Data lakes are currently the hottest innovation in the field of information management. This type of data storage is touted to offer a wide range of features, but admittedly, a lot of organizations are still unfamiliar with them. This has led some to wonder if they should switch from using a data warehouse to using a data lake.
What Is a Data Lake?
A data lake usually stores all raw, unfiltered data created by your systems. At most, this type of storage is semi-structured; it only creates schema on read. This allows you to change its setup easily to suit the current needs of your organization. Real-time data replication is also available for data lakes.
On top of that, a data lake is also designed to be accessible by different parts of your organization, and it supports all kinds of data and all kinds of users. As such, a data lake is best for discovery, allowing your data scientists to connect disparate sets of data to generate new findings about your current processes. Moreover, data lakes can be also quite affordable to set up, as these only use commodity hardware.
However, these features can also lead to some disadvantages. For example, a data lake can easily turn into a data dump in the absence of descriptive metadata for the information stored in it. The same can also happen when there is no protocol set in place for its maintenance. And since the data in a data lake is not optimized, hardware performance can also take a hit.
Meanwhile, the accessibility feature of a data lake is a double-edged sword. This can result in issues with access control plus an increase in security risks.
What is a Data Warehouse?
Compared to a data lake, a data warehouse is best for day-to-day use. As its name suggests, it is more rigid and structured than a data lake, with data being processed before storage and schema created on write. As such, data can be accessed faster and easier, making it more suitable for enterprise-level strategic reporting.
However, the organized structure of a data warehouse puts it at a disadvantage. Since data must fit a specific schema before being stored, some non-traditional data types may be excluded and lost, unlike a data lake that stores everything. Data warehouses can be also be hard to reconfigure and can be quite expensive to set up in terms of the hardware needed.
Which One Should You Get?
As you can see, the choice between a data lake or warehouse will be based on the needs of your organization. On the other hand, you can also opt to have both a lake and warehouse installed. Their different functions and features can complement each other, while addressing each other’s disadvantages.
In the end, whatever your choice—lake, warehouse, or both—it is important to always have the right hardware, software, and data management methods in place. That way, all parts of your organization can access the information they need without the hassle.