Several businesses use online data processing systems to boost the accuracy and efficiency of their processes. The data must be used before processing it. Two primary data processing systems prevalent in the world of data science are OLTP and OLAP.
Data scientists frequently use them to ensure efficient data processing. These analytical and transaction processing systems work across the same domain, i.e., data processing; however, their processing approaches are widely different.
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What is OLAP?
OLAP (Online Analytical Processing) refers to a category of software tools that help you analyse data for informed business decisions. The system lets you study database information from multiple database systems simultaneously.
It develops a single platform for sufficing all business analysis requirements, including planning, budgeting, predicting, and analysis. Alternatively, it is referred to as a data warehouse created such that it can integrate various data sources for developing a unified database.
Generally, OLAP is a perfect choice for running data mining, challenging analytical calculations, business reporting functions, and business intelligence. It is suitable for analytical use cases because the data is available in denormalised form. The denormalised form allows it to support large analytical queries. The ability to quickly aggregate and calculate the underlying datasets makes it more suitable for analytical use cases.
Example of OLAP
Financial companies can use an OLAP system to evaluate their annual sales data. They input data about sales revenue, profit margins, customer descriptions, overhead expenses, location, and sales associates to formulate their sales strategies for the following year accurately.
Retail businesses can analyse data from their latest advertising campaign. They input the relevant data into their OLAP system to consolidate data, study trends, and forecast results for their next campaign. Consequently, they get easy-to-understand and ordered data sets. This example may help you to understand the OLTP vs OLAP better.
What is OLTP?
OLTP (Online transaction processing) supports transaction-based applications in a three-tier framework. Its key objective is to capture data in real time. It oversees the daily transaction of an organisation and uses traditional DBMS. Usually, it works on a huge number of small online transactions.
This system facilitates the real-time implementation of massive database transactions recorded by a large number of people. Many everyday transactions use OLTP systems, for example, ATMs, hotel reservations, in-store purchases, etc. Furthermore, the OLTP system can lead to non-financial transactions such as text messages and password changes.
Commonly, it is used for fast CRUD operations like delete, read, write, etc. The OLTP system performs operations incorporating easy database queries with comparatively few records and demanding quick response times.
Example of OLTP system
An online airline booking process needs the data to be inserted into the database. Once the required data is entered, the selected airline is available in the cart. Moreover, it handles concurrency when multiple users access the same website at once. In this example, OLTP can fully normalise the database to guarantee the optimisation and consistency of all transactional tasks.
It provides direct access to the database for end users. Moreover, the OLTP system stores record from the previous week or the past few days to perform transactions successfully.
Difference Between OLTP and OLAP
The following table highlights the differences between OLTP vs OLAP.
|Point of Comparison||OLAP||OLTP|
|Meaning||It is used as an online database query management system.||It is used as an online database modifying system.|
|Method used||It uses a data warehouse.
|It uses a standard database management system (DBMS).|
|Data source||Contains historical data from different databases.||Contains only current operational data|
|Focus||The systems let you obtain data for complex analysis. Usually, the queries work on enormous records to streamline business decisions.||The systems are perfect for including and removing databases and processing simple updates. Usually, the queries contain only one or a very small amount of records.|
|Processing time||The significant difference between OLAP and OLTP is that the response timeline is slower than OLTP. Since workloads use many read cycles, they use huge data sets.||The response times are faster than OLAP. The workloads contain simple read-and-write functionings through SQL (structured query language), requiring a lower storage space and time.
|Task||It offers a multi-dimensional view of various business tasks.||It shows a scenario of current business tasks.|
|Usage of data||Data usage takes place in planning, decision-making, and problem-solving.||Data usage takes place in fundamental everyday operations.|
|Normalised||The tables are not normalised in OLAP databases.||The tables are normalised (3NF) in OLTP databases.|
|Availability||They don’t update current data, so OLAP systems are not backed up quite frequently.||Due to transactional processing, OLTP systems frequently update data. So, they need frequent backups to uphold data integrity.
|Queries||The operation of queries may take hours because the involved data is huge.||The operation of queries is fast because they work only on 5% of the data.|
|Operations||Supports only read operation; write operation is rarely supported.||Supports both read and write operations.|
|Updates||The data is refreshed regularly due to long, scheduled batch operations.||The user starts data updates. They are short and quick.|
|Hardware failure||Only a few transactions are affected due to hardware failure||All transactions will be affected due to hardware failure|
|Type of audience||Customers||Market|
|Key drawbacks||To use OLAP tools, data-modelling expertise is required. Cooperation is inevitable across multiple business segments.||Being business-focused, any downtime leads to disordered transactions and loss of revenue, eventually harming your brand reputation.
Advantages of OLAP
The following list of advantages will help you better understand OLAP and OLTP differences.
- The key advantage of using an OLAP system is the consistency of data and calculations.
- It develops a single platform to fulfil all kinds of business analytical needs.
- A difference between OLAP and OLTP is the database size. The OLAP system’s database size is smaller than a data warehouse because all transactional data is not needed for trend analysis.
- It implements security limitations on objects and users to protect sensitive data.
Disadvantages of OLAP
- OLAP tools demand cooperation between personnel of different departments which may not always be possible.
- It provides poor computation ability, slow response time, high potential risk, and absence of interactive and analysis capability.
Advantages of OLTP
- It oversees the everyday transactions of an organisation.
- It broadens an organisation’s customer base by streamlining all the involved processes.
- It supports ACID Compliance, where the ACID stands for Atomicity, Consistency, Isolation, and Durability. The corresponding properties are advantageous for a database that registers financial transactions. OLTP system guarantees lossless transactions and upkeeps the ACID property within its databases.
- It guarantees that the transactions recorded in the database don’t compromise concurrency between various users. Hence, the users don’t need to wait for other users to finish their transactions.
- Since the concurrency is maintained, all users can access the updated data.
- It sustains a normalised database that guarantees data integrity at all steps during transactions.
Disadvantages of OLTP
- Online transactions are badly affected if the OLTP system encounters hardware failures.
- The systems enable multiple users to access and modify the same data simultaneously. Consequently, it may create an abnormal situation and increase risk.
- To accomplish concurrency, availability, and quick transactions, OLTP systems usually use transactions that include multiple company networks. So, a more decentralised system is required.
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The choice between OLTP and OLAP systems depends on your business objectives. Analysing the OLAP and OLTP difference can help you choose the most suitable system for your business. If you want a single platform for business insights, OLAP can help you discover value from huge amounts of data. If you want to manage daily transactions, OLTP is a suitable choice as it can quickly process a significant amount of transactions per second. In many cases, organisations use both OLTP and OLAP systems. Indeed, OLAP systems can be used to analyse data that results in business process enhancements in OLTP systems.
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