Data Mart

A data mart is a subset of an organizational data store, usually oriented to a specific purpose or major data subject, that may be distributed to support business needs.[1] Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse, though in the bottom-up data warehouse design methodology the data warehouse is created from the union of organizational data marts.

In practice, the terms data mart and data warehouse each tend to imply the presence of the other in some form. However, most writers using the term seem to agree that the design of a data mart tends to start from an analysis of user needs and that a data warehouse tends to start from an analysis of what data already exists and how it can be collected in such a way that the data can later be used. A data warehouse is a central aggregation of data (which can be distributed physically); a data mart is a data repository that may or may not derive from a data warehouse and that emphasizes ease of access and usability for a particular designed purpose. In general, a data warehouse tends to be a strategic but somewhat unfinished concept; a data mart tends to be tactical and aimed at meeting an immediate need.

One writer, Marc Demerest, suggests combining the ideas into a Universal Data Architecture (UDA). In practice, many products and companies offering data warehouse services also tend to offer data mart capabilities or services.

There can be multiple data marts inside a single corporation; each one relevant to one or more business units for which it was designed. DMs may or may not be dependent or related to other data marts in a single corporation. If the data marts are designed using conformed facts and dimensions, then they will be related. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data.[2] This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.

Design schemas:

Star Schema or dimensional model is a fairly popular design choice, as it enables a relational database to emulate the analytical functionality of a multidimensional database.

Snowflake Schema Advantages of creating a data mart

Easy access to frequently needed data

Creates collective view by a group of users

Improves end-user response time

Ease of creation

Lower cost than implementing a full Data warehouse

Potential users are more clearly defined than in a full Data warehouse

Dependent data mart

According to the Inmon school of data warehousing, a dependent data mart is a logical subset (view) or a physical subset (extract) of a larger data warehouse, isolated for one of the following reasons:

A need for a special data model or schema: e.g., to restructure for OLAP

Performance: to offload the data mart to a separate computer for greater efficiency or to obviate the need to manage that workload on the centralized data warehouse.

Security: to separate an authorized data subset selectively

Expediency: to bypass the data governance and authorizations required to incorporate a new application on the Enterprise Data Warehouse

Proving Ground: to demonstrate the viability and ROI (return on investment) potential of an application prior to migrating it to the Enterprise Data Warehouse

Politics: a coping strategy for IT (Information Technology) in situations where a user group has more influence than funding or is not a good citizen on the centralized data warehouse.

Politics: a coping strategy for consumers of data in situations where a data warehouse team is unable to create a usable data warehouse.

According to the Inmon school of data warehousing, tradeoffs inherent with data marts include limited scalability, duplication of data, data inconsistency with other silos of information, and inability to leverage enterprise sources of data.

OLTP Online transaction processing

OLTP stands for Online transaction processing Online transaction processing, or OLTP, refers to a class of systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. The term is somewhat ambiguous; some understand a "transaction" in the context of computer or database transactions, while others (such as the Transaction Processing Performance Council) define it in terms of business or commercial transactions.OLTP has also been used to refer to processing in which the system responds immediately to user requests. An automatic teller machine (ATM) for a bank is an example of a commercial transaction processing application.
The technology is used in a number of industries, including banking, airlines, mailorder, supermarkets, and manufacturing. Applications include electronic banking, order processing, employee time clock systems, e-commerce, and eTrading. The most widely used OLTP system is probably IBM's CICS.

Requirements: Online transaction processing increasingly requires support for transactions that span a network and may include more than one company. For this reason, new OLTP software uses client/server processing and brokering software that allows transactions to run on different computer platforms in a network.
In large applications, efficient OLTP may depend on sophisticated transaction management software (such as CICS) and/or database optimization tactics to facilitate the processing of large numbers of concurrent updates to an OLTP-oriented database.
For even more demanding decentralized database systems, OLTP brokering programs can distribute transaction processing among multiple computers on a network. OLTP is often integrated into SOA service-oriented architecture and Web services.

Advantages: Online Transaction Processing has two key benefits: simplicity and efficiency.
Reduced paper trails and the faster, more accurate forecasts for revenues and expenses are both examples of how OLTP makes things simpler for businesses. It also provides a concrete foundation for a stable organization because of the timely updating. Another simplicity factor is that of allowing consumers the choice of how they want to pay, making it that much more enticing to make transactions.
OLTP is proven efficient because it vastly broadens the consumer base for an organization, the individual processes are faster, and it’s available 24/7.
Disadvantages:
It is a great tool for any organization, but in using OLTP, there are a few things to be wary of: the security issues and economic costs.
One of the benefits of OLTP is also an attribute to a potential problem. The worldwide availability that this system provides to companies makes their databases that much more susceptible to intruders and hackers.
For B2B transactions, businesses must go offline to complete certain steps of an individual process, causing buyers and suppliers to miss out on some of the efficiency benefits that the system provides. As simple as OLTP is, the simplest disruption in the system has the potential to cause a great deal of problems, causing a waste of both time and money. Another economic cost is the potential for server failures. This can cause delays or even wipe out an immeasurable amount of data.

OLAP On Line Analytical Processing,MOLAP,ROLAP,HOLAP

OLAP is an acronym for On Line Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is quickly becoming the fundamental foundation for Intelligent Solutions including Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making. Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are speedier. Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI). The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the row and column of the matrix; the measures, the values. Functionality: In the core of any OLAP system is a concept of an OLAP cube (also called a multidimensional cube or a hypercube). It consists of numeric facts called measures which are categorized by dimensions. The cube metadata is typically created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

Aggregations: It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data. The single most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities.
The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data [8]. Due to the potentially large number of aggregations to be calculated, often only a predetermined number are fully calculated while the remainder are solved on demand. The problem of deciding which aggregations (a.k.a. views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time as well. Many different approaches have been taken to view selection (which is NP-Complete), including greedy algorithms, randomized search, genetic algorithms and A* search algorithm
A very effective way to support aggregation and other common OLAP operations is the use of bitmap indexes.

Types: OLAP systems have been traditionally categorized using the following taxonomy.[9] Multidimensional
MOLAP:
MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP. MOLAP uses database structures that are generally optimal for attributes such as time period, location, product or account code. The way that each dimension will be aggregated is defined in advance by one or more hierarchies.
Relational:
ROLAP: ROLAP works directly with relational databases. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. Depends on a specialized schema design.

Hybrid: HOLAP
There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage. For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data, and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data. Comparison among these three OLAP's:
Each type has certain benefits, although there is disagreement about the specifics of the benefits between providers.
Some MOLAP implementations are prone to database explosion. Database explosion is a phenomenon causing vast amounts of storage space to be used by MOLAP databases when certain common conditions are met: high number of dimensions, pre-calculated results and sparse multidimensional data. The typical mitigation technique for database explosion is not to materialize all the possible aggregation, but only the optimal subset of aggregations based on the desired performance vs. storage trade off.
MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.
ROLAP is generally more scalable. However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.
Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use.
HOLAP encompasses a range of solutions that attempt to mix the best of ROLAP and MOLAP. It can generally pre-process quickly, scale well, and offer good function support.
Courtesy:Wikipedia