Today databases and data warehouses are not only systems for reliable storage and processing of service information. One of the most attractive features of big data technologies is the cost of storing the data described here.
Although operational databases and data warehouses are built mostly on the same technology support, these two systems differ from each other.
What is a database?
It is a set of neatly organized information with structured data (in rows, columns, and tables) that can be easily managed and indexed to find any information you need. Databases control workloads to create and update themselves, querying the data they contain and running applications there.
What is a data warehouse?
It is a unified repository that assembles data from numerous sources for further analysis to provide meaningful business insights. This electronic storage is mainly developed to analyze, report, and integrate transaction data from varied sources.
Here, we've split the most important discrepancies between database and data warehouse so you can highlight all aspects and select the one that meets the requirements of your data structure situation. Let's dive into more technical and detailed characteristics of database and data warehouse:
Five Main Differences between Database and Data Warehouse
1. From a functional point of view: operational databases process transactions, providing answers to operational requirements, while data warehouses are used based on ad queries, mainly for management purposes.
2. The functional requirements differ: operational databases are mainly focused on data security and consistency, which makes queries slow, ad-hoc. These queries, specific to economic analysis, can significantly degrade operating system performance due to the lack of predictable indexes, as in the case with data warehouses.
3. Although most operating systems and data warehouses are designed with relational technologies, their design is different because their purpose is also different. Operational databases are created for online transaction processing, and their primary goal is to store large amounts of transactional data efficiently. They include current information on daily activities and information on processes for further updates. As a result, the data is dynamic and, therefore, highly volatile. These systems are structured and repetitive and consist of current, short, and isolated transactions that include detailed data. These transactions read or update several records — mostly based on their primary keys. Operational databases range in size is from hundreds of megabytes to gigabytes. Their consistency is essential and refers to fast transaction processing.
4. Backup and recovery strategies are different. Most of the data in data warehouses is historical data that does not need to be saved multiple times. In some cases, it is good to save data from staging databases to minimize the impact on data warehouse performance. Recovery policies can be different in data warehouses and databases, depending on how much the organization requires continuous, uninterrupted access to the data stores.
5. Another difference between systems is about mechanisms required for simultaneous user access. Since the data stores are not updated, transaction management, concurrency control, and other similar mechanisms are used only at the initial stage of loading and subsequent addition because they are expensive in response time. These mechanisms can be disabled during the current use of data stores.
The question of databases vs. data warehouses is relevant for every business with big data needs. And as we can see from the below comparison, databases and data warehouses are much different in practice.
Database vs. Data Warehouse — A Comparative Review
The database uses Online Transaction Processing (OLTP)
The data Warehouse uses Online Analytical Processing (OLAP)
Performs vital operations for your business
Helps to analyze your business
Tables and Joins
These are complex because they are normalized
These are simple because they are denormalized
Applied data collection
Domain-specific data collection
Usually limited to one application
Stores data from any number of applications
Data is available in real-time
Data is updated from source systems as needed
ER modeling techniques are used
Data modeling techniques are used
The data stored is up to date
Current and historical data is stored in the data warehouse, though it may not be up-to-date.
Storage of the data
Flat Relational Approach method for data storage
A dimensional and normalized approach for the data structure.
Simple transaction queries
Complex queries for analysis purpose
Detailed Data is stored in a database
Highly summarized data
Companies are ready to spend huge amounts of money on the development of IT infrastructure, realizing the importance of this component for increasing efficiency and profit and increasing competitiveness in the market. Therefore, when choosing a system, it is extremely important not only to understand the goals of the company, predict possible changes in the future, but also to be well oriented in the continually changing IT technologies, evolving solutions, and strive to improve and offer customers their best products.
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