Data warehousing systems are integral to running a business, helping data knowledge workers, business analysts, and data scientists make data-driven decisions. In addition, data warehousing systems have evolved from batch processing systems to current online processing systems that process data in real-time.
There are several online data warehousing systems, but Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) are the most popular. Although the acronyms OLTP and OLAP are incorrectly used interchangeably, there are significant differences between the two systems. Knowing the differences will help you better understand their various applications in the business.
Below are the critical differences between OLAP and OLTP.
1. Primary Uses
The key differentiator when deciding on a data processing system in the OLTP vs OLAP debate is the primary function of each system. Below is an overview of each online data warehousing system and its primary function.
An OLTP system processes database transactions with the day-to-day financial transactions that facilitate a company’s cash flow. Such transactions include sales, order processing, changes, and simple data queries.
A good example is the data processing system used by e-commerce platforms. Such systems track orders and updates to the information, such as creating a new account or updating a customer’s shipping address.
Another example is the data processing processor in an ATM, which processes customer transactions, including queries such as account balances. In addition, an OLTP system automatically deletes outdated data and keeps the data up to date for a specified period.
OLTP systems process extensive data from multiple databases with different fields and update the data as multiple users access the system. However, it doesn’t merge data from different fields.
While an OLTP system primarily processes transactions, the main function of OLAP systems is data analysis. OLAP systems use historical data from archives generated through the OLTP system to aggregate data to facilitate data analysis and provide helpful insights from different data sets.
Such systems allow business analysts to view data in a summarized report. Unlike OLTP systems, which require constant updates to maintain data integrity, OLAP systems require less frequent updates because their data aggregation function takes more time. Therefore, as a data analyst, you must use OLTP and OLAP systems in various ways.
2. Data Retrieval Speed
Although both systems process data online and in real-time, OLTP and OLAP data analysis systems differ in data retrieval. OLTP systems can process large amounts of data but have incredibly fast data retrieval speeds, calculated in as little as milliseconds. You can retrieve data from OLTP systems at supersonic speeds because these systems mainly process simple queries.
OLAP systems, on the other hand, perform much more complex calculations. Also, the data retrieved from an OLTP system is simple. In contrast, the data from OLAP systems can be customized to accommodate unique queries based on the user’s areas of interest. As a result, OLAP systems are slower than OLTP systems, taking anywhere from a few seconds to several minutes to retrieve and display the requested data.
3. Data Models Utilized
Another key difference between OLTP and OLAP is the data model used. While OLTP systems use normalized data systems, OLAP systems use denormalized systems.
According to a literature review, data normalization is a data collection and organization system that classifies data into tables and establishes relationships between the different tables. Normalized data systems allow system administrators, such as data workers and scientists, to divide larger data tables into smaller ones to improve cohesion.
The above overview highlights several benefits of normalized data, such as eliminating data inconsistencies and redundancies in data relationships. In addition, normalized data systems increase data security, enable faster index creation, and maximize the overall value of the data in question.
On the other hand, denormalized data models used in OLAP systems facilitate the addition of pre-computed data to normalized data. Extracting and collecting data from different normalized data tables facilitates data aggregation.
The main advantage of a denormalized data system model is the speedup of JOIN clause queries, which take much longer in normalized data systems. Minimizing JOINS improves data readability and overall data system performance.
4. Data System Designs
OLTP and OLAP data systems are designed differently to accommodate different performance capacities. OLTP data systems are designed to be more industry-specific, while OLAP data systems are designed to be predominantly topic-oriented.
Different industries such as banking, retail, hospitality, online booking systems, manufacturing, and more need to capture unique data sets. Therefore, OLTP data systems have an industry-specific design to cater to these individual transactions.
In contrast, OLAP systems use a topic-specific data design to facilitate data aggregation and analytics reporting. Typical subject areas/metrics considered when designing an OLAP data system include sales, marketing, inventory, and other indexing categories that facilitate informed decision-making.
5. Storage Requirements
In distinguishing between OLTP and OLAP data processing systems, memory and storage considerations are critical differentiating factors. According to a report, both data processing systems require sufficient memory to support memory-optimized tables and online workloads.
As mentioned earlier, OLTP systems are designed industry-specific, and industry requirements vary. Besides the different data requirements, the data volume of each OLTP system also depends on the organization’s transaction volume. Therefore, OLTP systems that process more transactions require greater storage capacity than systems that process fewer transactions.
In addition to transaction volume and online workload support, some OLTP systems reach data and require more storage capacity to accommodate the data archive. OLAP systems, however, have high volume because they store and process archived data and larger data sets to enable comprehensive data aggregation.
6. Backup And Recovery
In addition to data storage, data protection is also an essential element in data storage systems. OLTP and OLTP systems have special backup and recovery requirements.
For starters, OLTP systems require constant data backup because their primary function is data modification, and inconsistent data backup compromises system data integrity. In addition, some OLTP systems provide incremental backups to maintain data integrity.
Also, data recovery is paramount in OLTP systems due to frequent updates. In OLAP systems, on the other hand, data backup and recovery are less important because these systems keep the current data the same.
7. Data Availability and Integrity
Data availability is critical to the integrity of the system. This is why data processed in OLTP systems is available 24/7 to authorized parties.
On the other hand, data from OLAP systems may not always be available due to the data aggregation process. However, data availability only affects data integrity in such a system.
Conclusion:
OLTP systems are primarily operational and focus on data changes, while OLAP systems are informational and help fulfill queries. Nonetheless, both systems are critical because they fulfill an essential element of business data management. In addition, data workers can combine both systems to use their data efficiently.
