Cloud computing platform has various databases that are in support. The open source databases that are developed to support it is as follows:

- MongoDB: is an open source database system which is schema free and document oriented database. It is written in C++ and provides tables and high storage space. 

- CouchDB: is an open source database system based on Apache server and used to store the data efficiently

- LucidDB: is the database made in Java/C++ for data warehousing. It provides features and functionalities to maintain data warehouse.

Cloud computing is going all together for a different look as it now includes different data types like emails, contracts, images, blogs, etc. The amount of data increasing day by day and cloud computing is requiring new and efficient data types to store them. For example if you want to save video then you need a data type to save that. Latency requirements are increasing as the demand is increasing. Companies are going for lower latency for many applications.

Cloud computing is a metaphor used for internet. It provides on-demand access to virtualized IT resources that can be shared by others or subscribed by you. It provides an easy way to provide configurable resources by taking it from a shared pool. The pool consists of networks, servers, storage, applications and services.

Cloud computing consists of 3 layers in the hierarchy and these are as follows:

1. Infrastructure as a Service (IaaS) provides cloud infrastructure in terms of hardware like memory, processor speed etc. 

2. Platform as a Service (PaaS) provides cloud application platform for the developers. 

3. Software as a Service (SaaS) provides cloud applications which are used by the user directly without installing anything on the system. The application remains on the cloud and it can be saved and edited in there only.

- Agglomerative Hierarchical clustering method allows the clusters to be read from bottom to top and it follows this approach so that the program always reads from the sub-component first then moves to the parent. Whereas, divisive uses top-bottom approach in which the parent is visited first then the child. 

- Agglomerative hierarchical method consists of objects in which each object creates its own clusters and these clusters are grouped together to create a large cluster. It defines a process of merging that carries on till all the single clusters are merged together into a complete big cluster that will consists of all the objects of child clusters. Whereas, in divisive the parent cluster is divided into smaller cluster and it keeps on dividing till each cluster has a single object to represent.

Cluster analysis is used to define the object without giving the class label. It analyzes all the data that is present in the data warehouse and compare the cluster with the cluster that is already running. It performs the task of assigning some set of objects into the groups are also known as clusters. It is used to perform the data mining job using the technique like statistical data analysis. It includes all the information and knowledge around many fields like machine learning, pattern recognition, image analysis and bio-informatics. Cluster analysis performs the iterative process of knowledge discovery and includes trials and failures. It is used with the pre-processing and other parameters as a result to achieve the properties that are desired to be used.

Data cleaning is also known as data scrubbing. Data cleaning is a process which ensures the set of data is correct and accurate. Data accuracy and consistency, data integration is checked during data cleaning. Data cleaning can be applied for a set of records or multiple sets of data which need to be merged.

Data cleaning is performed by reading all records in a set and verifying their accuracy. Typos and spelling errors are rectified. Mislabeled data if available is labeled and filed. Incomplete or missing entries are completed. Unrecoverable records are purged, for not to take space and inefficient operations.

Dimensional modelling is very flexible for the user perspective. Dimensional data model is mapped for creating schemas. Where as ER Model is not mapped for creating shemas and does not use in conversion of normalization of data into denormalized form.

ER Model is utilized for OLTP databases that uses any of the 1st or 2nd or 3rd normal forms, where as dimensional data model is used for data warehousing and uses 3rd normal form.

ER model contains normalized data where as Dimensional model contains denormalized data.