Which data warehouse option is best for your company? Learn more about the benefits of data lakes and data warehouses.
In the era of big data, organizations are increasingly thinking about how to store and use their data efficiently. The most common data storage options are data warehouses and data lakes. Are you still unsure whether you should choose one or the other when it comes to your company’s data? We can help you make the right choice.
Definitions of a Data Lake and Data Warehouse
More specifically, the term “data lake” refers to a facility for storing raw data, while the term “data warehouse” refers to a facility for storing structured data.
Data Warehouse
A data warehouse is a collection of information that has been filtered and organized to perform a specific function. This data is functional and structured. Therefore, a data warehouse is much larger than a database. A database stores only the most recent information about the activity, which must be updated in real-time.
Data Lake
A data lake is a collection of raw data that is stored in large quantities for no particular reason. In other words, there was no need to process and clean this data before storing it. Data can be not only unstructured but also semi-structured or structured.
What Is the Difference Between a Data Lake and A Data Warehouse?
Both can store large amounts of data and contribute to a robust BI architecture. However, different data warehouses are suited for very different customers, companies, and purposes.
#1. Data Structure
Data lakes store all types of data: structured, semi-structured, and unstructured. In other words, data is pulled directly from source systems.
In contrast, a data warehouse stores only structured data, which is organized into data forms for particular purposes. Therefore, the data warehouse stores cleansed and modified data.
#2. Purpose of Data
Data stored in a data lake has different purposes. They may be stored for later use.
In contrast, data in a data lake is used and stored for specific purposes. For example, to power a marketing team’s dashboard and facilitate the creation of weekly reports.
Thus, the benefits of a data lake are more long-term, while the benefits of a data warehouse are more medium-term. The data warehouse will undoubtedly need to adapt to the changing needs of the organization over time.
#3. Users
Users who lack the technical knowledge to work with raw data have difficulty using data lakes. Users with advanced statistical expertise and agency data scientists utilize them.
On the other hand, 80% of business users are operational users (marketing, product, digital, sales teams, etc.) who are the focus of the data warehouse. This is because they can view the specific KPIs and data sets they need with minimal technical knowledge.
#4. Accessibility
Because of the lack of structure, a data lake is more straightforward to process. However, changes to a data lake are more costly and complex. In fact, they require modifications to the existing structure.
#5. Storage
All data in a data lake is stored in raw form and is only modified when necessary. Therefore, some data is never used. As a result, a data lake also requires a lot of storage space, which is more expensive than a data warehouse.
A data warehouse stores various sets of business data that support a company’s analysis and strategic decisions. Clean and structured data is stored here. In addition, this data is historical and reflects the multiple values that data has acquired over time.
#6. Creating Models
The data model can be defined after the data has been imported into the data lake. In the case of a data warehouse, it is the other way around. The data schema is defined before the data is integrated into the project.
Data from data warehouses is more efficient, more secure, and easier to integrate. However, since the data schema is determined in advance, sizing a data warehouse becomes a challenge.
How do you choose between a data warehouse and a data lake?
Here, In A Nutshell, Are the Main Advantages of These Two Solutions
Benefits Of Data Warehouses
- The user’s data needs determine the structure and design of a data warehouse.
- Business users with limited technological knowledge about data warehouses can efficiently utilize them. They are the largest group of business users.
- As compared to a data lake, less storage space is required, which reduces maintenance costs.
Advantages of Data Lake
- Since a data lake is unstructured, it can be easily customized to meet the future needs of the company and its customers.
- Compared to a data warehouse with a predefined structure, analysts can perform multiple analyses of the data.
- Unstructured data is ideal for machine learning and predictive analytics and can answer a wide range of questions.
Finally, we recommend that you prioritize the needs of your employees and potential users.
