Before jumping into this topic of database vs. data warehouse, we must first quickly review the importance of analytics in database management. If you do not understand the significance of analytics, then there is no point in reviewing the distinction between data warehouses and databases. In a nutshell, the future of any industry like banking, healthcare, or e-commerce will be fully relying on the massive amount of available data to drive better decision-making at a lower cost. If you cannot effectively perform the analytics based on the data, you will have trouble improving the costs and quality of your database. Also, you will not succeed in the changing business environment.
A higher-level distinction between data warehouse and database
We call the database for the purpose of this article to be designed for transactional systems to run effectively. Typically, these are OLTP databases (online transaction processing databases). In the healthcare industry, an electronic health record or HER is a system, which is an example of a system that runs on OLTP. Any OLTP database is constrained of a single application into practice.
The major fact to be noted here is that the transactional databases do not do the analytics independently. In order to perform the same, they need the assistance of a data warehouse too. A data warehouse is a database of a different kind, which is a typical OLAP or online analytical processing DB. A data warehouse exists as an additional layer on top of another database, i.e., an OLTP DB. The data warehouse may take the data from all the databases and create an additional layer dedicated to analytics.
A database that is meant to handle the transactions is not designed to handle the analytical operations. The standard databases are not structured to the analytics part well. On the other hand, a data warehouse is meant to do the analytics part easier and faster. In modern-day business applications, there is a lot of time and money involved in developing and implementing the enterprise management systems, i.e., EHR in healthcare. These applications are now ready to pull the data from all related systems and then use it to drive cost and quality improvement. This is where the use of a data warehouse comes into play.
Databases vs. data warehouse
Now, as you have the overall idea about database vs. data warehouse, we may go deeper into the details of some of the major distinctions between database and data warehouse. Let us have a side by side comparison to understand the same. For a detailed overview of database functionalities and data warehouse features, you may also feel free to approach RemoteDBA.com expert services for remote database consultation.
| Database | Data Warehouse |
| The database is any data collection organized well for storage, retrieval, and easy accessibility.
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The data warehouse is a database that integrates the copies of transactional data from a disparate source and provisions the same for analytical purposes.
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| There are various types of databases, but all of them apply to the OLTP database. Other database types include XML, CSV, text, and even the excel spreadsheets. | The data warehouse is also OLAP databases. An OLAP database will layer on top of the OLTP DBs to enable analytics. An important note about these types of databases is that not all OLAPs are equal. Each of these may differ based on how the data is modeled. Most of these data warehouses tend to employ dimensional or enterprise data models.
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| The OLAP and OLTP systems can store and manipulate data in tables consisting of columns, indexes, keys, views, and data types. Both of these uses structured query language to query the data.
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A data warehouse may equal many applications and infinite databases. OLAP will let a single source of truth for organizational data. The source of truth is used to guide the analysis and decision making in an organization.
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As we can see, the OLTP DBs typically meet about 99.99% (nearly full) uptime. In OLTP, any system failures or chaos may end up in lawsuits. In this case, the database is linked directly to the front-end application. Data is made available in real-time to meet all the here-and-now needs of enterprises. In the case of healthcare applications, these data will contribute effectively to the healthcare practitioners who use this data to deliver precise and timely services to the patients.
OLTP databases are optimized for performing the read and write operations for every single-point transaction. The OLTP databases need to deliver the data within sub-second response times. Performing large analytical queries on these databases may be a bad practice as these may impact the system’s performance while using the same for day-to-day operations. A typical analytical query may take a few minutes to run by locking the users on it for the meantime.
On the other hand, as it works on large data sets, OLAP databases may be heavy on your CPU and may need higher bandwidth. Typical data warehouses are designed to handle large analytical operations, which will eliminate the strain that analytics may place on transactional databases when put as an additional layer.
OLTP DB structure is very complex by featuring tables and joins as the data is normalized. The data in it is structured in such a way that nothing gets complicated. Making a relational data model this way can deliver better efficiency and storage. This will also let a sub-second response time by improving the speed.
In OLAP structure, data get better organized to facilitate the reporting and analysis, but not for the sub-second transactional responses. Here, the data is denormalized to enhance the analytical query response and provide more ease of use for the business database users. In OLAP’s structure, there are only fewer tables and simplified structures, which will result in easier reporting and faster analysis of data.
Hope the information we discussed here helped you understand databases vs. data warehouses and the significance and inter-relation of both in enterprise usage.
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