Online transaction processing or OLTP and online analytical processing or OLAP seems to be very identical terms. Still, these are two different approaches in terms of functionality in enterprise database administration scenarios. OLAP uses more complex querying 4mchanisms to analyze the historical compared to a typical OLTP system. There are several differences that you need to know about.
OLTP systems help to capture and maintain transactional data in databases. Every transaction in OLTP involves individual records in the database, made of various columns and fields. Major examples of OLTP systems include credit card and banking systems. Retain checkout systems are also classic examples of OLTP. In the case of OLTP, the focus is mostly on processing the transaction quickly. OLTP databases are frequently read, written, and updated. In case of any transaction failure, the OLTP built-in system logic will ensure the data’s integrity in the DB.
OLAP systems apply more complex queries on a larger amount of historical data. These data are aggregated from the OLTP databases and various other sources and used for analytics, Bi, and other operational projects. The emphasis of OLAP systems is to optimize the response time by executing complex queries with optimum precision. Each such query may involve more than one column of data aggregated from various rows in the database. An example of OLAP is the year-over-year financial report or online marketing response trends. Analytics and business decision-makers benefit largely from OLAP DBs and data warehouses using many reporting tools, which can turn data into silos of usage information. The OLAP system’s query failure may not delay or interrupt the transaction processing for customers, but it can tamper with the insights generated for business intelligence.
Data from many OLTP database systems may be ingested into an OLAP system using extraction, transformation, and loading (usually known as ETL). Using a good ETL tool, the users can effectively collect data from various sources and send it to the designation like an OLAP data warehouse. It will be queried using business intelligence and analytical tools to gain better insights.
Comparing OLTP vs. OLAP
As we have seen above, OLTP is more operational, whereas OLAP can be found more information for decision making. For any support in choosing and implementing your database systems, consult with expert providers like RemoteDBA.com. Below, let us glance at the major features of these two types of data processing approaches by covering the fundamental differences between them and how they work together.
|Online Transactional Processing||Online Analytical Processing|
|Transactional capabilities||OLTP handles a large number of smaller transactions well.||OLAP handles larger data volumes and complex queries.|
|Querying||Simple queries, which are more standardized.||Can deal with more complex and diversified queries.|
|Database Operations||Based on standard commands like INSERT, DELETE UPDATE, etc.||Based on the commands like SELECT for data aggregation to facilitate reporting.|
|Source||Transactional data||Data aggregated from various transactions.|
|Design specifications||Based on the industry like banking, retail, production, and so on.||Subject-specific as inventory, sales, or marketing.|
|Responses timing||Millisecond responses||It may take many seconds to even hours based on the volume of data to be processed.|
|Updating data||Quick and short updates as initiated by the users.||Periodic updates of data based on schedules and batch jobs.|
|Purpose of functions||To run and control business operations in a real-time environment.||To plan and solve problems, identify the hidden patterns by analyzing data, and support the decision-making process.|
|Viewing data||Easy to understand list view of day-to-day transactions||Multi-dimensional view of data for better understanding and interpretation of the same from various perspectives.|
|Space needed||Small if you archive the historical data to be stored.||More space consuming as the need is to aggregate huge datasets.|
|Data backup and restoration||Needs regular backups to ensure business continuity and governance compliances.||Any lost data to be reloaded from the OLTP stores if regular backups are performed at OLTP.|
|Productivity outcomes||Help increase the productivity of users||Help to increase the productivity and accuracy of the data analytics, business managers, and business decision-makers.|
|Design||Normalized DBs for more efficiency||Denormalized databases to ensure better analytical proficiency.|
|Ideal users||Customer-facing executives as clerks, support professionals, online shoppers, etc.||Knowledge workers and decision-makers such as business analysts, data scientists, etc.|
OLTP usually offers a quick record of the ongoing business activities, whereas OLAP is mostly validated by the insights generated from the transactional data, which is compiled over time. Historical perspective of data will empower better forecasting of data-driven insights. Still, as in business intelligence administration, the insights generated using OLAP are only as good as the data that comes into these platforms.
To gain some actionable insights from the OLTP, you need to ensure that the data in it is properly extracted and transformed into a good data warehouse for analysis. Even though the programmers can do this, data ingestion can be better handled using some ETL tools. These tools will remove the need for constant maintenance of codes as the source APIs change, or the business needs change. A good ETL tool will help optimize the OLTP data ingestion and free up the staff’s time and effort to be reinvested into activities that add more value to the growth of the business.
While administering the OLAP applications, you cannot just pull the OLTP source data into it for processing. Instead, try to simplify taking out OLTP source data into a data warehouse in a sophisticated manner for OLAP to process. Choose an ideal tool based on your industry and the nature of data you are handling, which can scale your data accordingly and provide support when needed to stay ahead of the changes and track any insights as needed. Many such tools like Stitch ensure that the data population to warehouse is made simpler and easier. Using such tools can help you close the gap between OLTP and OLAP platforms. Many such tools also come for free and can be customized based on your needs.