Data Lake vs. Data Warehouse: The Key Differences Explained
The big data industry is expected to reach a value of $103 billion by 2023 as companies worldwide scramble to gather as much information as possible on their consumers and internal business processes. But what is driving this surge, and why are companies collecting data at such an exponential rate?
Well, the reason is simple – it’s so they can gain a competitive advantage over their rivals through the acquisition of valuable insights into their business operations and consumer target market. This allows companies to improve revenue production through enhanced decision-making and more sophisticated business strategies.
However, the biggest concern of business owners and CEOs these days is not how to acquire more data but rather what to do with the data they currently have. Data, after all, has no intrinsic value without a place for adequate storage, retrieval, and subsequent analysis.
This is why both Data Lakes and Data Warehouses have grown in popularity in recent times, but what are the distinctions between the two terms, and which is favored in certain differing situations? Let’s find out.
What is a data lake?
A Data Lake is a large pool of raw data which doesn’t yet have a defined purpose. It acts as a large repository that can store all kinds of data, including structured, semi-structured, and unstructured data.
One of the key advantages of data lakes is storing the data in its native format without any fixed limits on the account size or file. In general, data lakes are preferred by companies that need to store vast amounts of data (typically in a wide variety of formats). This data can be analyzed and integrated into the business for real-time implementation.
What is a data warehouse?
A data warehouse is also a repository, but this time the data that it holds is structured as it has already been filtered/processed for a specific purpose. This makes integrating the data much easier as the information is more readily available than in a data lake.
Data warehouses are primarily designed to gather business insights from the data. They make it much more straightforward for companies to manage, analyze, and integrate their data on many varying levels through their business operations. As the data has already been processed and organized, it can be used by a wider number of business professionals (and not just data scientists) as the data can be understood by a larger audience.
Now, it’s important to understand that not all data warehouses are made equal. In the past, most companies had to install physical warehouses on-premises. This was extremely costly, time-consuming, and required full-time employees to manage and maintain the databases. While many of the larger companies still employ this method, cloud data warehouses provide an excellent alternative with many added features.
With a cloud data warehouse, companies can enjoy improved speed and performance, improved access and integration, enhanced security, and a lower total cost of ownership. This drastically reduces the barriers to entry and makes the technology available to businesses of all sizes.
Here are a few of the key distinctions between the two data storage solutions:
1 – Data lakes store data in their native format
Raw, unprocessed data is stored in data lakes, whereas processed and refined data is stored in data warehouses. This gives data warehouses the upper hand when it comes to data storage as companies don’t have to worry about what source the data is coming from.
With a data lake, companies can keep all of their data, irrespective of its source and structure. This data is then kept in its native format and is only transformed once it is ready to be used and integrated into the business.
As a result, data lakes typically require a much larger storage capacity than data warehouses. However, in some cases, this is detrimental as the lake can become filled with low-quality data, especially if the company fails to put sufficient data governance measures in place.
2 – Data warehouses are more cost-efficient
Following on from the previous point, data warehouses are generally much cheaper to run as they only store processed data. This saves on storage space (which can be quite costly) as they do not have to maintain data they will never use.
3 – Data scientists are required for data lakes
Because the data is unstructured and unprocessed, data lakes are more difficult to navigate. Engineers must set up and manage data lakes for this purpose, and they must be used anytime information from the lake is needed. When integrating the data into the company, this has the potential to cause a substantial bottleneck and could reduce the availability of real-time insights from new data.
Conversely, data warehouses are suitable for operational users since the data is neatly structured, easy to comprehend, and requires a lower level of data science and programming expertise or experience to use.
If you operate a business and are debating between a data lake and a data warehouse, it’s critical to first evaluate your company’s needs so you can determine which solution would best meet your requirements. In general, firms that need to store huge amounts of raw data from various sources should use a data lake; nevertheless, you’ll need data scientists on hand to set up and manage the system.
On the other hand, data warehouses are appropriate for organizations who wish to store data that already has a purpose. Structured data is easier to comprehend when stored and processed, decreasing the amount of expertise necessary for employees to use and incorporate it into business operations.