Speaking of journeys, let’s take a little trip down memory lane to understand the challenges driving the idea of a Data Lakehouse. The starting point is everything when it comes to the convergence of two platforms or products, as that starting point informs your view of where you’re going, your perception of the trip, and your sense of whether or not you’ve ended up where you expected when you’ve finally arrived at the journey’s end. But in the real world this isn’t entirely true. In simple mathematical terms if A + B = C then B + A = C. And that distinction is important because where you start in your convergence matters. This is important because the question is less how a Data Lakehouse differs from a Data Lake or Data Warehouse and more how is it more like one or the other. Let’s start with the most obvious and a dead giveaway from the name: a Data Lakehouse is a combination of commodity hardware, open standards, and semi-structured and unstructured data handling capabilities from a Data Lake and the SQL Analytics, structured schema support, and BI tool integration found in a Data Warehouse. Needless to say, there were lots of questions for Bill, but there was one that I thought deserved focused discussion here: What is a data Lakehouse, and how is it different from a Data Lake or Data Warehouse? Last week, I had the privilege of hosting Bill Inmon, considered the father of Data Warehousing for a Webinar on Modern Data Integration in Cloud Data Warehouses. The diagram below shows the relationship between the four predecessors and the Data Analytics Hub (it will look familiar to you if you read installment two of this series). Further, a Data Analytics Hub is built to be accessible to all users on a cross-functional team (even a virtual one). A Data Analytics Hub brings together data aggregation, management, and analytics support for any data source with any BI or AI tool, visualization, reporting or other destination. Why? Because, in essence, a Data Analytics Hub, takes the best of all these integration, management, and analytics platforms and combines them in a single platform.
Given the titular proximity of Analytics Hub to Data Analytics Hub, it only made sense to clarify that an Analytics Hub remains as incomplete a solution for modern analytics as does a Data Lake, Hub, and Warehouse. I also take a moment to examine a fourth related technology, the Analytics Hub.
#Definition if analytical sandvox series
In the second blog in this series - What is a Data Analytics Hub?- I introduce the term Data Analytics Hub to describe a platform that takes the optimal operational and analytical elements of Data Hubs, Lakes, and Warehouses and combines them with cloud features and functionality to address directly the real-time operational and self-serve needs of business users (rather than exclusively IT users). In comparing these three platforms, it becomes clear that all of them meet certain critical needs, but none of them meet the needs of business end-users without significant support from IT.
In the opening installment of this blog series- Data Lakes, Data Warehouses and Data Hubs: Do we need another choice? I explore why simply migrating these on-prem data integration, management, and analytics platforms to the Cloud does not fully address modern data analytics needs.
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