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Let’s cut right to the chase, you are reading this blog because you are looking for expertise on storing data for your current needs. I will discuss the definition of and sample use cases that I have seen for data warehouses, lakes, hubs, and vaults. The differences between them are subtle, but they all serve a different purpose in the data world today.
A data warehouse is a consolidated, structured repository for storing data assets. Data warehouses will store data in one of two ways: Star Schema or 3NF, but these are only fundamental principles in how you would like to store your data model. We have seen, advised, and implemented both principles (in addition to the snowflake schema which is a variation of the star schema in my mind), but the one major flaw is that everything must be strictly defined (both in
The most common use cases for creating and using a data warehouse are to consolidate data and answer a business related question such as: How many users are visiting my product pages from North America? This ties the information you are receiving from your end users with a business question that needs to be answered from a structured data set. This is what most would identify as the cookie cutter business intelligence solution.
There is an alternative approach that is becoming more popular, especially when you are talking about cloud and more powerful warehouses. Organizations are adopting the ELT approach where they will “stage” their data in the warehouse (such as HP Vertica), and then let the power of the database perform the traditional transformation. Essentially, you are performing the most expensive operations with the system where you have more resources.
A data lake is a term that represents a methodology of storing raw data in a single repository. The type of data
The use cases we see for creating a data lake revolve around reporting, visualization, analytics, and machine learning.
Here is the architecture we see evolving:
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A data hub is a centralized system where data is stored, defined, and served from. I like to think of a data hub as a hybrid of a data lake and a database warehouse because it provides a central repository for your applications to dump data, but adds a level of harmonization at ingest so the data is indexed and can easily be queried. Please note that this is not the same as a data warehouse architecture because the ETL processing is merely for indexing the data you have rather than mapping it into a strict structure. The challenge comes in when you have to implement the data hub and how can you harmonize all of your siloed data sources.
In general, we see the same use cases for a data hub as we would for a data lake: reporting, visualization, analytics, and machine learning.
A data vault is a system made up of a model,
The glaring use case to me is one where you are auditing data for any reason: banking, security, logistics, or a number of other reasons why you are auditing data of your systems. Let’s say you decide that you need to update your security model to include additional fields and new applications in your enterprise. Using a data vault, you are able to checkpoint the time when you made the security model changes, update your infrastructure with your changes (and all associated applications), and the business team would continue receiving the full view of historical and current information regarding the audit trail.
I hope that you have learned a little bit about how we see each of these data models as well as seeing the value in each of them. There is