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Comparing Data Silos, Lakes and Warehouses

Most of us can imagine what a modern data center might look like on the inside. Generally speaking, these facilities are home to large-scale servers that store massive amounts of data. But these servers aren't just holding your personal backup files. Instead, these facilities are responsible for maintaining the large archives, repositories and network protocols that power the internet as we know it today.

But what about data silos, data lakes and data warehouses? If we already have a description for the modern data center, what are these other terms for? What are they describing?

Data Silos

In a broad sense, data silos contain data that may be easily accessible by one group but not immediately available to other groups within an enterprise. For example, data generated by a company's HR department by stored on servers that are accessible only to those within the HR department.

As you can see, data silos pose serious issues when it comes to data monitoring and analysis. If this data isn't available to your analysts, how are you expected to take into consideration? Not only can data silos hamper a company's day-to-day productivity, but they can make it impossible to gain 360-degree insight into your organization's data flow.

In most cases, it's best to avoid data silos in favor of a data lake or data warehouse.

Data Lakes

Modern data lakes are centralized repositories – usually an individual server – that stores both structured and unstructured data. As you might have guessed, this environment works great when it comes to data monitoring and analysis.

But there are some challenges here, too. Because data lakes often serve as a catch-all for data of all types, it can be difficult to properly organize and secure them. There are also concerns surrounding data governance and accessibility that need to be addressed, too.

Data Warehouses

Similarly to data lakes, data warehouses are also centralized repositories – typically in the form of individual servers – that store data. Because the data is stored in a single location, it lends itself well to ongoing data management, analysis and security.

However, the biggest difference between data warehouses and data lakes is the fact that the information stored within a data warehouse is typically processed in some fashion prior to storage. This eliminates irrelevant data and data redundancy while maximizing the integrity of the entire server, or data warehouse.

Data warehouses also come in many different varieties. In this way, they can be used for various purposes. A simple data warehouse, for example, just serves as a basic data repository. In this example, the data warehouse is nearly identical to a data lake.

When used with a staging area, however, a simple data warehouse now has a separate area to clean, process and organize data before it's transferred to long-term storage. Data warehouses can also be used in a hub-and-spoke configuration, too, where they help to connect the data created by multiple users. Finally, data warehouses can be used as sandbox environments for testing and analyzing new datasets.


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