Data warehouse and data lake are the common terms used for defining data storage in the digital space. While the two come with their own sets of differences, they are usually interchanged or misunderstood as people are not aware of the fine line differences between them.
In simple terms, data lake refers to a pool of raw data, the purpose of which is undefined whereas the data warehouse refers to structured and processed data whose purpose is specific and defined.
The article explains the key differences between a data warehouse and a data lake while also explaining which may be a better fit as per the company’s requisites.
Data Type: Structured vs Unstructured
Data warehouse stores processed and structured data gleaned from transactional systems. The less or never used data is stored here which makes it cost-efficient for data storage and management. Non-traditional data sources like text, image, web server logs, social network activity are overlooked in this storage model.
Data lakes store raw and unstructured data and require a larger storage capacity compared to data warehouses. Date lakes are tricky as they may turn into data swamps without proper data governance measures. Unlike a data warehouse, data lake embraces all types of data regardless of the sources.
Purpose: Analytics for business decisions vs Cost-effective big data storage
The data stored in a data warehouse is cleansed and processed, so the storage space is not wasted on irrelevant or unused data. Organizations utilize data from a data warehouse for specific purposes. Also, the repository provides a multi-dimensional view of granular and summary data to make strategic decisions in businesses.
Whereas, the purpose of unstructured and fragmented data stored in a data lake is undetermined. The data in a data lake is stored with the belief that it might be of use sometime in the future.
User: Data scientists & Engineers vs Data analysts & Business analysts
Data warehouse comes in handy for data analysts and business analysts as they work on reports and sliced data to measure key performance metrics. Almost 80% of users of an organization prefer to deal with structured, purpose-built, and easy to use data. Thus, a data warehouse is a favorite choice for many business professionals.
While data lake supports all types of users, it is specially used by data scientists and engineers. Data scientists leverage sophisticated tools and algorithms to comb through the unstructured behemoth data and convert it into meaningful insights. Since data scientists require large and diverse data to perform statistical analysis and predictive modelling, data lake serves the best choice for them.
Accessibility: Secure vs Flexible
Since a data warehouse is more structured by design it is more secure as well as complicated. Though the structured and processed data stored in a data warehouse makes it easy to gain insights, it also makes it hard and costly to manipulate.
On the other hand, a data lake is a repository of structured, semi-structured, and unstructured data, which makes it flexible and easy to access. Further, fewer limitations in a data lake enable easy and fast data manipulation.
Data warehouse vs Data Lake: Which is the best
Data warehouse and data lake, both are equally important for an organization as each serves its purpose in benefitting from big data. Different industries have different requirements. Hence it is vital to pen down requirements before choosing a suitable storage system.
To explain clearly, consider the healthcare industry. They have adopted a data warehouse currently but haven’t been successful with the structured data yet. As the nature of data in the healthcare industry is highly unstructured (clinical data, physical notes, etc.), the data warehouse storage model is inappropriate. Whereas, a data lake is a better fit for the industry as it comprises of both structured and unstructured data.
Alternatively, in the case of the finance industry, a data warehouse is appropriate as the repository will be accessed by business professionals rather than data scientists. Even more, the combination of both data warehouse and data lake (hybrid model) will best suit the industry, as big data will help the industry make in-depth analysis while data warehouse will enable data-driven decision making through the analysis.
Wrapping Up
Data warehouse and data lake, though varied by structure, purpose, process, users, flexibility, etc., come with their own advantages and limitations. Identifying the needs and goals of the company helps determine between a data warehouse or a data lake or a hybrid repository system to harness the big data.