Tomorrow (Tuesday (8/10/21) I will be on a podcast for SaxonGlobal called “The Alphabet Soup of Data Architectures” where I will talk about the modern data warehouse, data fabric, data lakehouse, data mesh, and more. I hope you can check it out live here (I’ll post a link here when the recording is available).

I have also created two more presentation decks:

Data Lakehouse, Data Mesh, and Data Fabric

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs. Check it out here.

Data Warehousing Trends, Best Practices, and Future Outlook

Over the last decade, the 3Vs of data – Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:

– Choosing the best solution – Data Lake vs. Data Warehouse vs. Data Mart
– Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
– Step by step approach to building an effective data warehouse architecture
– Common reasons for the failure of data warehouse implementations and how to avoid them

Check it out here.