If you are looking to implement a strategy that will help you improve your decision-making, you have probably heard that one of the most important aspects of this process is the ability to analyze data. Data Lake, one Data Lakehouse or one Data Warehouse They are the right solution for your organization. In this article we will talk about Data Lake, Data Lakehouse and Data Warehouse, what are they? What are their differences? And which one is better for your company?
Data Lake, Data Lakehouse and Data Warehouse, what are they?
Data Lakes and Data Warehouses are elements that are used in the storage of Big Data, However, they are terms that they share functionalities, but they are not the same.For its part, a Data Lake is a set of raw data that does not yet have a defined purpose. On the other hand, a Data Warehouse is a data repository that has structure and has been filtered and processed for a specific purpose. Likewise, we find Data Lakehouse, which is a new way of managing data, combining some of the best elements of Data Lakes and Data Warehouses.
They are commonly confused due to their apparent similarities, but in reality the only element they have in common is the large amount of data that they can handle. It is important to distinguish them because they cater to different needs, While the Data Lake could be optimal for one company, it might not be for another, and the same goes for a Data Warehouse and Data Lakehouse.
What is Data Lake?
Data Lake is a repository where we can find large amounts of structured, semi-structured, and unstructured information. It offers a large amount of data for a variety of applications. higher performance in real time and a wide variety of capabilities Analytic.Its data is stored at the sheet level and provides a highly effective solution that can process large amounts of information without transformation.
What is Data Lakehouse?
Data Lakehouse is a New open system that allows implement similar data structures and data management functions, very similar to a Data Warehouse but with the low-cost storage which they use in Data Lakes. When combined, the data can move faster without having to access multiple systems.
What is a Data Warehouse?
Data Warehouse It is a mix of technologies and components for the strategic use of data. It collects and manages information from various sources to provide a meaningful perspective on the results. It is a process of transformation of data into information.
It stores data and folders that help organize and use the data for strategic decision-making, providing different views of your information.
The main differences between Data Lake and Data Warehouse
There are different elements that distinguish Data Lake from Data Warehouse; the most relevant ones encompass topics such as structure, user, and accessibility, among others.
- Data Lake can store data not processed and that still they have no established purpose, Whereas Data Warehouse has processed data and has a specific purpose.
- Data Lake is relatively new in the technologies of Big Data, while Data Warehouse It has been in use for decades.
- Data Lakes keeps all the data, Unlike Data Warehouse, which decides which data to include or remove.
- Data Lake uses the process of ELT (Extract Load Transform) Whereas Data Warehouse uses ETL (Extract Transform Load).
- Data Lake users are data scientists, in addition to being very accessible and up-to-date. On the other hand, Data Warehouse is mostly used by business people; it is somewhat complicated and it is complex to make changes.
- The cost of Data Lake is relatively accessible compared to Data Warehouse.
- Data Lake is ideal for those who want a in-depth analysis. Data Warehouse is for those more advanced users Operatives.
Key points of a Data Lakehouse
There are some key points that differentiate Data Lakes and Data Warehouses from a Data Lakehouse, which leads to a fusion between the two.
- Metadata layers for Data Lakes
- New engine designs that provide high-performance SQL execution in Data Lakes.
- Optimized access for Data Science and learning tools.
Data Lake vs Data Warehouse: How to choose which one is best for my company?
Considering some of the differences we may find between these tools, we have listed some questions that will further clarify your doubts and help you make the best decision regarding which one best suits your organization’s needs.
What kind of structure do the data have?
The main difference between these two terms lies in the fact that Data Lakes store unprocessed data, while Data Warehouses store processed and refined data. That is why Data Lakes need a larger storage capacity than a Data Warehouse. The risk of Data Lakes is that unprocessed data can become data without the necessary quality measures. A Data Warehouse, by containing only processed data, can be understood by a wider audience. Data Lakehouses have the ability to extract intelligence from unstructured data, making the use of multiple systems unnecessary.
What will be its purpose?
Another important point to consider when making the decision on which tool would be best for a company could be its purpose. Data Lakes, while containing a large amount of data that does not necessarily have a specific purpose, or simply data that we want to have at hand, have much less organization and filtering. While Data Warehouses store processed data that was used for a specific purpose, using the storage space only for the data necessary.
Who will be its users?
As mentioned above, the user is a crucial element in the decision-making process when choosing between Data Lakes and Data Warehouses. Data Lakes are much more difficult to manage, as not everyone can handle data without processing it, requiring a data scientist to understand and translate it. Data Warehouses only require a user who is familiar with the related topics, as it is presented in a much more graphical and understandable way for any employee. The open data format used in Data Lakehouses will make the task of analyzing the information easier for data scientists and potential engineers who are related to the system.
How accessible?
In terms of accessibility, Data Lakes do not have a structure, which makes it easy to access and change data. These changes can be made quickly because Data Lakes have few limitations. Data Warehouses are much more structured, which makes them easy to understand, but they are difficult to manipulate due to the limitations they have. Data Lakehouses have a simplified scheme, in addition to reducing data movement and redundancy, direct access to data is available for analysis tools.
In conclusion, is a Data Lake or a Data Warehouse better?
As mentioned above, each tool can meet different needs and not all companies need the same. Data Lakes are excellent for companies that do not need a large organization, require having large amounts of information without processing and their main users will be data scientists. Data Warehouses are more recommended when they will be used with various employees of the organization, they need more structure and have been filtered previously, leaving only the necessary data. Data Lakehouses are the fusion between a Data Lake and a Data Warehouse, having advantages over a multiple solution system, requiring less time and effort, with a simplified system, no need for a greater movement of data and direct access to them, in addition to the cost-effective storage of data.
There are different tools available Microsoft which can help build modern and flexible architectures using any of the three storage tools. Furthermore, platforms such as Snowflake for the creation of them.
At Intus, we can helping you build a Data Lake or Data Warehouse, depending on the needs of the organization. With aFlexible and scalable architectures, for improve data management y facilitate the analysis, through a reliable information source for the entire company.
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