Technology is changing at an exponential rate, and in such a rapidly evolving landscape, it is easy to get confused by the seemingly endless terminology and acronyms that come with those changes. So in this post, I have attempted to explain the difference to between two big buzz words being thrown around the traps.
Data Lake and Data Warehouse are now commonly used terms within the world of big data and are often used as if they are interchangeable. However, they are not, and in fact, it’s the core function of each that differentiates them.
A data lake is for the storage of unstructured, unorganised raw data; and its purpose and audience is undefined.
Data warehouse stores processed, structured data for a specific purpose and audience.
Because of these differences, it is essential to understand why you are storing data to decide which is right for your business. They do not need to exist in isolation or as a single repository; however, most organisations will have a single data lake but might have multiple data warehouses.
Unfortunately, the strength of each is also generally the weakness of the other and as the world shifts to a customer experience economy, most organisations will discover they need both to meet the current and future demands of their internal and external stakeholders.
However, as access to cloud storage and computing becomes more affordable, even small businesses have the opportunity to leverage the power of almost infinite storage and compute power.
The key is to make sure you understand the “Why”. if you know that then you can articulate what you need for your business and that is what will guide your decisions.
When it comes to navigating Google Ads with new privacy, browser, and regulation changes, Google developed their Customer Match tool to help bridge the gap, enabling organisations to better leverage their 1st party data for advertising campaigns.