Difference between Database and Data Warehouse

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Database and data warehouse are both systems that store data. But they serve very different purposes.

What is a Data Warehouse?

A data warehouse is an information system which stores historical and commutative data from single or multiple sources. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision makers rely on reports, dashboards, and analytics tools(business intelligence (BI) tools, SQL clients) to extract insights from their data, monitor business performance, and support decision making. It is designed to analyze, report, integrate transaction data from different sources.

Data Warehouse eases the analysis and reporting process of an organization. It is also a single version of truth for the organization for decision making and forecasting process.

What is a Database?

 

A database is a computerized system that makes it easy to search, select and store information. Databases are used in many different places. A database (DB), in the most general sense, is an organized collection of data. Most common examples are relational databases like Oracle, SQL SERVER and MySQL, etc. 

How do data warehouses and databases co-exist?

Why use a Database?

Here, are prime reasons for using Database system:

• It offers the security of data and its access
• A database offers a variety of techniques to store and retrieve data.
• Database act as an efficient handler to balance the requirement of multiple applications using the same data
• A DBMS offers integrity constraints to get a high level of protection to prevent access to prohibited data.
• A database allows you to access concurrent data in such a way that only a single user can access the same data at a time.

Why Use Data Warehouse?

Here, are Important reasons for using Data Warehouse:

• Data warehouse helps business users to access critical data from some sources all in one place.
• It provides consistent information on various cross-functional activities
• Helps you to integrate many sources of data to reduce stress on the production system.
• Data warehouse helps you to reduce TAT (total turnaround time) for analysis and reporting.
• Data warehouse helps users to access critical data from different sources in a single place so, it saves user’s time of retrieving data information from multiple sources. You can also access data from the cloud easily.
• Data warehouse allows you to stores a large amount of historical data to analyze different periods and trends to make future predictions.
• Enhances the value of operational business applications and customer relationship management systems
• Separates analytics processing from transactional databases, improving the performance of both systems
• Stakeholders and users may be overestimating the quality of data in the source systems. Data warehouse provides more accurate reports.

Processing Types: OLAP vs OLTP

The most significant difference between databases and data warehouses is how they process data. While most databases are OLTP application files, most data warehouses are online application processing (OLAP) files. OLAP gets information by gathering data from OLTP and other database files. Because of how OLAP files are structured, it’s far easier to run queries and analyses on the data they contain, and anyone can query the data warehouse with either data warehouse software or knowledge of SQL. Individual subsections of the data warehouse, which are typically relevant to an individual team or department, are called “data marts.”

The data in databases are normalized. The goal of normalization is to reduce and even eliminate data redundancy, i.e., storing the same piece of data more than once. This reduction of duplicate data leads to increased consistency and, thus, more accurate data as the database stores it in only one place.

Difference between Database and Data Warehouse

Databases aren’t better than data warehouses, or vice versa. They perform very different functions from one another, and each is very powerful.

Characteristics	Data Warehouse	Transactional Database
Purpose	To store large datasets and historical information to analyze data	To store operational data that is current and most up to date
Suitable workloads	Analytics, reporting, big data (OLAP)	Transaction processing (OLTP)
Data source	Data collected and normalized from many sources	Data captured as-is from a single source, such as a transactional system
Data capture	Bulk write operations typically on a predetermined batch schedule	Optimized for continuous write operations as new data is available to maximize transaction throughput
Tables and Joins	Denormalized schemas, such as the Star schema or Snowflake schema. Joins are simple	Highly normalized, static schemas. Joins are complex.
Data storage	Optimized for simplicity of access and high-speed query performance using columnar storage	Optimized for high throughout write operations to a single row-oriented physical block
Data access	Optimized to minimize I/O and maximize data throughput	High volumes of small read operations
Data Modelling	Data modeling techniques are used for designing.	ER modeling techniques are used for designing.

 

Applications of Database

Sector	Usage
Banking	Use in the banking sector for customer information, account-related activities, payments, deposits, loans, credit cards, etc.
Airlines	Use for reservations and schedule information.
Universities	To store student information, course registrations, colleges, and results.
Telecommunication	It helps to store call records, monthly bills, balance maintenance, etc.
Finance	Helps you to store information related stock, sales, and purchases of stocks and bonds.
Sales & Production	Use for storing customer, product and sales details.
Manufacturing	It is used for the data management of the supply chain and for tracking production of items, inventories status.
HR Management	Detail about employee’s salaries, deduction, generation of paychecks, etc.

Database Use Cases

Databases process the day-to-day transactions in an organization. Some examples of database applications include:

• An ecommerce website creating an order for a product it has sold
• An airline using an online booking system
• A hospital registering a patient
• A bank adding an ATM withdrawal transaction to an account

Applications of Data Warehousing

Sector	Usage
Airline	It is used for airline system management operations like crew assignment, analyzes of route, frequent flyer program discount schemes for passenger, etc.
Banking	It is used in the banking sector to manage the resources available on the desk effectively.
Healthcare sector	Data warehouse used to strategize and predict outcomes, create patient’s treatment reports, etc. Advanced machine learning, big data enable datawarehouse systems can predict ailments.
Insurance sector	Data warehouses are widely used to analyze data patterns, customer trends, and to track market movements quickly.
Retain chain	It helps you to track items, identify the buying pattern of the customer, promotions and also used for determining pricing policy.
Telecommunication	In this sector, data warehouse used for product promotions, sales decisions and to make distribution decisions.

Data Warehouse Use Cases

Data warehouses provide high-level reporting and analysis that empower businesses to make more informed business. Use cases include:

• Segmenting customers into different groups based on their past purchases to provide them with more tailored content
• Predicting customer churn using the last ten years of sales data
• Creating demand and sales forecasts to decide which areas to focus on next quarter

Summary

Now you understand the difference between a database and a data warehouse and when to use which one. Your business needs both an effective database and data warehouse solution to truly succeed in today’s economy.