We are living at the age where the advancement of technology continues to rise. Today, organizations have different kinds of Information Technology Computer systems to manage the daily events and activities as well as to define their long term goals and strategies to maximize their profits, in the process reducing the Administrative and Operational overheads. We can divide these IT computer systems into two major types which are OLTP or Online Transaction Processing Systems and OLAP or Online Analytical Processing Systems. In general, we can assume that the function of these systems is defined by their names. As such OLTP systems provide the source data to data warehouses, whereas OLAP systems help to analyze it.
OLTP is illustrated by a large number of online transactions. Very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by the number of transactions per second are only some of the accents of the system. Detailed and current data and plan are available in the database of the system in order store the transactional databases in the entity model which deals with operational data. Operational data are those data involved in the Operation of a particular system. In OLTP system data are frequently updated and queried.
For example, the Online Ticketing system of a Theme Park use this type of IT system. Customers visit a website of the Theme Park to search for options for availability of tickets that they could purchase. A single purchase of tickets constitutes a Transaction. Getting the current stock and the rates of tickets, displaying the current stock and rates to the customer, reducing the stock of tickets based on the quantity bought by the customer; and then there is the financial part to the transaction of processing the payment are some of the activities involved on that single purchase.
The OLTP system’s job, here is to permit these kinds of Transactions wherein a good system could handle a large number of transactions all at once but the response time of website should not be affected by the large number of customers who access it. Not just theme parks, even other organizations like those that trade in various kinds of products, Financial Companies, Banks have spent huge amounts of money in developing and maintaining an efficient Online Transaction Processing system.
On the other hand, OLAP is might as well opposite to the OLTP since OLAP is illustrated by relatively low volume of transactions while the other is of large volume. Queries are often complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. In OLAP database there is aggregated, historical data, stored in multi-dimensional plans. Historical data are those that archived over a long period of time. Compare to the previous system, this system’s updates are very rare. Query need to access large amount of data and require huge number of aggregation. OLAP Queries have significant importance in strategic decision making. This helps the top level management in decision making.
However, OLAP system has its own concern of the handling complex Mathematical and Statistical formulas within a fraction of a second, and that too in large numbers. These calculations throw up results that would be used by Analysts and Decision Makers within an organization to analyze the results, and come up with suggestions to the Management in taking decisions that would help them in doing away with schemes and plans that are non-revenue generating, and at the same time investing in schemes that would increase profits and customer patronage.
If we try to get this Data from an OLTP system, it would be a real test of our patience. The structure of OLTP systems do not support faster processing of data to generate Aggregations and Summaries, the reason being they are purely Relational in nature. For Analytical Data, we need a technology that would support calculations and retrieval of data at a blazing speed, and that’s where the OLAP tools come in.
Since 1996, InetSoft has offered software platform for dashboards, visual analysis, and reporting to be used in conjunction with OLAP servers or any combination of open standards data sources.