Data storage is an important decision for enterprises in virtually all industries. Because data collection is essential to business success, companies must invest in one of the primary forms of data storage. However, there are several differences between a data lake and a data warehouse — from their structure and processing to their different users, purposes and cost. Recently, the use of these terms has evolved, and each has a standard meaning.
Data management professionals, such as graduates of Lamar University’s online Bachelor of Science (B.S.) in Computer Information Sciences program, must understand the differences between a data warehouse and a data lake to best manage a company’s data.
What Is a Data Warehouse?
A data warehouse is a data management system that enables and supports business intelligence (BI) activities, especially analytics. Prevalent in midsize and larger enterprises, this type of system centralizes and consolidates large amounts of historical data from multiple sources. It allows IT and business professionals to perform queries and analyses and share information across business functions for greater efficiency.
The information within a data warehouse derives from a wide range of sources, such as application log files and transaction applications. A data warehouse system’s analytical capabilities allow organizations to extract valuable, actionable business insights from their data to improve decision-making for more consistently positive business outcomes. Data warehouses also benefit enterprises using machine learning, such as manufacturing operations.
What Is a Data Lake?
A data lake is a vast pool of data with bits in raw, native formats because the purpose for acquiring the data is not yet defined. It is a storage repository that contains structured, semi-structured and unstructured data. Larger businesses use data lakes to collect and store data without needing to process or analyze them immediately.
Data scientists and engineers typically use data lakes for quick storage without transformation and for later data use for research and testing. This characteristic enables a data lake to accept unstructured data, whereas a data warehouse can only accept structured data from multiple sources.
Key Differences in Processing and Structure
The primary difference between a data warehouse and a data lake is how data is processed. Data warehouses use an extract, transform and load (ETL) process to verify the integrity of the data and store it in a common format. Data lakes, on the other hand, use a schema-on-read process that cleans, validates and processes data through streaming pipelines. Data warehouses store data in a structured format, while data lakes keep data in a flat architecture. Data warehouses provide quick performance due to their standard underlying data structure, while data lakes are more complex when it comes to queries.
The main structural difference between a data warehouse and a data lake is the storage method. Data warehouses store data in a structured format, while data lakes use a flat architecture. Data warehouses require data to be structured before it is loaded, while data lakes allow for the loading of data in its native format. Data warehouses need a schema to be defined upfront, while data lakes can store data in its raw form. Data warehouses are optimized for read and write operations, while data lakes work best for read operations.
Finally, another compelling benefit to data lakes is their relatively lower cost of storing data. The technologies are open source, which makes licensing and community support free, and data lakes are for low-cost hardware.
How Can I Learn More About Data Storage and Management Strategies?
Deciding between a data warehouse and a data lake depends on the business intelligence needs. If you have a business and IT users that need to perform data analytics without further data curation, then a data lake is the best choice.
Understanding the differences between the two storage options is crucial before deciding which one to use. Therefore, the curriculum in Lamar University’s online B.S. in Computer Information Sciences program includes courses such as Database Design to develop expertise in relevant areas.
Learn more about Lamar University’s online B.S. in Computer Information Sciences program.
