Data Visualization

Data Storage Explained

Although data storage is generally considered to be a singular solution, handled in a traditional database, that is, in fact, not the case. There are different methods for storing data such as in a database, data warehouse, or data lake. Too often, particularly to a person who may not be tech literate, data lakes and data warehouses are thought of as the same thing. But the truth is, they’re very different, and here’s why.

Explaining database, data lake, and data warehouse

Firstly, let’s define what a database is. A database is a form of electronic storage for an organized collection of structured information. Databases are typically controlled by database management systems or DBMS, and the combined data, DBMS, and any other associated applications are collectively referred to as a database system.  

One step up from a database is a data warehouse, which is essentially a large storage system for data that is accumulated from multiple different sources. Data warehouses can help companies or organizations become more efficient, and are popular amongst mid-size or larger businesses, particularly regarding the sharing of data across team or department databases.

Lastly, we have the data lake which is basically a massive storage receptacle with the ability to hold huge amounts of original, raw data. Data lakes have one huge advantage over data warehouses, they are decidedly more flexible. They are typically favored by engineers or data scientists, or others who are actually building data warehouses for companies. Data kept in a data lake is raw, unstructured, and unorganized, meaning that smaller organizations generally won’t have the need to use a data lake.

Expanding the key differences

The biggest difference in terms of actual data is that databases and data warehouses can only store structured data. All three types of storage can usually handle hot and cold data, or though large amounts of cold data are typically best suited to data lakes, where latency will not be as much of an issue. Before data can be loaded into a database or data warehouse, it must have some form of structure, known as a schema-on-write. However, while a data lake can store raw, unstructured data, it must be structured before it can be used, known as a schema-on-read. For this reason, schema-on-write processing allows for a much faster execution of queries because the data is already loaded in a strict format.

However, there is also a significant cost difference between the two solutions, and this is where data lakes come out on top. Data warehouses are typically costly, particularly if large volumes of data need to be stored. Data lakes are a much more cost-effective solution, primarily because they are usually open-source meaning that the licensing is free.

Data lakes are also more agile than data warehouses, but this is not just a pro, it’s a con too. Because of this agility, they’re decidedly more difficult to work with than data warehouses, which is why they are primarily used by data experts. This is also why the average organization is better off utilizing a data warehouse. Data warehouses are also a more secure option than, having been around for a much longer period of time.

Some Words of Caution About Data Lakes

There is a reason why data lakes are the less common option, particularly among those who are not experts in the field. Here are a few key points to be aware of when considering data lakes:

  • In a data lake, you can store absolutely anything without question, which makes drawing value from useful data more difficult.
  • Data lakes accept anything, which means they don’t filter out risky data. Storing any and all data regardless of its origin increases the likelihood of risk for your organization.
  • Data lakes don’t prioritize data, which increases complexity and can drive up company costs trying to sort through the confusion and prioritize data.
  • If you’re not a data pro, data lakes are messier, more time-consuming, and less efficient.

There is no silver bullet solution to databases, data warehouses, and data lakes. The key thing to remember is it all boils down to what best suits your individual organization. Databases are the easiest for everyone to use, but they may not provide enough for larger organizations. Data warehouses are certainly the best solution for mid to large size companies that need plenty of storage but don’t want to deal with the debacle that comes with a data lake. Data lakes have their pros but are really only efficient if you’re a data expert, or your company is fortunate enough to have quick and easy access to a data expert.

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