Data lakes and data warehouses can be considered solutions or tools for storing information. Although both are functional and effective, there are certain differences to observe. In order to transform data practices, understanding how data warehousing differs from a data lake is important. This comprehension can help professionals like engineers, managers, data scientists, etc., to identify which solution is more apt as per the intended purpose to fulfill. Majorly, one should be aware of the use cases, analytic functioning, ELT processing, etc., associated with both tools. Further, knowing the specific benefits linked to each of these can assist in achieving different purposes in the best ways.
What is a Data Lake?
In simple terms, data lakes are repositories of unstructured, semi-structured, and structured information, irrespective of its scale. This repository is centralized and is functional for keeping, processing, as well as securing this data. It is important to know that storage can take place in the native format. Also, different varieties of this data can be processed, notwithstanding size-related limitations.
- A data lake can be used for storing information in large amounts.
- The stored data can be utilized to fulfill analytic requirements.
- This repository’s architecture is scalable/open.
- Hence, it can comprise different data types such as:
- Structured like an Excel sheet or database table,
- Unstructured, ranging from audio to images,
- And, semi-structured, i.e. XML or Extensible Markup Language files and web pages.
- Information storage is ensured without affecting its completeness.
- Usually, in these zones, the files are stored:
- Cleansed,
- Raw,
- And, curated.
What does Data Warehousing Mean?
Data warehousing indicates a technology which classes information that has been structured from a single source or multiple ones. It facilitates the comparison, as well as data analysis, of the data for the purpose of business intelligence.
A data warehouse can be understood as an enterprise platform or system. It comes into use for the storage of information collected via different sources. These sources are heterogeneous, including DBMSs, database management systems, and other files.
- Through data warehousing, statistical outcomes can be obtained.
- They can be useful for decision-making processes.
- Further, it can be utilized for the analysis of business information.
How Do Data Lakes Differ from Data Warehousing?
Although both data lakes and warehouses are solutions for cloud storage, their use cases differ. They are different in terms of the data they store, the kind of users who can access the repositories, etc. Even for outlining the schema or facilitating the ELT process, there can be certain dissimilarities between both.
As per Use Cases
A data lake can be used for storing non-relational as well as relational information obtained via different sources. These sources can comprise IoT devices, business/mobile applications, social media platforms, etc. For this, it is not required to define the data schema/structure for as long as it has not been read.
On the other hand, data warehousing can be considered relational. Its schema/structure is usually defined or modeled prior. This occurs as per the requirements of the product/business. These requirements are optimized, selected, and conformed as per structured query language operations.
For Help with Analysis
Users should essentially be aware that a data lake can help with predictive analytics. It can be ideal for machine learning too. For other types such as big data analytics, data visualization, and business intelligence as well, data lakes can be preferred.
Like a data lake, a data warehouse can also help with business intelligence. However, a key difference arises when processes like the visualization of information have to be facilitated. One can rely on a data warehouse for this specific process. Other than this, users can find it reliable for data analytics.
According to the Storage of Information
It is significant to know that a data lake can comprise information of every type of structure. This is inclusive of unprocessed and raw data. Unlike this, a data warehouse can accommodate information which has been changed to serve particular purposes. This information can further be utilized for operational/analytic reports.
Considering the Users
The information kept in data lakes can be used by engineers, data scientists, and other such professionals. Additionally, those who need to study it in the raw/native form can depend on it. By doing so, new insights can be generated to benefit a business.
Professionals like managers can make use of a data warehouse. It is also apt for a business-end user. He/she can get insights through the key performance indicators or KPIs of the business. Keep in mind that in this case, the information exists in a structured form. Hence, it becomes possible to answer all the pre-planned questions, mainly for analysis.
For Defining Schema
In the case of a data lake, schema can be defined, post the storage of information. This also enables the process of keeping this information relatively faster than when data warehousing is used.
On the flip side, if a data warehouse is preferred, it is possible to outline the schema prior to storing the information. This can account for more time for the processing of the information. However, once this is over, one can use the data as and when needed.
To Facilitate ELT Processing
For facilitating the ELT or extract, load, and transform process, information is fetched through a source and then stored in data lakes. Afterward, it can be structured as per requirements.
Considering data warehousing, when ELT is carried out, the information is extracted, loaded, and then transformed into this repository. This information is accessed via varying sources prior to its extraction.
Concerning the Cost
A user can rely on a data lake for the affordable storage fee associated with it. Besides, using this repository can require less time. As a consequence of this, the operational expenditures can be cut down.
On the contrary, data warehousing can be expensive. In addition to this, they can add to the operational fees. This is because more time is needed to use them.
Note: Though data lakes and warehouses differ, they can be used together by an organization.
Data Lakes Vs. Data Warehouses: Which is More Beneficial?
Be it a data warehouse or a data lake, both can be beneficial. Each of these repositories can serve advantages to users in their own ways. To finalize a specific one, the decision can be influenced by the features required by a user along with the purposes he/she wants to fulfill.
With data lakes, a user can store a vast amount of information, whether it has been structured or is unstructured. One can do so with ease without investing huge sums. This is a major benefit to observe with a data lake.
- In addition, since a data lake keeps raw information, it can be accessed quickly.
- New measures can be employed to analyze data and get unique insights.
Data warehousing brings along the advantage of quick information access as well as analysis, without requiring any preparation in particular. With this repository, the complete and correct information can be found in no time. It is, therefore, quite advantageous for businesses. Also, since the data is unified in this repository, it can be trusted, mainly for decision-making.
In the Final Analysis
A professional can rightly choose between data warehousing and data lakes, based on some key differences. Moreover, he or she can opt for both solutions, considering the type of requirements that have to be met. Irrespective of the tool or technology preferred, the ease of storing and accessing information can be observed.
Frequently Asked Questions
- What are data lakes?
Data lakes, in simple words, are information repositories. They come into use for keeping the information along with processing and safeguarding it.
- What is a data warehouse?
A data warehouse can be considered an enterprise platform. Largely, for information storage, it can be utilized.
- Why should you use a data lake?
You can use a data lake to keep information, especially when it is available in vast amounts. It can exist in the form of an Excel sheet, Extensible Markup Language file, etc.
- Is a data lake different from a data warehouse?
Yes, a data lake is different from a data warehouse in certain ways. The use cases of each majorly set one solution apart from the other.
- How is data warehousing different from a data lake?
Data warehousing helps in classifying information. Usually, such data is structured via different sources or a specific one. Contrary to this, data lakes are useful for keeping structured, unstructured, or even semi-structured information.
- Is a data lake better than a data warehouse?
Whether or not a data lake is better than a data warehouse will depend on the purpose to be achieved through the tool. Thus, in their own ways, both tools can be effective.
- Are data warehouses cheaper than data lakes?
Data warehouses can be more expensive than data lakes. Besides, they may add to the cost of operating your business.