5 Key Differences Between Data Warehouses And Data Lakes

Data warehouse and data lake are two solutions used to store large amounts of data from multiple sources. But one cannot use the terms interchangeably.

A data warehouse stores highly structured data that is used to support specific business analytical needs. Business users can readily use the data to gain insights and make decisions about their line of business. Google Big Query is an example of a data warehouse.

A data lake extracts data from different sources and stores it as is. When the need arises, the data is transformed for use. Data engineers and scientists can leverage such data for applications like machine learning.

Azure Data Lake is a data storage solution offered by Microsoft. If you need help with any of Microsoft’s software and tools, you can consult with a third-party IT help desk service provider.

If you are confused which data storage solution will work best for your organization, the article will present you with five differences that can help you decide.

Let us look at some integral differences between data warehouses and data lakes.

• Type of Data Stored
• Data Processing
• Ease of Use
• Users
• Cost

A data warehouse stores processed and refined data. The data is cleaned and structured based on existing business needs.

A warehouse typically contains quantitative data derived from transactional systems. Advancements are being made to store non-traditional forms of data, but they can be expensive and cumbersome.

Business leaders can use such quantitative data for strategic analysis and reporting.

On the other hand, a data lake stores unprocessed and raw data. The unstructured data collected may be used immediately or stored for future use.

A data lake can include images, text, server logs, emails, documents, and audio and video files. You can store data in the lake indefinitely and process it as and when required.

Data warehouses follow the extraction, transform, and load method of handling data. First, data is extracted from different sources. Then, it is cleaned and scrubbed per a predefined requirement, and the structured data is loaded. The data is ready for business use right away.

Data lakes follow the extraction, load, and transform method of handling data. First, the data is extracted from different sources and loaded as is. Then, when the need arises, the data is structured for use.

Data processing is one of the key differences as it affects the adaptability and speed of generating results. For example, the development cycle can be long and complex as the data needs to be transformed before it is loaded. As a result, it can take a significant amount of time and resources.

On the other hand, raw data is more flexible and easier to process. You are not limited by predefined data structures and can explore the data creatively per your requirement.

In the case of data warehouses, the schema is defined according to the business objective, So the end data is structured and easier to understand. Tasks like data visualization and BI reporting become easy.

But the predefined schema can sometimes prove to be limiting. For example, you do not have the creative freedom to source data and customize reporting. Thus, maintaining and operating a warehouse can sometimes be complex.

In the case of data lakes, the schema is defined after the data is loaded. So, the processing time is much less. Raw data is comparatively flexible and easier to work with and analyze.

With data lakes, you can explore the data at your own pace and according to your unique requirements. In addition, you have access to a much larger dataset. But if the users do not have the expertise or do not want to do the work, the data lake structure can overwhelm them.

The problem with data lakes is that they can turn into data swaps. Storage and maintenance can be issues if your organization generates too much data without proper governing rules.

Data warehouses are suitable for applications like BI and reporting. It is suited for a larger audience of business professionals. They can use structured data to generate reports, present and trade intelligence.

Unstructured data is not suited for business users as such data is open to interpretation. It can cause confusion and defeat the purpose of the reports used for decision-making.

Data lakes can be used for applications like experimental analysis and machine learning. Professionals like data engineers can best use the structure to gain insights. They will have the technical expertise and experience in dealing with non-traditional data sets.

Data warehouses are a more expensive data storage option. They are complex in nature and take time and effort to manage.

You must take your time in the planning stage and understand the project’s scope. If you rush into the coding part, you may be left with an inflexible and rigid architecture that does not fulfill business objectives.

Data lakes are easier to manage and have relatively low operational costs than warehouses. One can build data lakes on low-cost servers and or cloud-based storage solutions. In addition, the underlying architecture makes it easier to scale than traditional databases.

According to Statista, at a global level, data creation is expected to reach over 180 zettabytes by 2025. If you want to leverage data, understand what storage solution would work for your organization.

If your end use of data is predefined, for example, you want to study customer journeys based on quantitative data; then a data warehouse is a better option.

Consider a data lake if you have a large data set and are storing data for applications like machine learning.

Budget, processing speed, and ease of use are other factors you should consider.

Also, know that these solutions are not mutually exclusive. For example, you can leverage the data lake’s storage capability and combine it with data warehouse features like indexing and querying.

To summarize the differences, a data warehouse stores structured data, the schemas are predefined, and it follows the ETL processing method. Business-end users can use warehouses for applications like BI reporting.

A data lake stores unstructured data, the schema is defined post the data is loaded, and it follows the ELT processing method. Experienced data scientists can use data lakes for applications like machine learning.

If you need help with data storage support solutions, consult with an MSP to know more.