After implementing dozens of high-profile analytics and cloud data warehousing projects we discovered that the “holy grail” of analytics for managing all types of data workloads that would provide to value at all levels of any organization was the combination of object storage (aka data lake storage), a battle testing Data Warehouse or Data Vault, and versatile data analytics.  

This resulting in us coining the phrase “Data LakeHouse” which now hundreds of organizations and software development teams around the world use in varying degrees of accuracy.   As thought-leaders our vision has always been bigger than the parts of this reference architecture and more on the outcome of correctly configuring the parts in any organization so that the whole is greater than the sum of the parts. As the natural progression to enable this new found concept DataLakeHouse.io was born. 

At the center of every data strategy one of the first components is just getting data in a single location or repository so it can be centralized so all data is accessible. This used to be a time consuming process before DataLakeHouse.io and it is where we started.  Then we took to validating data from common source systems to eliminate another area of frustration that we heard from our customers. And lastly, workloads and process automation for key operations were developed and still stand to deliver value on a daily basis to all of our customers such as Avallano, RBC, Stripe, Aquicore, Bradford, etc.