article in this series, I explained the five components necessary […]
From Data Swamp to Data Lake: Data Classification
This is the fourth blog in a series that explains how organizations can prevent their Data Lake from becoming a Data Swamp, with insights and strategy from Perficient’s Senior Data Strategist and Solutions Architect, Dr. Chuck Brooks. Read the previous blog, here. In the first article in this series, I explained the five components […]
From Data Swamp to Data Lake: Data Quality
This is the third blog in a series that explains how organizations can prevent their Data Lake from becoming a Data Swamp, with insights and strategy from Perficient’s Senior Data Strategist and Solutions Architect, Dr. Chuck Brooks. Read the previous blog, here. In the First article in this series, I explained the five components necessary […]
From Data Swamp to Data Lake: Data Catalog
This is the second blog in a series that explains how organizations can prevent their Data Lake from becoming a Data Swamp, with insights and strategy from Perficient’s Senior Data Strategist and Solutions Architect, Dr. Chuck Brooks. Read the first blog, here. In the first article in this series, I explained the five components […]
From Data Swamp to Data Lake
For several years now I have heard people that wanted to slow the progress of companies becoming data-driven use the term “Data Swamp”, usually without much understanding of what a Data Swamp is. In this series of blogs, I will define what a Data Swamp really is and why it may not be a […]
The Star Schema is Obsolete, Moving to the Cloud
This is the third and final blog in this series, to read the previous blog click, here. In the first post, I urged you to stop using star schema dimensional models because they were obsolete. In the second post, I elaborated on why star schema dimensional models were obsolete using Ralph Kimball’s own reasons […]
The Star Schema is Obsolete, Reduced Cost, and Better Performance
This is the second blog in the series. Read the first blog, here. In my first blog, I stated that the star schema and dimensional modeling are obsolete, and I urge you to stop using them. I explained that in the first chapter of The Data Warehouse Toolkit Ralph Kimball explained that dimensional modeling […]
The Star Schema is Obsolete
If you are still using a star schema and dimensional modeling to build your data warehouse / Data Lake, I implore you to reconsider. This has been a contentious subject in the past, but when building modern data environments It is important to me to cover this topic in detail. Ralph Kimball introduced the industry […]
Becoming Data Driven: Increase Data Knowledge and Literacy
This is the fourth blog in a series that dives into how organizations become data-driven, with insights and strategy from Perficient’s Senior Data Strategist and Solutions Architect, Dr. Chuck Brooks. You can read the other blogs in the series via these links: first blog, second blog and, third blog. A data-driven organization is one […]