ADBT Presentation

8/2/2019 ADBT Presentation

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Two commonly used acronyms in the database world are

OLTP and OLAP. OLTP stands for "Online TransactionalProcessing" and OLAP represents "Online AnalyticalProcessing." The purposes of the two systems are vastlydifferent, leading to different approaches in organizing andstoring the data.

OLTP (On-line Transaction Processing) is characterizedby a large number of short on-line transactions (INSERT,UPDATE, DELETE). The main emphasis for OLTP systems isput on very fast query processing, maintaining data

integrity in multi-access environments and an effectivenessmeasured by number of transactions per second. In OLTPdatabase there is detailed and current data, and schemaused to store transactional databases is the entity model(usually 3NF).

OLAP Vs OLTP



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OLAP (On-line Analytical Processing) is characterizedby relatively low volume of transactions. Queries are oftenvery complex and involve aggregations. For OLAP systemsa response time is an effectiveness measure. OLAPapplications are widely used by Data Mining techniques. InOLAP database there is aggregated, historical data, storedin multi-dimensional schemas (usually star schema).



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We can divide IT systems into transactional (OLTP)and analytical (OLAP). In general we can assumethat OLTP systems provide source data to datawarehouses, whereas OLAP systems help to

analyze it.



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Following is a simple comparison between the twoapproaches.

PURPOSE

OLTP: Main purpose is to automate operational procedures andprovide up-to-the minute information on current businessprocesses. Reporting tends to be workflow related, answeringqueries such as "which claims are still open?" and "which

customers are waiting for a response?“

OLAP: Main purpose of this is for reporting and dataanalysis. This is where users go to answer queries related tobusiness cause, important trends, and overall profitability.

Reports from an OLAP are intended to help with general businessdecision making and future planning, answering queries such as"which demographic attribute makes customers less likely tosubmit a claim?" or "which insurance policies are most likely tobe purchased by those customers who purchase a boat policy?".



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DATA SOURCE

OLTP: Data is input by various clerks and office staff,

maintaining an operational log. There can be hundreds of users inputting data simultaneously, or there may be only oneuser. Regardless of the size, data is in a constant state of change.

OLAP: Data is rolled in from various data sources, most of which are likely OLTPs that are used in several differencebusiness offices. It may also contain data from flat files ordata mined from the web or other businesses. Regardless of the sources, data tends to be slowly updated in a scheduledmanner, such as nightly or weekly, allowing statistics to be

more easily gathered than in the dynamic OLTP.


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IMPORTANT METRICS

OLTP: In OLTP, throughput (number of transactions per

second) is vitally important, as is minimizing concurrencyconflicts. The point is to allow users to insert and update dataquickly so that their daily jobs are not disrupted. This meansdata organization and indexing should be done in a way thatincreases the insert and update speed, without being overlyconcerned about the speed of reporting.

OLAP: In OLAP, response time and query throughput are themost important metrics. Since the main purpose is reporting,users expect to be able to gather statistics quickly. Therefore,indexing and data organization may be significantly different

from the OLTP, increasing large query response time while notoptimizing for inserts and updates.



DATA ORGANIZATION

OLTP: In relational OLTP systems, the data is generally in

third-normal form. This lessens the amount of space requiredand allows fast inserts and updates on data that is logicallyinterrelated. For example, one could update just an event thatoccurred on a claim, without updating the entire claim itself.

OLAP: In relational OLAP systems, the data will usually be in

some sort of denormalized form, often in a star schemaformat based on "facts" and "dimensions." The flattenedschema allows the system to retrieve data by going to fewertables with clear relationships, which increases the speed of complex queries.



INDEXING

OLTP: Indexing can slow down inserts and updates because

both the table and the index have to be updated. Therefore,OLTP systems tend to have only a few indexes per table,representing the most common searches related to workflowreports.

OLAP: When done right, indexing significantly increases thespeed of large queries. It is not uncommon for an OLAP tableto have an index on every single column.



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PHYSICAL ARCHITECTURE

OLTP: Most OLTP databases are in a relational database form,

organizing information according to tables that are connectedby a common column, or "key“.

OLAP: Some OLAP databases may be stored in a relationalform, and thus are called ROLAP. Others may be stored in a

multi-dimensional engine which stores data in multi-dimensional arrays. These are called MOLAP.

DISK SPACE

OLTP: OLTPs are smaller, generally between a few hundredmegabytes and a hundred gigabytes. This is because thethird-normal form organization reduces data redundancy andspace-intensive indexes are kept to a minimum.



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OLAP:OLAPs can be much large, especially since data maycome from multiple OLTPs. The denormalized data andextensive indexing also takes up quite a bit of space. OLAPstend to run in the hundreds of gigabytes into the terabyterange.

In summary, OLAP and OLTP are similar in that they areterms used to describe storage of interrelated data. Butbecause the intended use of the systems are different, the

physical and logical architecture need to be different in orderto optimize performance.



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Thank You

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