Data Lake vs. Data Warehouse: What are the Differences?

Data lakes are scalable repositories of raw data that can include structured, unstructured, and semistructured data from many different sources. For example, a data lake can include JSON files, CSV files, PDFs, multimedia, and data from mobile apps, IoT devices, text, or social media. The data can be current or historical, with the intent that an organization will mine the data for insights.

Data lakes typically use the extract, load, transform (ELT) approach to data ingestion, whereby data scientists or analysts will only structure the data as needed. Data is taken from a data lake and structured to support online analytical processing (OLAP) systems. This differs from online transaction processing (OLTP), wherein databases with fixed schema or structures support real-time transactions for processes, including e-commerce, record keeping, and consumer or business apps.

Any advanced analytics implementation, such as for energy sector resource analysis, life sciences research, and smart cities planning and development, is better with a data lake. Because data lakes are effectively vast repositories of unstructured data, they are easy to manage and maintain, and users can scale up storage cheaply and independently from compute resources. To make the best use of data lakes, organizations will need the expertise of data scientists, data engineers, and AI architects to sort through the vast variety and quantity of data to generate actionable insights. But because the data types can include multimedia and other unique forms of information, the quality of insights can be more nuanced and incisive.

Choose data lakes when you have a vast quantity and variety of data sources and have the expertise of data scientists, data engineers, and AI experts to extract the most value from your data.