The Difference Between a Data Warehouse and a Database
Does your business manage a great deal of transactions every day? Do you have years of historical data you need to break down to improve your business? Great! At that point you need a database and a data warehouse… however which data goes where?
Databases and data warehouses are the two frameworks that store data. However, they serve very different purposes. In this article, we’ll clarify what they do, the key differences among them, and why utilizing them successfully is fundamental for you to grow your business.
We’ll start with some significant level definitions before giving you more itemized clarifications.
What is a Database?
A database stores real-time information about one particular part of your business: its main job is to process the daily transactions that your company makes, e.g., recording which items have sold. Databases handle a massive volume of simple queries very quickly.
What is a Data Warehouse?
A data warehouse is a framework that arranges data from various sources within an organization for reporting and investigation. The reports created from complex queries within a data warehouse are utilized to settle on business choices.
A data warehouse stores historical data about your business so you can investigate and extract bits of knowledge from it. It doesn’t store current information, nor is it updated in real-time.
Data Warehouse vs. Database
Let’s dive into the main differences between data warehouses and databases.
Processing Types: OLAP vs OLTP
The most noteworthy difference among databases and data warehouses is the manner by which they process data.
Databases utilize OnLine Transactional Processing (OLTP) to erase, insert, replace, and update large numbers of short online transactions rapidly. This sort of processing promptly responds to user requests, as is utilized to process the everyday operations of a business in real-time. For instance, if a user needs to reserve a lodging utilizing a web based booking form, the process is executed with OLTP.
Data warehouses utilize OnLine Analytical Processing (OLAP) to break down huge volumes of data rapidly. This process enables analysts to take a gander at your data from different perspectives. For instance, despite the fact that your database records deals data for every moment of every day, you may simply need to realize the aggregate sum sold every day. To do this, you have to gather and aggregate the business data together for every day. OLAP is explicitly intended to do this and utilizing it for data warehousing 1000x faster than if you utilized OLTP to perform a similar estimation.
A database is streamlined to update (include, alter, or erase) data with most extreme speed and effectiveness. Response times from databases should be extremely speedy for effective transaction processing. The most important part of a database is that it records the write operation in the framework; an organization won’t be in business very long if its database didn’t make a record of every purchase!
Data warehouses are streamlined to rapidly execute a low number of complex queries on large multi-dimensional datasets.
The data in databases are normalized. The objective of normalization is to reduce and even kill data redundancy, i.e., storing a similar bit of data more than once. This reduction of copy data prompts increased consistency and, along these lines, more accurate data as the database stores it in just one spot.
Normalizing data splits it into various tables. Each table represents a separate entity of the data. For instance, a database recording BOOK SALES may have three tables to signify BOOK information, the SUBJECT covered in the book, and the PUBLISHER.
Normalizing data ensures the database takes up minimal disk space and so it is memory efficient. However, it is not query efficient. Querying a normalized database can be slow and cumbersome. Since businesses want to perform complex queries on the data in their data warehouse, that data is often denormalized and contains repeated data for easier access.
Databases typically simply process transactions, however it is additionally conceivable to perform data examination with them. However, top to bottom exploration is trying for both the user and computer because of the normalized data structure and the large number of table joins you have to perform. It requires a gifted developer or analyst to create and execute complex queries on a DataBase Management System (DBSM), which occupies a ton of time and computing resources. Moreover, the examination doesn’t dive deep – as well as can be expected get is a one-time static report as databases simply give a depiction of data at a particular time.
Data warehouses are intended to perform complex scientific queries on large multi-dimensional datasets in a straightforward manner. There is no compelling reason to learn progressed theory or how to utilize refined DBMS software. Not exclusively is the examination simpler to perform, yet the results are significantly more helpful; you can plunge deep and perceive how your data changes over time, rather than the preview that databases provide.
Databases process the day-to-day transactions for one aspect of the business. Therefore, they typically contain current, rather than historical data about one business process.
Data warehouses are used for analytical purposes and business reporting. Data warehouses typically store historical data by integrating copies of transaction data from disparate sources. Data warehouses can also use real-time data feeds for reports that use the most current, integrated information.
Databases support a large number of concurrent users since they are updated in real-an ideal opportunity to reflect the business’ transactions. In this way, numerous users need to interact with the database all the while without influencing its performance.
However, just a single user can change a bit of data at once – it would be disastrous if two users overwrote similar information in different manners simultaneously!
In contrast, data warehouses support a limited number of concurrent users. A data warehouse is separated from front-end applications, and utilizing it includes writing and executing complex queries. These queries are computationally costly, thus just few individuals can utilize the framework all the while.
Database transactions usually are executed in an ACID (Atomic, Consistent, Isolated, and Durable) compliant manner. This compliance ensures that data changes in a reliable and high-integrity way. Therefore, it can be trusted even in the event of errors or power failures. Since the database is a record of business transactions, it must record each one with the utmost integrity.
Since data warehouses focus on reading, rather than modifying, historical data from many different sources, ACID compliance is less strictly enforced. However, the top cloud providers like Redshift and Panoply do ensure that their queries are ACID compliant where possible. For instance, this is always the case when using MySQL and PostgreSQL.
Database vs. Data Warehouse SLA’s
Most SLAs for databases state that they must meet 99.99% uptime because any system failure could result in lost revenue and lawsuits.
SLAs for some really large data warehouses often have downtime built in to accommodate periodic uploads of new data. This is less common for modern data warehousing.
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
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
Database vs. Data Warehouse Comparison
|Processing Method||OnLine Transaction Processing (OLTP)||OnLine Analytical Processing (OLAP)|
|Optimization||Deletes, inserts, replaces and updates large numbers of short online transactions quickly.||Rapidly analyze massive volumes of data and provide different viewpoints for analysts.|
|Data structure||Highly normalized data structure with many different tables containing no redundant data.|
Thus, data is more accurate but slow to retrieve.
|Denormalized data structure with few tables containing repeat data.|
Thus, data is potentially less accurate but fast to retrieve.
|Data timeline||Current, real-time data for one part of the business||Historical data for all parts of the business|
|Data analysis||Analysis is slow and painful due to the large number of table joins needed and the small time frame of data available.||Analysis is fast and easy due to the small number of table joins needed and the extensive time frame of data available.|
|Concurrent users||Thousands of concurrent users supported.|
However, only one user can modify each piece of data at a time.
|Small number of concurrent users.|
|ACID compliance||Records data in an ACID-compliant manner to ensure the highest levels of integrity.||Not always ACID-compliant though some companies do offer it.|
|Uptime||99.99% uptime||Downtime is built-in to accommodate periodic uploads of new data|
|Storage||Limited to a single data source from a particular business function||All data sources from all business functions|
|Query type||Simple transactional queries||Complex queries for in-depth analysis|
|Data summary||Highly granular and precise||As granular and precise as you want it to be|
Presently you understand the difference between a database and a data warehouse and when to utilize which one. Your business needs both a successful database and data warehouse solution to truly prevail in the present economy.