How a data warehouse can support OLTP? Briefly explain.
Online transaction processing (OLTP) is the gathering, processing and updating of input information. In other words, OLTP is gathering and processing of transactional information and updating existing information to reflect transactional changes. Database management systems are the technology tools that directly support OLTP which are most often referred to as operational databases. The valuable information stored in these transactional databases forms the basis for business intelligence. The data contained in these databases, which is in raw form, is highly normalized to reduce data duplication and increase efficiency of the transactions.
A data warehouse is a special form of a database that contains information gathered from operational (or transactional) databases for the purpose of supporting the decision-making process. The data stored in these databases, which is in summarized form instead of its raw form, is lightly normalized or even not-normalized at all increasing the duplication.
Also the OLTP databases are highly affected by the front-end applications developed by some programmer or developer so provide only the fixed type of data access and very little flexibility in forming the queries limiting the ability to retrieve the intelligence stored in database thus if the decision maker has to use the OLTP system for decision making, he is forced to use only those queries that have been pre-written thus the process of decision making is driven not by the decision maker. Data warehouse, on the other hand, coupled with the OLAP, provides the interactivity between the decision support system and decision maker which is required.
So, typically, a data warehouse and OLTP systems works separately from each other and even reside on separate server machines.
Do you agree that OLAP is a Data Warehouse tool? Explain.
Online analytical processing, or OLAP (also known as multidimensional analysis), is actually an approach to on-line retrieval and analysis of data to reveal business trends and statistics not directly visible in the data directly retrieved from a data warehouse but not a tool itself. However, this term is also used to describe the tools that provide this functionality. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management, budgeting and forecasting, financial reporting as well as other similar areas.
Different vendors have provided multidimensional OLAP for general ledger (G/L) operations. In your opinion an organization should use these packages or try to build an in-house solution? What are advantages / drawbacks of using these packages?
In my opinion, the ready-made OLAP solutions provide a limited integration level with the existing business process. However, it can suffice the purpose for very small or small scale applications but as the business grows, there are many growing needs and versatile requirements for the extraction of business intelligence stored in the data warehouse which these ready-made solutions fail to provide. A custom-made solution, on the other hand is based on the exact requirement for that specific business process and is flexible enough to provide the extensibility without compromising other factors like efficiency, interpretability etc. These ready-made OLAP solutions, however, have the benefit of minimum requirement for the technical services which are required otherwise to build it from scratch.
The integration of any OLAP solution to a General Ledger is critical to the reporting and operational effectiveness of the solution for that business. Many General Ledger manufactures having get this wrong and their clients end up wasting their valuable resources which, in turn, results in clients developing and integrating their own OLAP reporting solutions
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