A Note On Mondrian Architecture

Since 1960 and beyond the need for an efficient data management and retrieval of data has always been an issue due to the growing need in business and academia. To resolve these issues a number of databases models have been created. Relational databases allow data storage, retrieval and manipulation using a standard Structured Query Language (SQL). Until now, relational databases were an optimal enterprise storage choice. However, with an increase in growth of stored and analyzed data, relational databases have displayed a variety of limitations. The limitations of scalability, storage and efficiency of queries due to the large volumes of data [1] [2].

The database used should be open and industry standard to allow easy integration with other applications and easy movement of data in the future. The database

Using the right combination of Hadoop products and the other platforms can be sensational in terms of analytics because it has the capacity to analyze analysis of petabytes of Web log data in large Internet firms, and now is being applied to similar analytic applications involving call detail records in telecommunications, XML documents in supply chain industries (retail and manufacturing), unstructured claims documents in insurance, sessionized spatial data in logistics, and a wide variety of log data from machines and sensors. Hadoop-enabled analytics are sometimes deployed in silos, but the

New data platforms have emerged from these basic needs and one of them is the data management platform (DMP). DMPs have quickly transformed the way companies gather and use information and the industry is full of great products that make data ecosystems more usable.

Built – in SQL aggregate and analytic functions facilitate access to and manipulation of data in the data warehouses and data marts.

An OLAP system is used for data analysis by knowledge workers, including managers, executives, and analysts and it is market-oriented.

Design, code and deliver user friendly multi-tier business intelligence solutions that utilize data warehouse/data mining technologies to consume data across various database platforms and data stores.

Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide strategic information to support decision-making. On-line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted from

Data mart is a simple form of a data warehouse that is focused on a single subject, such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data. De-normalization is the norm for data modeling techniques in this system. Online Analytical Processing or OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. OLAP system response time is an effectiveness measure that is used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas. OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. Online Transaction Processing or OLTP is characterized by a large number of short on-line transactions. OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. OLTP systems the number of transactions per second measures effectiveness; this contains detailed and current data. The schema used to store transactional databases is the entity model. Normalization is the norm for data modeling techniques in this system. Predictive Analysis is about

“The integration of advanced analytics into a transactional database is revolutionary. Today a majority of advanced analytic applications use a primitive approach of moving data from databases into the application tier to derive intelligence.

Emergence of big data generated by an increased number of data sources led the evolution of many data-handling tools. Storing and analyzing vast amounts of structured and unstructured data is a big challenge. Traditional relational databases such as Oracle, DB2, HANA, MySQL, and SQL Server still handle structured data for enterprise applications like ERP and CRM and financial systems. Most of these databases have added some level of in-memory features exception to SAP HANA, which runs the entire database in-memory so that users can gain insights into data faster.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

According to their uses, databases are also classified as transactional databases and data warehouses. A transactional database is optimised for capturing and storing data that are transacted into the system on a regular basis (Choudhuri, 2014). A relational database supports these transactions; therefore, transactional databases usually have data organised in a relational format to optimise quick data entry and storage. On the other hand, data warehouses support historic and integrated data storage that is optimised for reporting and analysis purposes. In order to make the data retrieval easy and

Due to the advancement that has been realized, the amount of data being generated may be overwhelming. Many proposals on data warehouses can solve this problem and handle the complex data. However, one challenge often occurs as a result of the analysis. The terminology used by different companies in OLAP is

It is consider as a specialists that an OLAP system which contains an information on server that allows quick access to data (atomic data and derived data) and facilities calculations. Because OLAP tools work with multidimensional data models, perform complex analytical queries, ad hoc and has a high processing speed, some scholars have suggested that they rather be called FASMI (Fast Analysis of Shared Multidimensional Information) . Thus, Nigel and Richard Creeth Pendle, from the idea that "18 or even 12 rules are too much "wanted to define the characteristics of OLAP applications in a simple way and thus propose FASMI test, five key words: