A data warehouse is a system that collects, organizes, stores, and analyzes large amounts of digital information. Data warehouses, also known as Enterprise Data Warehouses (EDW), tend to be a component of business intelligence (BI) to further the goals of business enterprises. \n\nFor businesses, a data warehouse solution will often also use machine learning and the ability to process vast amounts of information from multiple business data sources. All of this can assist significantly with decision support. There are many uses of data warehouses, each pertaining to the specific needs of the enterprise that owns and operates them. Data scientists can also use data warehouses in non-business environments for academic research.\n\nData warehouses can be broken down into three types. Each comes with distinct advantages and disadvantages, and the type chosen by the business or institution will depend upon the various requirements of that enterprise.\n\n1. __On-premises data warehouse__: This type of data warehouse features a local physical base for the computer hardware, which usually means it will be housed by the company on-site. Due to the high volume of data to aggregate and organize, this tends to require a significant investment. The sale or leasing of this technology includes licensed software to run the hardware of the data warehouse.\n2. __Cloud data warehouse__: As the name suggests, cloud data computation happens off-site and is distributed among servers and computers and networked via the Internet. There may still be a central repository for the data, but it won’t be where the business is based. This can be a significantly more cost-effective option for enterprises seeking advanced data management because no upfront costs are involved in building the necessary hardware. Instead, the company will pay for access and use of the infrastructure, as well as the software license.\n3. __Data warehouse appliance__: The third category is something of a hybrid. Usually sold as a bundle of hardware and software, a data warehouse appliance allows for a combination of on-site security and control and distributed use of information processing. \nData warehouses can help with decision-making, address a user’s particular needs, and offer quality assurance. \n\n1. __Decision-making__: The more a business relies upon data analysis, data mining, and data integration to inform [decision-making](https://www.masterclass.com/articles/how-to-make-informed-decisions), the more likely it is to benefit from the use of a data warehouse. This is especially true of large enterprises like airlines, healthcare providers, banks, large retail chains, and other businesses with significant-sized data. \n2. __Specific needs__: For a business to develop a data system from scratch would require large expenditures of time and money, so the providers of data warehouses can offer something that’s both sturdy enough to work in various parts of the economy, while also being tailored to fit the specific needs of the business. \n3. __Quality assurance__: The sophistication of data warehousing also helps assure high data quality since it filters the data for relevance and functionality.\nData warehouses are helpful to many businesses, but they do have limitations. Data warehouses excel when the data they are processing is a strictly defined type. They optimize speed by performing specific tasks of data analysis with efficiency and regularity. Increasingly, some businesses will require broader and more varied forms of data, and standard data analytics are insufficient.\n\nA data warehouse, data lake, and database can all provide high-performance methods of data mining and analysis with varying capabilities for different amounts of data.\n\n- __Database__: A database typically compiles one kind of raw data, or in the case of relational databases, different types of related data. The business decision-makers deal with a simple data set or data store—one or more types of data storage—categorized for quick analysis. Databases use a data management system known as SQL (structured query language) to determine how the data is stored and retrieved for the end-user. Databases also tend to use metadata to help categorize the data they store.\n- __Data warehouse__: A data warehouse drastically increases decision-making possibilities by handling much greater historical data, often from disparate sources. Data warehouses offer sophisticated methods of organization and analysis. These methods are known as schemas, a sort of rule or algorithm for making data useful. Together, the schemas make up a data model. A data warehouse will usually feature an SQL but might also include other business intelligence tools.\n- __Data mart__: A data mart is a subset of data warehouses that focus on specific data for specific business insights. A company’s sales, personnel, or operations departments might use operational data to assist in related business decisions. \n- __Data lake__: A data lake is a further innovation in the realm of data mining and utility. It can handle even greater volumes of data than a traditional data warehouse, and it specializes in dealing with heterogeneous data. Data lake architecture lacks the schema that a data warehouse possesses. These fundamental differences permit greater flexibility for business users, but this often comes with a cost to speed and efficiency.\nGet the [MasterClass Annual Membership](https://www.masterclass.com) for exclusive access to video lessons taught by business luminaries, including Bob Iger, Chris Voss, Robin Roberts, Sara Blakely, Daniel Pink, Howard Schultz, Anna Wintour, and more.\nLearn about data warehousing, an electronic storage system for analyzing big data.