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Difference between Database and Data Warehouse with Comparison Chart

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Difference between Database and Data Warehouse with Comparison Chart
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In the current scenario ridden with Big Data, data banks and data creation systems, companies across the world are looking for the most lucrative ways for managing their data. In interviews too, participants are being asked questions related to the definition of database, what is data warehouse, the difference between data warehouse and database, etc.

Users of operational systems, accounting, CRM, HR, etc. are forever grappling with systems that use and generate large quantities of data. A fair idea about database vs data warehouse helps them handle data more effectively, apply logic to data, and move the acquired data into right channels to create the necessary structures. This article aims to explore the difference between database and data warehouse in lucid ways.

Data Warehouse vs Database Comparison Chart

Basis of difference

Database

Data Warehouse

Utility

A database is useful for Online Transactional Processing () and can also be utilized for the creation of data warehouses. It records user data for historical use and analysis.

A data warehouse is used for the purposes of Online Analytical Processing (). It allows users to read the accessed historical data with a view of making business decisions and strategies.

Complexity of tables/ joins

In a database, the tables and joins serve to be complex as they have to be normalized (for RDMS). The normalization of databases is carried out for reducing redundant data and saving on storage space.

In a data warehouse, the tables and joins are not as complex as in a database. More than one normalized database may be used for the creation of a data warehouse.

Modelling technique

Entity – Relational modelling technique is put in use for designing RDMS databases.

Data – modelling techniques are utilized for designing a data warehouse.

Optimization

A database is optimized for enabling write operations. The optimization process is necessary for maximizing the speed and effectiveness with which the stored data can be updated (modified, added, or deleted). Optimized data enables quicker analysis and data accessibility. The write operations are recorded in the database system.

A data warehouse is optimized for read only operations. 

Performance

Performance is relatively low for queries related to analysis.

High performance levels are in force for analytical queries.

Processing type

Databases use Online Transactional Processing (OLTP) modes to delete, replace, insert and/or update any number of short transactions.

Data warehouses utilize Online Analytical Processing (OLAP) modes that have been optimized for handling low numbers of complicated queries on large accumulated historical data sets.

Ease of performing analytical queries

As there are many joins and tables in a database, it is difficult to perform analytical queries without the help of experienced database administrators or developers. Users have to be familiar with database applications in order to write the queries for meaningful analysis and results.

Data warehouses use OLAP which serves as a function of business intelligence. This easy-to-use analysis process helps managers and analysts select, access, extract, view as well as analyse corporate data for identifying and obtaining insights on market trends and identifying potential issues.

Query response time

The query response time from databases has to be extremely fast for enabling efficient transaction processing.

As the tables are denormalized in a data warehouse and already transformed to provide summarized data and multidimensional views, the query response time in faster. The quick query response time is used for measuring the effectiveness of the OLAP system.

Data timeline

Databases are used for processing day-to-day transactions in organizations. Generally, they do not contain historical data and provide current data via normalized relational databases.

Data warehouses are important for analytical purposes, business reporting and making decisions. They generally store historical data after integrating the copies of transaction data acquired from disparate sources. They may use real-time data feeds to access current, integrated information for specific reports.

Concurrent users

OLTP databases are designed to support thousands of concurrent users without affecting the system’s overall performance.

Data warehouses are designed to support a restricted number of concurrent users only. As a data warehouse is away from front-end applications, it is dependent on complex queries. Therefore, only limited people may access the system simultaneously.

Examples

Credit card processing applications.

Customer relationship management (CRM), social media data, enterprise resource management (ERP), etc.

Drawbacks

Reporting, visualization, and analytical operations are difficult to perform over big integrated data sources and data streams.

A data warehouse is more expensive than a database. It also provides less control for security and access configurations.

Read more here Difference between OLTP and OLAP

What is a Data Warehouse?

A data warehouse refers to a system that is designed to pull data into an organization for the purposes of analysis and reporting; the data so collected is pulled from many sources. Thereafter, complex queries are used for creating reports within the data warehouse. The reports are used by the management for making business strategies and decisions. Overall, a data warehouse can be termed as a consolidated view of logical or physical data repositories collected from different systems.

  • The main job of a data warehouse is to correlate data belonging to different systems in one place for the purpose of querying, report generation or taking business decisions.
  • Data warehouses are utilized for the purposes for online analytical processing (OLAP). This kind of processing uses complex queries for the sake of analysing rather than processing transactions.

What is a Database?

A database comprises of data organized in the form of columns, rows, tables, views, etc. These database objects are indexed periodically for the cause of accessing relevant information and making them more meaningful to their users. These days, it is common to find enterprises and organizations creating and managing their databases with the help of well-designed database management systems like Oracle and SQL.

  • Databases can be created and designed to store customer information and product inventory with the help of customized database management system software.
  • Companies use their databases for performing the tasks of online transaction processing (OLTP) more effectively.
  • Database have to be supported by database software to offer convenient and quick access to information so that queries can be dealt with quickly and effectively.
  • Databases are termed as operational systems as they are used for processing daily transactions in organizations.
  • Data administrators can be trained to write, read and generate reports from the data present in the database.
  • Transactional databases are useful for providing consistency, isolation, atomicity as well as durability (ACID) compliances for ensuring that all transactions taking place are complete and consistent.

Key difference between Data Warehouse and Database

Some important points of difference pertaining to data warehouse vs database are enumerated below:

1. A database is mainly used and designed for the purpose of recording data. On the other hand, a data warehouse comes in handy for analysing data. While databases are normally optimized for read-write operations pertaining to singular point transactions, data warehouse are used for big analytical queries.

2. Database is a collection of data that is application oriented. On the other hand, data warehouse lays focus on a category of data. While databases are generally limited to single applications and target only one process at a time, data warehouses provide storage for data pertaining to any given number of applications. They may contain/ target infinite applications/ processes as needed.

3. Another difference between database and data warehouse pertains to the former being a real-time data provider, while the latter serves as a source of data and records that can be accessed easily for the purpose of analysis and decision making.

Conclusion

Data-driven business environments can function effectively only if they have speedy databases and data warehouses for recording, accessing and analysing data. It is important to understand the difference between database and data warehouse to enable effective real-time data migration. In case you have any further queries with regards to database vs. data warehouse do drop a line in the comments section below. We will wait to hear from you.


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