Businesses today increasingly rely on data analytics to provide insights, identify opportunities, make important decisions, and innovate. Every day, a large amount of data (a.k.a Big Data) is generated from multiple internal and external sources that can and should be used by businesses to make informed decisions, understand their customers better, make predictions, and stay ahead of their competition.

difference between data lake and warehouse

For effective data-driven decisions, securely storing all this data in a central repository is essential. Two of the most popular storage repositories for big data today are data lakes and data warehouses. While both store your data, they each have different uses that are important to distinguish before choosing which of the two works best for you.

What is a Data Lake?

With large amounts of data being created by companies on a day-to-day basis, it may be difficult to determine which method will be most effective based on business needs and who will be using the data. To visualize the difference, each storage repository functions similarly to how it sounds. A data lake, for example, is a vast pool of raw, unstructured data. One piece of information in a data lake is like a small raindrop in Lake Michigan.

All the data in a data lake is loaded from source systems and none is turned away, filtered, or transformed until there is a need for it. Typically, data lakes are used by data scientists to transform data as needed. Data warehouses, on the other hand, have more organization and structure – like a physical warehouse building. These repositories house structured, filtered data that is used for a specific purpose. Still both repositories have many more layers to them than these analogies suggest.

To learn more about data lakes and their benefits, specifically with AWS Lake Formation, visit this post.

What is a Data Warehouse?

A data warehouse is the traditional, proven repository for storing data. Data warehouses use an ETL (Extract, Transform, Load) process, compared to data lakes, which use an ELT (Extract, Load, Transform) process. The data is filtered, processed, and loaded from multiple sources into the data warehouse once its use is defined. This structure, in turn, allows for its users to run queries in the SQL environment and get quick results. The users of data warehouses tend to be business professionals because once the data is fully processed, there is a highly structured and simplified data model designed for data analysis and reporting.

While the structure and organization provided by a data warehouse is appealing, one major downside you might hear about data warehouses is the time-consuming nature of changing them. Since the use of the data in data warehouses is already identified, the complexity of changing the data loading process for quick reporting takes developers a lengthy amount of time. When businesses want fast insights for decision-making, this can be a frustrating challenge if changes to the data warehouse need to be made.

In terms of cost, data warehouses tend to be more expensive in comparison to data lakes, especially if the volume of data is large. This is because of the accessibility, which is costly to make changes to. However, since a data warehouse weeds out data outside the identified profile, a significant amount of space is conserved reducing overall storage cost.

What are the Benefits of a Data Warehouse?

 While data lakes come with their own benefits, data warehouses have been used for decades compared to data lakes, proving their strong reliability and performance over time. Thus, there are several benefits that can be derived using a strong data warehouse, including the following:

  • Saves Time: With all your data loaded and stored into one place, a lot of time is saved from manually retrieving data from multiple sources. Additionally, since the data is already transformed, business professionals can query the data themselves rather than relying on an IT person to do it for them.
  • Strong Data Quality: Since a data warehouse consists only of data that is transformed, this refined quality removes data that is duplicated or inadequately recorded.
  • Improves Business Intelligence: As data within a data warehouse is extracted from multiple sources, everyone on your team will have a holistic understanding of your data to make informed decisions.
  • Provides Historical Data: A data warehouse stores historical data that can be utilized by your team to make future predictions and thus, more informed decisions.
  • Security: Data warehouses improve security by allowing certain security characteristics to be implemented into the setup of the warehouse.

Should I use a Data Lake or a Data Warehouse?

Due to their differences, there is not an objectively better repository when it comes to data lakes and data warehouses. However, a company might prefer one based on their resources and to fulfill their specific business needs. In some cases, businesses may transition from a data lake to a data warehouse for different reasons and vice versa. For example, a company may want a data lake to temporarily store all their data while their data warehouse is being built. In another case, such as our experience with McDonald’s France, a company may want a data lake for an ongoing data collection from a wide ranges of data sources to be used and analyzed later. The following are some of the key differences between data lakes and data warehouses that may be important in determining the best storage repository for you:

  • User type: When comparing the two, one of the biggest differences comes down to who is using the data. Data lakes are typically used by data scientists who transform the data when needed, whereas data warehouses are used by business professionals who need quick reports.
  • ELT vs ETL: Another major difference between the two is the ELT process of data lakes vs ETL process of data warehouses. Data lakes retain all data, while data warehouses create a highly structured data model that filters out the data that doesn’t match this model. Therefore, if your company wants to save all data for later use – even data that may never be used – then a data lake would be the choice for you.
  • Data type: As the more traditional repository, the data in data warehouses consists of data extracted from transaction systems with quantitative metrics and attributes to describe them. On the other hand, data lakes embrace non-traditional data types (e.g. web server logs, social network activity, etc.) and transforms it when it is ready to be used.
  • Adaptability: While a well-constructed data warehouse is highly effective, they do take a long time to change if changes need to be made. Thus, a lot of time can be spent getting the desired structure. Since data lakes store raw data, the data is more accessible when needed and a variety of schemas can be easily applied and discarded until there is one that has some reusability.

Furthermore, different industries may lean towards one or the other based on industry needs. Here’s a quick breakdown of what a few industries are using most commonly to store their data:

  • Education: A popular choice for storing data among education institutions are data lakes. This industry benefits from the flexibility provided by data lakes, as student grades, attendance, and other data points can be stored and transformed when needed. The flexibility also allows universities to streamline billing, improve fundraising, tailor to the student experience, and facilitate research.
  • Financial Services: Finance companies may tend to choose a data warehouse because they can provide quick, relevant insights for reporting. Additionally, the whole company can easily access the data warehouse due to their existing structure, rather than limiting access to data scientists.
  • Healthcare: In the healthcare industry, businesses have plenty of unstructured data including physicians notes, clinical data, client records, and any interaction a consumer has with the brand online. Due to these large amounts of unstructured data, the healthcare industry can benefit from a flexible storage option where this data can be safely stored and later transformed.

There is no black-and-white answer to which repository is better. A company may decide that using both is better, as a data lake can hold a vast amount of structured and unstructured data working alongside a well-established data warehouse for instant reporting. The accessibility of a good warehouse will be available to the business professionals of your organization, while data scientists use the data lake to provide more in-depth analyses.

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Choosing between a data lake and data warehouse, or both, is important to how your data is stored, used, and who it is used by. If you are interested in setting up a data lake or data warehouse and want advisory on your next steps, 2nd Watch has a highly experienced and qualified team of experts to get your data to where it needs to be. Contact us to talk to one of our experts and take your next steps in your cloud journey.

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