Data lake vs Data warehouse both are widely used to store big data. However, they are not interchangeable terms. A data lake is a large pool of raw data whose purpose is still unknown. A data warehouse is a place where structured and filtered data can be stored that has been used to accomplish a particular purpose.
Although they may be confused, the two types of data storage can actually be more distinct than one another. The only thing that is truly similar between the two types of data storage is their high-level purpose for storing data.
This distinction is crucial because they serve different purposes, and require different eyes to optimize them. A data lake may work for one company but a data warehouse is better for another.
Four key differences between a data lake and a data warehouse
There are many differences between a database lake and a data warehouse. There are key differences between a Data Lake and a Data Warehouse. These include their data structure, ideal users, and the processing methods used. The overall purpose of the information is also important.
Data structure: raw vs. processed
Raw data refers to data that hasn’t been processed yet for a specific purpose. The most significant difference between data lakes or data warehouses lies in the structure of the raw and processed data. Data lakes store unprocessed raw data while data warehouses store refined and processed data.
Data lakes require a greater storage capacity than data warehouses. Raw, unprocessed data can be easily analyzed and used for machine learning. However, there is a risk that all this raw data can become data swamps if proper data quality and governance measures are not in place.
Data warehouses store only processed data and save storage space. They also eliminate data that is not being used. Also, it is easier to understand processed data by a wider audience.
Purpose: undetermined vs in-use
Individual data pieces within a data lake are not used for a specific purpose. Raw data flows into data lakes, sometimes for a future purpose and sometimes simply to keep track of. Data lakes are less organized and filter data more efficiently than their counterparts.
The term “processed data” refers to raw data that has been used for a specific purpose. Data warehouses can only store processed data. This means that all data stored in data warehouses have been used to fulfill a specific purpose within an organization. This ensures that data is not wasted by not being used.
Users: data scientists vs business professionals
Unprocessed data can make data lakes difficult to navigate. Unstructured, raw data requires the expertise of a data scientist to interpret and translate it for specific business purposes.
Data preparation tools are also getting more support. These tools allow for self-service access to data stored in data lakes.
The processing of data can be used in tables, charts, spreadsheets, and other formats so that all employees if they are not already, can see it. The only requirement for processing data like those stored in data warehouses is that the user has a good knowledge of the topic.
Accessibility: flexible vs secure
Accessibility and ease-of-use refer to the whole of the data repository, not the individual data files. The data lake architecture is free of any structure, making it easy to find and modify. Data lakes are very flexible and allow for quick changes.
Data warehouses are more structured by design. The data warehouse architecture has the advantage of making data easier to understand. However, data warehouses can be costly and difficult to manage due to their limited structure.
Data lake vs data warehouse: Which is the best?
Both are often required by organizations. While data lakes were created to harness big data and make machine learning more efficient, there are still many business users who need data warehouses.
Healthcare: data lakes store unstructured information
Data warehouses are a well-known technology in the healthcare industry, but they have not been very effective. Due to the nature of healthcare data (physicians’ notes and clinical data), it is not easy to organize. Data warehouses are not the best choice because of their inability to provide real-time insights and unstructured nature.
Data lakes can be used to combine structured and unstructured data. This is a great option for healthcare companies.
Education: Data lakes offer flexible solutions
The value of big data has been a key component in education reform over the past few years. Big data about student attendance and grades can help students who are struggling to get back on track. It can also help them predict future problems before they happen. Big data solutions that are flexible and adaptable have been a boon to educational institutions in improving their fundraising and billing.
Many of these data are vast and extremely raw. Therefore, institutions in education benefit greatly from the flexibility offered by data lakes.
Also read: What Reverse ETL can Lighten Your Data Load
Finance: Data warehouses are appealing to the masses
A data warehouse can be used in finance and other business settings. It is accessible by all employees, not just data scientists.
Data warehouses are a key player in the financial services industry’s big data revolution. A financial services company might be tempted to abandon such a model if it is cheaper, but not as efficient for other reasons.
Transportation: Data lakes make it possible to predict
The ability to make predictions is a major benefit of Data Lake insight.
The prediction capabilities that come from flexible data within a data lake in the transportation industry and especially in supply chains management can be hugely beneficial, including cost-saving benefits.
Why it is important to choose data lake vs data warehouse
Although the debate about “data lake or data warehouse” has just started, there are key differences between each model in terms of structure, process, and agility. Depending on the needs of your company, creating the right data lake VS data warehouse will be crucial to your company’s growth.