Database and Data Warehouse: Whats the Difference?

Database and Data Warehouse
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Does a large number of business transactions take place each day at your company? Do you have data from previous years that you wish to study in order to make your company more successful? Great! Then you are going to require a database in addition to a data warehouse… But which pieces of information belong where? Both a database and data warehouse are examples of different types of storage systems. However, they are used for quite different objectives. In this article we will discuss the difference between relational, operational, transactional, data lake and data warehouse.

Let’s quickly go over the basics of how these various storage systems function and the situations in which they could be useful.

What Is Database?

A database stores information or data in a centralized location. Online transaction processing (OLTP) relies on databases, which are accessible digitally by users. Since the advent of computerized data storage, businesses have relied on database management systems. A database management system (DBMS) is only a means of providing convenient access to information.

Relational database management systems (RDBMS) have dominated the market for several decades, so when we talk about databases, we almost always mean an RDBMS. Companies use them because it is faster to store and retrieve data using a relational database management system.

Also, a database is a sorted set of information. Relational databases store information in “tables,” which constitute collections of data that cluster related data together. In this analogy, a table represents a grid with columns and rows.

  • Each record in a table, such as a list of shipments or a list of customers, is represented by a row.
  • Data fields such as a customer’s name, address, phone number, etc. can be seen as columns in a table.
  • Columns, rows, and tables are all defined in a database by a schema, which is a specification of all database parts.

Online transaction processing (OLTP) systems rely heavily on databases as their backend because they add, update, and delete records one at a time. Since records are retrieved from tables one at a time, the most efficient approach to store them is in rows, with indexes on key fields to speed up retrieval.

However, not all infrastructures rely on a transaction model. It might be helpful to examine patterns in data collected over time. It is not necessary to know the values of certain records. You need summaries of behavior, such as total amounts spent and distance traveled. Again, you must act quickly upon acquiring this information.

Database Use Cases

Databases, like data warehouses, have several practical uses in a wide variety of fields. Personal databases are another common application. A few instances are as follows:

  • Electronic health record (EHR). Information about a patient can be stored in an electronic health record (EHR) as early as the first visit. Then, on subsequent visits, the information is refreshed. This data is protected and private while it is hosted on the platform. It revises the scheduled appointment time and date, as well as the patient’s current list of symptoms and diagnosis. Electronic health records also allow doctors to view their data from any location so long as they have permission to do so.
  • Consumer recommendations. A database is used by Netflix and Spotify to keep track of the shows and songs they offer, as well as your viewing and listening habits. NoSQL databases save this data and use it to make suggestions about what you might like to see next based on your previous interactions.

Database Professionals

Data science experts are often those that have professional experience working with databases. Some common occupations in this area are described below. Keep in mind that the below job titles may differ depending on the industry.

  • Database architect. A database architect’s job is to create and maintain databases. They pioneer new approaches to database management, development, and protection. Their main objective is to improve data accessibility for users like data analysts, data scientists, and engineers. The median annual pay for a database architect in the United States is $109,693. What Is a Database Manager, and How Do You Become One?
  • Database administrator. A database administrator’s job is to make sure the database functions smoothly. They design and implement databases to keep track of things like financial records, product specs, and order details. Database administrators also handle permissions to ensure that only authorized users can access the data. Also, the median annual pay for a database administrator in the United States is $78,837.
  • Data analyst: In order to help businesses with their issues, data analysts collect, clean, and analyze data sets. A database analyst’s annual income in the United States averages $74,294.

Types of Databases

Multiple varieties of databases exist. You can categorize them as bibliographic, full-text, numerical, or image-based. In the field of computers, databases usually fall into groups in accordance with the structure they employ.

Here are just a few examples of important organizational databases:

#1. Relational

This statistical method describes information in a way that allows for flexible data organization and retrieval. Tables are the building blocks of relational databases. In those tables, information is structured according to predetermined formats. Each column in a table stores some kind of information, and each row stores an instance of that information. However, a relational database uses rows, columns, and tables to arrange data about individual customers. Indexing them facilitates searching using SQL and NoSQL queries.

Also, user and application programming interfaces for relational databases are typically written in SQL. In a relational database, adding a new data type doesn’t require rewriting any of the connected programs. Data in a relational database is managed, queried, and retrieved with the aid of a relational database management system (RDBMS). Also, read What Are Relational Database Management Systems.

Typically, an RDBMS will allow its users to manage who can read and write to the database, as well as generate reports and do analyses. In order to ensure that all transactions are finalized and that all data is consistent, certain databases provide support for the ACID model.

#2. Distributed

This database contains files or records in more than one place. The processing of data is also distributed and copied across the network.

Homogeneous distributed databases use the same hardware at each node and share the same software stack for managing and accessing data across nodes. Heterogeneous groups exist as well. In such situations, the multiple locations may use different hardware, operating systems, and database programs.

#3. Cloud

These DBs are constructed for a virtual setting in a public, private, or hybrid cloud. The amount of data transferred and stored for a user is what determines their monthly fee. They also come with high availability and scalable resources. These databases are compatible with SaaS (software as a service) applications.

#4. Graph

These registries are an example of a non-relational database. They use graph-theoretic ideas for relational storage, mapping, and querying. Nodes and edges are the building blocks of a graph database. Entities, or nodes, are the links between other nodes.

However, these databases are commonly used for network analysis. Customer data collected from a company’s websites and social media platforms can be analyzed using graph databases.

The SPARQL language and protocol are used for analytics in graph databases. SPARQL can analyze data in the same ways that SQL can, and it can also be used for semantic analysis, which involves looking at connections between pieces of data. Because of this, it can be utilized for analytics on data sets consisting of both structured and unstructured information. Using SPARQL, users can analyze data from a relational database, take advantage of friend-of-a-friend connections, PageRank, and find the shortest path.

#5. NoSQL

NoSQL databases excel at managing massive amounts of disparate data. Relational databases have limitations that these alternatives can overcome. In addition, they excel at evaluating data stored on cloud computing infrastructure and big, unstructured data sets. Non-relational databases is another name for these types of databases.

Why Do Databases Face Difficulties?

There are a few consistent difficulties that arise throughout database installation, operation, and upkeep.

  • A company’s data is an asset that must be protected at all costs. Competent cybersecurity personnel require expertise to protect data repositories, which can be expensive.
  • Having reliable data is a result of data integrity. Data integrity is difficult to achieve since it requires limiting database access to only authorized users.
  • Maintaining a database and keeping it up-to-date are essential for optimal efficiency. Changes in the underlying technology or the data contained within a database might have a negative impact on its usability if it isn’t properly supported.
  • It might also be challenging to integrate databases. Data lakes and data warehouses are two examples of ways in which this might be accomplished, as is the consolidation of several databases.

What Is a Data Warehouse

A data warehouse is a central repository allowing an organization to access data from various departments and units for the purposes of reporting and analysis. The data warehouse is then used to generate reports using complex queries. The reports are used by management in making business decisions. In a data warehouse, you can see how various systems’ physical and logical data stores fit together.

However, a data warehouse’s primary function is to centralize data from many sources so that it can be queried, reports can be generated, and business decisions can be made. Data warehouses are the locations for OLAP (Online analytical processing). This form of processing does not deal with transactions but rather uses complex queries for analysis.

The operational database and the decision support database (Data Warehouse) are kept in completely distinct locations. The data warehouse, however, is not a thing but rather a setting. It’s a part of an information system’s architecture that’s designed to give users easy access to and presentation of data that would otherwise be difficult to find in a conventional operational database.

How Data Warehouse Works?

A Data Warehouse is a storehouse for data that comes from various other sources. The transactional system and other relational databases are two sources of data that go into a data warehouse.

Data can take the form of:

  • Structured
  • Semi-structured
  • Unstructured data

Business intelligence tools, SQL clients, and spreadsheets can all access the processed data stored in the Data Warehouse after it has been transformed and ingested. Information from numerous sources can be combined in a data warehouse.

An organization can gain a more complete understanding of its clients by centralizing this data. As a result, you may rest assured that it has taken into account every piece of data at its disposal. Data mining is only possible with a data warehouse. In data mining, the goal is to discover useful trends that can boost revenue and earnings.

Data Warehouse Use Cases

There are a wide variety of uses for a data warehouse in corporate settings. Their potential applications could be sector specific. Here are two cases in point:

  • Health care. A data warehouse may store information about patients that can help doctors better diagnose illnesses and assess the effectiveness of various treatments. A data scientist in the healthcare industry may, for instance, examine the information stored in a data warehouse to learn why chemotherapy is more commonly used for patients over the age of 25 with cancer.
  • Marketing. A data warehouse can help a marketing organization monitor the results of a campaign or new product launch. Performance, sales, and customer service interactions may all be monitored with the help of internal dashboards and reports.
  • Banking. Its widespread adoption in the banking industry attests to its efficacy in managing on-desk resources. A select group of financial institutions also employed it for product and market performance analysis and market research.
  • Public sector. The government relies on data warehouses for acquiring intelligence. Also, it aids government agencies in keeping track of and analyzing individual tax and health insurance data.

Data Warehouse Professionals

Professionals in the field of data science are those who make use of data warehouses in their job. Careers in this area are defined in the following list. Be aware that the job titles listed below may change slightly from one sector to another.

  • Business intelligence (BI) analyst. Data warehouses are the bread and butter of a business intelligence analyst, who uses them to provide enterprise-wide and department-specific business insights via data visualization. Using data visualization and programming languages like Python, SQL, and Tableau, they construct reports, dashboards, and other visual tools. In addition, the average salary for a business analyst in the United States is $80,654.
  • Data warehouse analyst. A data warehouse analyst’s job include investigating and assessing information stored in one. On the basis of their findings, they offer suggestions for how the company’s current data storage and reporting processes might be better. In addition, they may compile and display their findings to aid in other areas of the company’s operations. The average annual compensation for a data warehouse analyst in the United States is $81,010.
  • Data warehouse engineer. An individual who works as a data warehouse engineer develops and oversees data warehouse plans. They could be in charge of determining project parameters, vetting potential software packages, and guiding the development of long-term strategies. Also, the average annual income for a data warehouse engineer in the United States is $95,760.

Types of Data Warehouse

There are primarily three types of data warehouses, or DWHs:

  • Enterprise Data Warehouse (Edw). In this context, “warehouse” refers to an Enterprise Data Warehouse (EDW). It’s used by employees all around the company to help them make decisions. It provides a standardized means of arranging and representing information. It also allows information to be categorized by topic, allowing for more granular levels of access control.
  • Operational Data Store. When neither an organization’s Data warehouse nor its OLTP systems can meet its reporting demands, the necessity for an Operational Data Store (also known as an ODS) arises. Data warehouse in ODS is constantly updated. This means it is the go-to option for things like keeping employee records and other mundane administrative tasks.
  •  Data Mart. The data warehouse includes the data mart as a specific section. It’s tailor-made for one particular field of business, like sales, finance, sales, or finance. An autonomous data mart allows for data collection straight from the sources.

Components of Data Warehouse

The following are the three components that make up data warehouses:

  • Warehouse Manager. The duties of the warehouse manager include managing the data stored in the warehouse. It carries out tasks including data consistency checks, index and view building, denormalization and aggregate generation, source data transformation and merging, data archiving, and data baking.
  • Load manager. The front component is another name for the load manager. It completes all tasks necessary for the extraction and loading of data into the warehouse. To get the data ready for the data warehouse, these activities also involve transformations.
  • Query Manager. The term “backend component” can also refer to the query manager. It manages all user inquiries and conducts all related processes. This Data warehouse part works by submitting queries to the proper tables so that they can be run at the appropriate times.

Advantages of Data Warehouse

Here are some of the benefits of data warehouses.

  • Businesses might benefit from a data warehouse because it centralizes and makes accessible a wide variety of data from a variety of sources.
  • The data warehouse delivers reliable data on a wide range of business processes. It also allows for spontaneous queries and reports.
  • The Data Warehouse allows for the consolidation of disparate data sources, which eases the load on the manufacturing infrastructure.
  • A data warehouse can cut down on the amount of time it takes to perform analyses and generate reports.
  • When data is reorganized and integrated, it becomes more convenient for users to run reports and analyze the data.
  • Users are able to access crucial data from a variety of sources in a centralized data warehouse. Consequently, it frees up the user’s time formerly spent scouring several databases.
  • The data warehouse is where all of the past records are kept. This facilitates the examination of varying time frames and patterns for the purpose of forecasting.

Disadvantages of Data Warehouse

Here are some of the disadvantages of data warehouses.

  • Not the best choice for messy data.
  • The development and rollout of a data warehouse is an arduous and time-consuming process.
  • The data stored in a warehouse can get stale really rapidly.
  • Modifying data sources, indexes, and queries, as well as changing data types and ranges, can be challenging.
  • The data warehouse may appear simple at first glance, but it is actually too complex for most consumers.
  • Data warehousing projects, no matter how well managed, invariably end up taking longer and covering more ground than originally planned.
  • Users of the warehouse may eventually come up with their own sets of business regulations.
  • Companies must invest heavily in the learning and implementing processes.

Database vs Data Warehouse

A Data warehouse and a database serve similar purposes in terms of storing and managing data. However, there are a few significant distinctions that must be made. To begin, data warehouses can do analyses. They provide analytical queries for businesses to monitor and report on certain metrics. A database, on the other hand, is only a centralized repository for information. The primary function of a database is to provide safe, convenient storage for and access to data.

Furthermore, a database and a data warehouse work together to store and organize the vast volumes of information that businesses generate every day. A clothing manufacturer, for instance, might keep client data in one database and website analytics in another. A data warehouse will allow them to compare the two datasets over time to see patterns in consumer behavior. 

Let’s take a deeper look at the distinctions that exist between these two storage systems. 

#1.  OLTP vs. OLAP

One type of data processing system is known as online transaction processing (OLTP). This is the prevalent model for databases housing operational data for most companies. OLTP serves to facilitate the rapid resolution of day-to-day business inquiries by providing users with timely access to complete and correct data.

A data processing system known as online analytical processing (OLAP) prioritizes data analysis to drive decision-making over performance and regular use. The integration of OLAP systems with business intelligence solutions simplifies the task of answering inquiries and delivering in-depth reports to business stakeholders for non-technical managers and executives.

In most cases, a database is the go-to OLTP solution for businesses that want quick access to their data. For data scientists, BI tools, and other large-scale analytics use cases, an OLAP solution that can aggregate both real-time and historical data is ideal for data warehouse systems.

#2. Use Cases

A Data warehouse and database are not interchangeable, and they serve very diverse purposes.

Small, discrete transactions are the bread and butter of databases since they are what drive an organization’s day-to-day operations. An online ticket purchase, a bank account transfer, and the addition of new patient information are all instances of such activities.

In addition, questions regarding the past, present, and future of a company that demand a more in-depth level of study are best suited to data warehouses. This includes tasks like data mining from different databases to discover previously unknown insights about client habits and purchase trends.

#3. Reporting and Analysis

While OLTP databases do allow for some reporting and analysis, it is more challenging due to the normal format of the data. Further, for optimal performance, databases often only store the most recent information, making it impossible to conduct historical queries.

In contrast, data warehouses are purpose-built facilities that were developed initially to facilitate reporting and analysis. Data from the present as well as the past is available to users, expanding the scope of possible conclusions.

#4. Data Structure

Information in databases has been “normalized.” With normalization, you won’t have to worry about saving the same bit of information again. By eliminating the need to store the same information in multiple places, the database becomes more consistent and, by extension, more reliable.

Data normalization involves dividing the information into numerous tables. Separate data entities are represented by tables. A database tracking BOOK SALES, for instance, would split its data into three tables: one for BOOK details, one for the SUBJECT of each book, and one for the PUBLISHER.

By standardizing data, we can guarantee that our database will be both memory- and disk-efficient. However, it is inefficient in terms of queries. Normalized databases might be difficult to query because of their structure. Data in a data warehouse is frequently denormalized and contains repeated data for ease of access since businesses want to run complicated queries on that data.

#5. Service Level Agreements

Since databases are used for online transaction processing (OLTP), their availability is critical and must exceed 99.9%. When online transaction processing (OLTP) databases go down, it can cause major problems and possibly shut down operations.

However, a data warehouse is largely utilized for back-end analysis, thus downtime is not as much of an issue for them. In fact, most data warehouses have planned maintenance windows during which new data is added. Everyone benefits from the downtime because it allows for faster uploads during times when users don’t need access to data. By turning off all except the bare necessities, your process will speed up and become more exact.

#6. Optimization

When data is updated (added, changed, or removed), a database is designed to do it as quickly and efficiently as possible. Transaction processing efficiency necessitates lightning-fast database response times. One of the most crucial features of a database is its ability to keep track of every transaction that takes place within the system, as without this feature, the business would not last long.

While a data warehouse is designed to process a small number of complex queries on a huge, multi-dimensional dataset in a short amount of time.

Is a Data Warehouse Bigger Than Database?

Yes. It is possible to store data in all of these locations using database software; but, in terms of the volume of data stored, a data warehouse is significantly larger than a database. The data warehouse serves mostly for the purposes of data mining and data analysis in order to provide decision-makers with assistance.

Operational Database vs Data Warehouse

There are a few distinct kinds of database systems that serve various needs in businesses, including an operational DBMS and a data warehouse.

When it comes to the day-to-day operations of a business, nothing but the best will do when it comes to a database system. For the purpose of managing and controlling the processes that produce and deliver the organization’s products or services, these systems are intended for transaction processing. Database systems that are actively used include those for managing client relationships, stock levels, and orders.

A Data warehouse, on the other hand, is built to aid in analytical and decision-making processes within a company. These platforms are used to bring together information from several operating systems into one coherent perspective. Business intelligence, data analysis, and decision-making are all assisted by data warehouses because of how well they perform queries and generate reports. 

The following are some of the most noticeable distinctions between a data warehouse and an operational database system:

  • Purpose. To keep things running smoothly, businesses rely on operational database systems, while a data warehouse aids in strategic planning and in-depth research.
  • Data Structure. Data in operational database systems is often standard, or structured into numerous related tables, to lessen the likelihood of data duplication and increase the reliability of the data it contains. However, data warehouses often employ a denormalized data structure, meaning that the information is stored in fewer, more efficient tables for the purposes of reporting and analysis.
  • Data Volume. A Data warehouse can hold years’ worth of data, but operational database systems only need to keep track of the most recent data.
  • Performance. Operational databases are optimized for high-volume, high-speed transaction processing. But data warehouses are built for querying and reporting and handle complicated analytical queries on massive data sets.

Transactional Database vs Data Warehouse

The fundamental function of a transactional database is to capture data, while the major function of a data warehouse database is to provide answers to analysis queries that are crucial to the success of your business.

Online transaction processing (OLTP) technologies, including transactional databases, are designed to record and process transactions in real time. Take the case when a customer receives cash from an ATM but the transaction is not reflected in the bank’s records. The bank wouldn’t be able to survive if this kept happening regularly. Thus, the banking system is structured to ensure that your transaction is recorded while you wait at the ATM. Because this system is optimized for writing, queries (read operations) are slow.

On the other hand, a data warehouse (DW) is a type of database built with the express purpose of making data analysis and querying easier. Data in these databases is read-only, but it may be queried and analyzed in a more time- and resource-efficient manner than databases used in traditional online transaction processing (OLTP) applications. In this regard, an OLAP system is made to be easily read by its users. By keeping your business intelligence solution separate from your application database, you may avoid taking your bank and ATMs offline whenever the CFO requests a report.

To avoid the circumstance where a novice user receives the application database diagrams and is told to find the needle of data in the proverbial haystack of table proliferation, DW is also better specified and maintained. It’s also quicker and more reliable in answering questions.

In addition DWs simplify, standardize, and typically de-normalize table structures, improving analysis quality. Thus, you preserve only the necessary data in simpler, extensively documented tables and reduce table connections and query complexity, as shown later.

Data Lake vs Database vs Data Warehouse

Here are some of the distinctive differences between these three storage systems.

  • Structure. Databases adhere to strict schema constraints and follow a predetermined structure. Data warehouses and data lakes, on the other hand, can store all three types of data (structured, semi-structured, and unstructured).
  • Purpose. Transaction processing in real time is where databases really shine. The primary purpose of a data warehouse is to facilitate analysis and reporting. Data exploration and complex analytics are just two examples of what may be done with the raw materials stored in a data lake.
  • Transformation. Databases can only store set up data and demand uniformity in database schemas. Data warehouses and data lakes provide the flexibility to change schemas and transform data on the fly.
  • History. Most databases only keep recent information. Data warehouses collect and organize past datasets for use in forecasting trends and making sound choices. In order to facilitate comprehensive data exploration, data lakes can hold not just historical data but also data in real time.

Why Not Use a Data Warehouse?

Simply said, databases handle transactional data for operational purposes, while data warehouses store and analyze massive amounts of data for strategic decision-making. Decisions and expansion can be fed by any and all available data, from user interactions on a website to sales and stock information.


In conclusion, both data warehouses and databases are effective ways to store massive amounts of data. Both are extremely valuable in the corporate world, but their benefits differ. Their value in today’s information-based economy is immense. This ingenuity, however, is conditional on the objectives of a business.

Database and Data Warehouse FAQs

Should I Use a Data Warehouse or Database?

The primary purpose of creating and using a database is to store information. When it comes to analyzing data, though, a data warehouse comes in handy. Large analytical queries are best handled by the data warehouse, while a database is typically designed for read-write operations on a transaction-by-transaction basis.

Is Mysql a Database or Data Warehouse?

MySQL is not a lightweight DBMS; it is a complete database management system. Due to its relational format, MySQL is arguably the simplest database to work with and learn. However, some of the other choices above may be more appropriate for widespread implementation.

Is Snowflake a Data Warehouse?

Yes. Snowflake’s architecture divides the central data storage layer from the data processing layer, much like BigQuery’s. As a result of its superiority over its competitors in terms of performance, scalability, and query optimization, Snowflake is presently the most popular data warehouse on the market. The catch is that Snowflake is typically more expensive, so you’ll have to factor that in.

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