What is the difference between a database and a data warehouse?

Data Warehouse vs Database: A data warehouse is specially designed for data analytics, which involves reading large amounts of data to understand relationships and trends across the data. A database is used to capture and store data, such as recording details of a transaction.

Data Warehouse: Suitable workloads - Analytics, reporting, big data. Data source - Data collected and normalized from many sources. Data capture - Bulk write operations typically on a predetermined batch schedule. Data normalization - Denormalized schemas, such as the Star schema or Snowflake schema. Data storage - Optimized for simplicity of access and high-speed query. performance using columnar storage. Data access - Optimized to minimize I/O and maximize data throughput.

Transactional Database: Suitable workloads - Transaction processing. Data source - Data captured as-is from a single source, such as a transactional system. Data capture - Optimized for continuous write operations as new data is available to maximize transaction throughput. Data normalization - Highly normalized, static schemas. Data storage - Optimized for high throughout write operations to a single row-oriented physical block. Data access - High volumes of small read operations.

Any data storage for application generally uses the database. It could be relational database or no sql databases which are currently trending.

Data warehouse is also database. We can call data warehouse database as specialized data storage for the analytical reporting purposes for the company. This data used for key business decision.

The organized data helps is reporting and taking business decision effectively.

Database:

Used for Online Transactional Processing (OLTP).

• Transaction-oriented.
• Application oriented.
• Current data.
• Detailed data.
• Scalable data.
• Many Users, Administrators / Operational.
• Execution time: short.

Data Warehouse:

Used for Online Analytical Processing (OLAP).

• Oriented analysis.
• Subject oriented.
• Historical data.
• Aggregated data.
• Static data.
• Not many users, manager.
• Execution time: long.

A Data Warehousing (DW) is process for collecting and managing data from varied sources to provide meaningful business insights. A Data warehouse is typically used to connect and analyze business data from heterogeneous sources. The data warehouse is the core of the BI system which is built for data analysis and reporting.

Source for the Data warehouse can be cluster of Databases, because databases are used for Online Transaction process like keeping the current records..but in Data warehouse it stores historical data which are for Online analytical process.

A Data Warehouse is a type of Data Structure usually housed on a Database. The Data Warehouse refers the the data model and what type of data is stored there - data that is modeled (data model) to server an analytical purpose.

A Database can be classified as any structure that houses data. Traditionally that would be an RDBMS like Oracle, SQL Server, or MySQL. However a Database can also be a NoSQL Database like Apache Cassandra, or an columnar MPP like AWS RedShift.

You see a database is simply a place to store data; a data warehouse is a specific way to store data and serves a specific purpose, which is to serve analytical queries.

OLTP vs OLAP does not tell you the difference between a DW and a Database, both OLTP and OLAP reside on databases. They just store data in a different fashion (different data model methodologies) and serve different purposes (OLTP - record transactions, optimized for updates; OLAP - analyze information, optimized for reads).