In our previous posts, we have discussed the basics of OLTP and OLAP. OLTP (On-line Transaction Processing) and OLAP (On-line Analytical Processing) are often compared as a theoretical topic. The data from OLTP databases can be made to be ingested into OLAP systems through the extract, transform, load (ETL) process. The ETL tools help the users to collect data from several sources and send it their desired destination, such as a data warehouse. In the data warehouse, insight can be gained by using analytics and business intelligence tools. This is how OLTP and OLAP are related. Both the OLTP and OLAP are online processing systems. OLAP is the reporting engine, whereas OLTP is the business process engine. OLAP is for multidimensional analytical queries like financial reporting, forecasting, etc. OLTP is a database modifying system. OLAP is a database query answering system.
OLTP is the source of the data to control and run fundamental business tasks. It reveals a snapshot of ongoing business processes. The inserts and updates initiated by end-users. OLAP data comes from the OLTP Databases.
When we buy from the big retail stores, a lot of tasks during checkout. The visible part is the invoice generation.
The items which we purchase are inserted in the database, loyalty points are automatically credited. The Stock of the purchased items are automatically calculated and auto ordered. So many advance tasks involve OLTP (online transaction processing) and subsequently, OLAP helps in the business process such analysis of sales, prediction of sales and also link to various tools which uses machine learning.
Apache Hadoop processes the historical data which is loaded in the HDFS. We can not use Hadoop as an OLTP database which is characterized by INSERT -UPDATE- DELETE. Hadoop does not provide any random access to the data stored. It provides access to historical data.