Both data warehouses and data lakes are essential data storage solutions commonly used across the business world.
Despite the similar-sounding names for both terms, however, the two are not interchangeable.
These are three of the most important differences between data lakes and data warehouses, as well as how businesses use the two to organize their big data.
Data Warehouses Vs. Data Lakes
- The Structure of Stored Data
- The Purpose of Stored Data
- The Security of Stored Data
1. The Structure of Stored Data
A data warehouse stores data much like an actual warehouse. After important information is ingested into the warehouse, the data will be extracted, cleaned, and made consistent with other types of data in the system.
This consistency and organization will make the data much more accessible to conventional analysis approaches that will be used by business analysts and professionals in similar roles.
This organization can make data harder to access or use, however, especially if you need to use many different types of data at the same time.
Data engineers, data scientists, and machine learning developers often prefer data lakes, in part due to the lack of pre-existing structure and data pre-processing.
With a data lake, the data scientist or ML expert does not have to hunt around for the information they want to use, or for applications with interesting associated data. Instead, all the data is accessible all the time.
If they need to structure their data for an ML algorithm or similar application, they can use an approach like schema-on-read that will structure the data as it is used.
Many businesses are also beginning to use the emerging data lakehouse architecture for storage.
The lakehouse combines elements of the warehouse and lake approach to data storage and structure. Lakehouses use various approaches to provide a combination of structure and data availability, potentially making the storage solution useful for both conventional and advanced analytic approaches.
Data warehouses, lakes, and lakehouses may be operated by the business that owns them, or by companies that offer data storage solutions, due to the high potential cost of maintaining in-house data storage.
2. The Purpose of Stored Data
The same organization may maintain both data warehouses and data lakes, but it’s likely that these two data solutions will be used for vastly different purposes.
Data warehouses are most useful in non-big data analytics and business reporting. Data in the warehouse is normalized and consistent, making it easy for a business analyst to quickly compare distinct data points — like the sales numbers for different products, the function of those products, and their target audience.
The information in data lakes is not processed for a specific purpose, and a combination of structured, unstructured, and raw data may be present. This combination of information makes the data useful for the training of AI models or big data analysis.
Businesses that are investing in advanced predictive capabilities often maintain data lakes because they’re necessary for these analytic technologies.
For key decision-makers, both types of data storage can be invaluable. More and more often, CFOs and similar executives are writing about the value of real-time data when it comes to making highly informed decisions.
Data warehouses aren’t well-suited to real-time analytics, but the structure and organization they provide may still be essential for a business’s overall analytics strategy.
3. The Security of Stored Data
Data security is a major concern across the business world. Cyber-attacks are becoming both more frequent and more expensive, making data security a top priority for businesses that store large amounts of information.
Data lakes are often less secure than data warehouses simply due to how lakes are used. In most cases, many different users, applications, and third parties will require access to the data lake.
The different data flows that lakes and warehouses use can also affect security. Data lakes typically use the ELT (Extract, Load, Transfer) workload, while data warehouses typically use ETL (Extract, Transfer, Load).
ETL loads data first into a staging server before the target system, while ELT loads data directly into the target system.
Transferring the data before it is loaded is often necessary when storing available raw, unprocessed data would create security concerns.
For the most part, however, potential security issues won’t play a major role in whether or not a business chooses to maintain a data lake. If a business has determined it needs a data lake for some purpose, it’s likely that a data warehouse won’t be a suitable substitute. Instead, the potential security issues with data lakes will inform the business’s cybersecurity strategy.
How Businesses Use a Data Warehouse vs. Data Lake
Both data warehouses and data lakes play valuable roles in data storage and analytics. Warehouses provide structure to data that is useful for certain, more conventional approaches to analysis.
Data lakes contain large amounts of unstructured data, making them useful for big data analysis and AI.
One company may rely on both data warehouses and data lakes to effectively analyze available information.
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