OLTP stands for online transaction processing (OLTP), whereas OLAP stands for online analytical processing. Both OLTP and OLAP are online processing systems. The basic difference between OLTP and OLAP is that OLTP works with the processing of transactions, OLAP is more focused on analytical processing. Transactions refer to independent processes that are responsible for managing the data in a database. Relational Databases store the transactional data. Let us understand these online processing systems better.
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The essential functions of both these systems are:
- OLTP is responsible for collecting, storing, and processing data from the transactions in real-time.
- OLAP is responsible for analysing historical data from OLTP systems using queries.
In this article, we have enlightened the essential aspects of OLTP vs OLAP which will help you understand both OLTP and OLAP.
OLTP vs OLAP
The following topics will be covered in this OLTP vs OLAP:
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What is OLTP?
The OLTP system maintains the transaction data in databases. There is a separate database record for every transaction. Organisations use OLTP for maintaining day to day transactions. It supports transactions in applications with 3-tier architecture.
OLTP focuses on processing transactions as fast as possible since the databases are read, written, and updated regularly. It also promises data integrity in case of any transaction failure with the use of pre-built system logic.
Examples of OLTP:
- Credit card activity
- Scanning at checkout kiosks in retail stores.
Features of OLTP:
- It manages transations in real-time.
- It focuses on processing transactions quickly.
- Relational Databases store data in this system.
- They manage the transactions governed by the ACID properties.
- They modify the data in the databases.
What is OLAP?
Organisations mainly use OLAP for projects in analytics, business intelligence, and data mining. It is a collection of software tools that provides data analysis for business decision-makers. This system's function is to apply complex queries to big aggregated historical data from the OLTP databases, among other sources.
OLAP focuses on response time to the complex queries. Every query is independent with columns of aggregated data from different rows.
Databases and Data warehouses in OLAP enable the analysts and other decision-makers to make use of custom reporting tools to extract information from data. When it comes to failure in queries, the processing of transactions is not interrupted for customers. However, it can disrupt the accuracy of business intelligence information.
Examples of OLAP:
- Annual financial performance
- Trends in marketing leads
Features of OLAP:
- They manage historical data.
- These systems do not make changes to the transactional data.
- Their primary objective is data analysis and not data processing.
- Data warehouses store data in multidimensional format.
ETL Role in OLTP and OLAP:
ETL stands for extract, transform, and load. In simple words, it connects OLTP and OLAP. The OLAP systems consume data from one or more OLTP databases through a process called extract, transform, load (ETL). Users use the ETL tool to collect and send data to a destination such as an OLAP data warehouse, where it is queried through analysis and business intelligence tools to obtain information.
OLTP vs OLAP: Difference Between OLTP and OLAP
While in OLTP, the main objective is data processing; in OLAP, it is data analysis.
Let's take a glance at the primary differences between both processing systems in the comparison table.
|Data source||Operational data. OLTP systems are the original data sources.||Consolidation data. OLAP data comes from the OLTP databases.|
|Use||Responsible for controlling and running basic business tasks.||Responsible for planning, problem-solving and supporting business decisions.|
|Queries||Queries are standard and straightforward.||Complex queries.|
|Speed of processing||Fast speed.||Complex queries can take a long time to process.|
|Backup and recovery||Frequent complete backups along with incremental backups.||No regular backups. Instead, OLTP data is reloaded as a recovery method.|
|Process||Online transactional system.||Online analysis and data retrieving process.|
|Method used||Uses traditional DBMS.||Uses a data warehouse.|
|Quality of data||Detailed organisation of data.||Disorganised data.|
|Nature of audience||Market-oriented process.||Customer-oriented process.|
|Database design||Application-oriented design.||Subject-oriented design.|
|Types of users||Clerks, online shoppers, etc., use OLTP.||Data knowledge workers like managers and CEOs use OLAP.|
|Productivity||Enhances the productivity of the user.||Enhances the productivity of business analysts.|
|Updates||The user starts the data updates, which are short and fast.||Regular refreshing of data with long, scheduled batch jobs.|
OLTP vs OLAP - Advantages and Disadvantages
What are the Benefits of OLTP and OLAP methods?
Advantages of OLTP:
- Day to day transactions in an organisation is easily regulated.
- Increases the organisation’s customer base as it simplifies the individual processes.
Advantages of OLAP:
- Businesses can use a single multipurpose platform for planning, budgeting, forecasting, and analysing.
- Information and calculations are very consistent in OLAP.
- Adequate security measures are taken to protect confidential data.
What are the Drawbacks of OLTP and OLAP methods?
Disadvantages of OLTP:
- Hardware failures in the system can severely impact online transactions.
- These systems can become complicated as multiple users can access and modify data at the same time.
Disadvantages of OLAP:
- Traditional tools in this system need complicated modelling procedures. Therefore, maintenance is dependent on IT professionals.
- Collaboration between different departments might not always be possible.
In this OLTP vs OLAP article, we discussed the key features of OLTP and OLAP systems. We also looked at an overview of the differences between OLTP and OLAP in a comparison table. In summary, OLTP and OLAP are essential parts of the data warehousing domain.
We hope that learning about the differences between the methods in depth will help you build your data engineering foundation.