A database uses a relational model to store data, whereas a Data Warehouse uses various schemas such as star schema and others. In star schema, each dimension is represented by only the one-dimensional table. Data Warehouse supports dimensional modeling, which is a design technique to support end-user queries.
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 compares the cluster with the cluster that is already running. It performs the task of assigning some set of objects into groups, also known as clusters. It is used to perform the data mining job using a technique like statistical data analysis. It includes all the information and knowledge around many fields such as 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 to achieve the properties that are desired to be used.
Purpose of cluster analysis:
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Chameleon is a hierarchical clustering algorithm that overcomes the limitations of the existing models and methods present in Data Warehousing. This method operates on the sparse graph having nodes that represent data items and edges which represent the weights of the data items.
This representation allows large datasets to be created and operated successfully. The method finds the clusters that are used in the dataset using the two-phase algorithm.
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A fact table is usually designed at a low level of granularity. This means that we need to find the lowest level of information that can be stored in a fact table e.g., employee performance is a very high level of granularity. Employee_performance_daily and employee_perfomance_weekly can be considered as lower levels of granularity.
The granularity is the lowest level of information stored in the fact table. The depth of the data level is known as granularity. In date dimension, the level could be year, month, quarter, period, week, and day of granularity.
The process consists of the following two steps:
The above factors of determination will be re-sent as per the requirements.
SCDs (slowly changing dimensions) are the dimensions in which the data changes slowly, rather than changing regularly on a time basis.
Three types of SCDs are used in Data Warehousing:
Multidimensional OLAP (MOLAP) is faster than Relational OLAP (ROLAP).
Hybrid SCDs are a combination of both SCD1 and SCD2.
It may happen that in a table, some columns are important and we need to track changes for them, i.e., capture the historical data for them, whereas in some columns even if the data changes we do not have to bother. For such tables, we implement Hybrid SCDs, wherein some columns are Type 1 and some are Type 2.
As part of Struts Framework, we can develop the Action Servlets and the ActionForm Servlets and other servlet classes.
In case of ActionForm class, we can develop the validate() method. This method will return the ActionErrors object. In this method, we can write the validation code.
A very large database (VLDB) is a database that contains an extremely large number of tuples (database rows) or occupies an extremely large physical file system storage space. A one terabyte database would normally be considered to be a VLDB.
Time dimensions are usually loaded by a program that loops through all possible dates appearing in the data. It is not unusual for 100 years to be represented in a time dimension, with one row per day.
Both differ in the concept of building the Data Warehouse.
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A data warehouse is a set of data isolated from operational systems. This helps an organization deal with its decision-making process. A data mart is a subset of a data warehouse that is geared to a particular business line. Data marts provide the stock of condensed data collected in the organization for research on a particular field or entity.
A data warehouse typically has a size greater than 100 GB, while the size of a data mart is generally less than 100 GB. Due to the disparity in scope, the design and utility of data marts are comparatively simpler.
The staging layer, the data integration layer, and the access layer are the three layers that are involved in an ETL cycle.
Data purging is a process, involving methods that can erase data permanently from the storage. Several techniques and strategies are used for data purging.
The process of data purging often contrasts with data deletion. Deleting data is more of a temporary process, while data purging permanently removes data. This, in turn, frees up storage and/or memory space, which can be utilized for other purposes.
The purging process allows us to archive data even if it is permanently removed from the main source, giving us an option to retrieve the data from the archive if it is needed. The deleting process also permanently removes the data but does not necessarily involve keeping a backup, and it generally involves insignificant amounts of data.
The ETL test is performed in five stages as follows:
A slice operation is the filtration process in a data warehouse. It selects a specific dimension from a given cube and provides a new sub-cube. In the slice operation, only a single dimension is used.
good explanation of every concept.
Awesome collection…That what exactly I was waiting for..Good work..
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