Benefits of OLAP and OLTP in Data Management.

Explain how the separation of OLTP and OLAP systems benefits overall organizational data management strategy.

First lets understand what is OLTP and OLAP:

OLTP(Online Transaction Processing): is a type of data processing that consists of executing a number of transactions occurring concurrently.For example online banking, shopping,order entry etc.

Definition according to IBM:

OLTP: enables the real-time execution of large numbers of database transactions by large numbers of people, typically over the internet.

In lay man terms, it processes real time data.

NB:A database transaction is a change, insertion, deletion, or query of data in a database.

OLAP (Online Analytical Processing): a type of data processing that involves numerous real-time transactions executed concurrently by many users.

In lay man terms, it process historical data most for analysis.

Understanding the benefits of OLAP and OLTP systems separation on data management strategy

Now, that we have a basic comprehension of the systems we can now delve into their various individual prowess:

• Data formatting: OLAP systems use multidimensional data models; in cube format, thus you can view the same data from different angles. Enabling it to handle complex queries, facilitating in-depth analysis for decision making.

OLTP systems uses uni-dimensional data models that organizes data into tables on one data aspect. Ensuring data accuracy and consistency, crucial for maintaining the reliability of organizational operations

• Data Architecture: OLAP architecture prioritizes data read over data write operations. With this capability it is able to support reporting,data mining and other analytical tasks. Availability is a low priority concern ad the primary use case is analytics.

OLTP architecture prioritizes data write operations which helps it to handle simple, frequent transactions efficiently.
Availability is a high priority due to its significance in daily business operations.

• Performance: OLAP processing times can vary from minutes, hours to days depending on the type and volume of data. Data updates in OLAP databases utilize batch processing , where by you periodically process data in large batches then upload the batch to the system all at once. Crucial for integrating data from multiple sources, providing a comprehensive view of the organization.

OLTP processing times in milliseconds or less updates are on real time and are initiated by you or your users. Stream processing is the most used where high volume data moves in a continuous incremental manner with the goal of low-latency processing.