Data warehouse vs database should you choose one or the other – or, in some cases, would you need both? Each has a purpose and a value for your organization. Each can be simple or complex; both support organizational decisions. In general, they are complementary. The question becomes which one do I need based on what outcome the organization is trying to achieve for a given process or project. Knowing the difference can help avoid mistakes that could jeopardize the success of using either technology to support the needs of the business. Be careful not to mix up the value of the two approaches and choose the wrong one for the business processing you are expecting.
What is a Database?
A database stores data and information in a logical relationship with other data and information. Usually, a database has a particular focus relative to a specific part of a business and contains data related to a specific operation or business function and is collected during the course of running that specific operation or function. The point of the database is to store all pertinent information related to that particular operation, for example, a set of customer, employee or citizen records or the parts lists for all components for all products manufactured by a company – either would be great target use cases for a database. Organizations can have multiple databases supported by different database systems or the same DB system. Databases can be separated in any way that the business finds valuable. Separation can be driven by performance, security, or any other valid business or technical reason. One database can be used by multiple people in different roles in the organization, each finding value, including the ability to collaborate with other departments. Databases can be specialized to handle certain types of data and or certain data operations. For example, a database that is being used by multiple users concurrently would need to make sure that any given dataset or element of that set is only being written to by a single user at any given time to avoid data corruption. Databases that conform to this requirement are considered ACID (Atomic, Consistent, Isolated, Durable) compliant and are used in most Online Transaction Processing or OLTP operations.
Databases for OLTP come in many types. The majority use either row-based or columnar-based architectures, but both generally use Structured Query Language (SQL) though many also provide other programmatic APIs often lumped together under a not SQL or NoSQL label. Implementation of the architectures can vary in complexity and use. There are small personal databases and enterprise-grade databases. Some databases have defined structures and tables or sometimes called a common data model. Then some databases have nothing related to tables and structures besides the common system structures and tables. All databases allow the creation or addition of any tables that an organization needs. Tables may consist of very structured data with a well-defined schema or tables of semi- or unstructured data, for example, document stores or video archives. Other databases have hybrid structures or core underlying structures such as a Key-Value Store that enables them to be very flexible about the types of data and variety that can be stored in a given database.
What is a Data Warehouse?
A data warehouse’s underlying core engine is a database. The key difference is the degree of sophistication of its management and its focus on bringing together data from many disparate and diverse sources, aggregating it together as a set of cross-domain, departmental, or other series of operations, largely for the purposes of online analytical or OLAP processing of information outside of the actual operational process execution. A data warehouse periodically collects information from operational databases, historically in batch mode but increasingly in real-time streams as well. Over time, this aggregated data represents a historical dataset that serves as a baseline pattern for more advanced analytics. However, typical usage is for reporting on operational efficiencies or other key performance indicators that drive business decisions at all levels of an organization.
There are different types of data warehouses offered by multiple vendors. Each has some of the same capabilities that define them as data warehouses. Some vendors will differentiate themselves by adding feature enhancements and additional applications that the other vendor does not have.
Data Warehouse vs Database: Key Differences
The key differences in Data Warehouse vs Database:
- The database is used for active daily transactions such as insert, delete, update or update a record based on daily interactions within an application. This is sometimes called OLTP.
- A data warehouse is used to analyze lots of data simultaneously, usually to produce a report or do trend analysis. This is sometimes called OLAP.
- In any enterprise, downtime for mission-critical operations can be catastrophic, but this is far more the case with an OLTP system and the database it’s built upon – particularly one focused on financial transactions than OLAP systems and the data warehouse they’re built on as these operations tend to be outside operations.
- Databases are optimized or normalized (sometimes called indexing) to allow quick online transactions against the data in the database with an emphasis on writes and updates. The time to perform data analysis is compacted using a normalized database because “pre-wiring” all the internal relationships between the normalized data structures accelerate query returns.
- Data warehouses are designed to handle complex analytics without the need for normalization of data structure that a database needs to perform well. Unlike a database, multiple views of data and data redundancy are allowed. The emphasis is one bulk yet selective reads of datasets.
- A database can support thousands of concurrent users at one time with their access requirements to the data in the database. This support helps with capacity utilization of the database to perform needed data access at desired response times or service level agreements. Doing analytical processing can affect all user response times.
- A Data warehouse can also support a large volume of users at one time but generally require more resources to support concurrency given the size of datasets in a data warehouse and the complexity of the queries that it runs.
With these key differences listed, it is essential to understand that a database is not in conflict with a data warehouse related to capabilities and structure. Each can add value, but the value is determined based on usage.
Why Databases in Business?
Databases are used to help organizations structure data in a meaningful way so that an understanding of data relationships can be used for decisions and enable an organization’s performance to deliver good services and products. A Database also helps different organizations’ departments work in a coordinated fashion using automated technology and tools to do their jobs without otherwise manual interventions. Databases bring related data together in one structure for data integrity.
Why Data Warehouses in Business?
Data warehouses are essential for analyzing data that should not be done with a transactional database. This analysis is necessary for discovering trends and answering any questions about the past, present, and future that the organization needs to know to make decisions. A data warehouse can take data from various sources and analyze them together. Without data warehouses, each department in an organization may have its own data, and additional processing will be needed to use the different data sources together.
Data Warehouse vs Database: Which is a Good Fit for your Business
Database or data warehouse, which is a good fit for your business. Probably both, each has capabilities that support the business performance and ability to understand its customers. Besides that, both enable organizational collaboration and coordination across the company in an automated fashion. Each has specific capabilities that help the organization remove the constraint of using one solution for both ineffectively. Many organizations have both and use them