Nowadays, big data is not just a trend, it’s practically a necessity to almost every business. However, one problem with big data is that without a storage and management system, it’s easy to get lost and drowned with huge volumes of information.
So how do you deal with thousands of gigabytes of data every single day? To start with, you need either a data lake or a data warehouse. They may sound similar, but understanding the differences between an enterprise-class data lake and data warehouse will not only optimize your data but also save on costs.
Data Lakes and Data Warehouses Explained
Data lakes and data warehouses are, simply put, two different data storage models. Their main difference is how the database is structured. A data warehouse is used to store rigidly-structured and organized data. Just like an actual warehouse, where everything that goes into it is packed into boxes and placed on corresponding shelves, any data stored in a data warehouse goes through a process that makes it fit the format and arrangement of the data already stored within. This is to streamline the execution of analyses, queries, reports, and dashboards that involve the data within.
This model is perfect for companies whose business-critical data come in consistent and predictable formats, as well as those that require quick reporting for business strategies. Examples of companies that can benefit from data warehouses include insurance companies, law offices, and financial organizations.
In contrast, a data lake contains a mix of all types of data – structured, semi-structured, or even raw data. These can include anything and everything, from receipts, documents, and call-center notes to social media interactions and customer feedback made through phone calls and surveys. In essence, it is a free-for-all data reservoir that accepts and preserves all data in their native format. Such a database model allows a company to preserve all the details surrounding every piece of data they store – details that could help advanced analysis efforts and create connections with other data inside the data lake, which can thus help form unexpected but still actionable business insights.
Data lakes are best suited for companies whose essential data can come in multiple or ever-changing formats, as well as those that are seeking to innovate how they deal with customers in general. The health care industry and media companies are good examples.
Key Differences Between Data Lakes and Data Warehouses
Apart from the difference in structure, there are other key aspects that differentiate a data lake from a data warehouse. Any company looking to adopt either model for their business needs will need to consider them heavily in making their final choice.
- Data lakes tend to be much cheaper as a data storage option than data warehouses. This is primarily because of the software solutions needed to keep a data lake running, which are mostly open-source and are designed to be installed on low-cost commodity hardware.
- A data warehouse contains data that is already rigidly structured. Should the company, for any reason, decide to change the structure of their data, it may prove to be costly and time-consuming. In contrast, a data lake can easily be restructured to fit the needs of developers and data scientists.
- One of the main priorities of any business is being able to adequately secure their data, no matter what form of storage they use. As data warehouses have been around for longer, there are more choices when it comes to security solutions for this type of database model. Data lakes are relatively new and therefore have fewer, but not necessarily less safe, security options.
- As a data lake preserves information in its raw form, data analysis solutions can easily find patterns and links within the data that can yield unexpected but actionable discoveries Business process pain points or hidden legal issues within the company are just a few examples. In the case of data warehouses, their structured format allows businesses to analyze the data and form strategies and solutions for ongoing and expected issues, such as an under-performing department or a lower-than-expect revenue.
Data lakes and data warehouses are non-interchangeable. And while businesses can always use both in a way that benefits them — for example, all archived data can be stored in a data lake, while all current and incoming data can be kept in a data warehouse — this is not always financially possible. Therefore, a company needs to carefully examine how they want to use their data before making a choice between the two models.