Beyond the RDBMS: Data warehouse vs. data lake vs. data mart

There are many ways to store big data, but the choice of data warehouse vs. data lake vs. data mart comes down to who uses the data and how. Use this cheat sheet to compare. The vast amount of data organizations collect from various sources goes beyond what traditional relational databases can handle. This leads to the data warehouse vs. data lake question -- when to use which one and how each compare to data marts, operational data stores and relational databases. All of these data repositories have a similar core function: housing data for business reporting and analysis. But their purpose, the types of data they store, where it comes from and who has access to it differs. In general, data comes into these repositories from systems that generate data -- CRM, ERP, HR, financial applications and other sources. The data records created from those systems are applied against business rules and then sent to a data warehouse, data lake or other data storage area. Once all the data from the disparate business applications is collated onto one data platform, it can be used in business analytics tools to identify trends or deliver insights to help make business decisions.