Business organizations are known to have a wide array of applications which stand out of the ordinary in storing as well as processing a bunch of transactions on a daily basis. Also known as OLTP or online transaction processing systems, the databases offer the capabilities for the retrieval, creation, deletion and updating the data.
Though the databases are found to be relational in a traditional manner, at present, the file systems are considered to be an alternative choice to store the raw data.
‘As mentioned above, the data warehouse has earned a high reputation as online analytical processing system which is recognized to be the repository of data which can be transformed, extracted as well as loaded from a variety of operational source systems after which they are modeled for offering the reporting and analysis of data. Though a wide assortment of data warehouses are present in the market, here are the most common ones:
Enterprise DW
This kind of DW offers a central repository which is created to bestow support to the process of decision making for the whole business organization.
Data Mart
It is recognized to be the subset of the specific DW which was used for bestowing support to a certain business unit, region or function area.
Operational Data Store
It has similarity to the enterprise warehouse with regards to the scope. However, it has refreshed data in real time and it is possible to use it for the purpose of operational reporting.
Difference between OLAP and OTLP systems
The data which is store in OLAP is known to be de-normalized which enables summarization, aggregation as well as drilling down of the data. In addition to this, the data warehouses allow the users of the business to have an in-depth knowledge of what are happening, the causes behind it, the timing and the processes to opt for.
However, OLTP are considered to be single applications which are created with an eye to recording certain processes of businesses such as transactions of credit card. Unlike the de-normalized type of the data warehouses, the database structure is known to be highly normalized which ensures consistency isolation, data atomicity and longevity. Owing to the complications that arise at the time of writing different queries for the analysis in similar applications, the SMEs or developers are needed for the specific support.
Places in which data warehouses are stored
As there is an exponential growth of data, the data warehouses are considered to be crucial. Different considerations are made on the hardware which are responsible for the processing, storage and offer a medium for the movement of data at the same time. It is possible to store the data warehouses in the cloud, on premise or a combination of both the environment.
You can take the decision on the basis of requirement for keeping the critical missions of organization mission on the premises. In case you are searching for the cloud solutions, it is a prerequisite to take accessibility, visibility, latency, security, industrial regulations, and the reliability of the providers of the cloud into account.
Typical data warehouse implementation
Here is a model of a traditional data warehouse implementation
Gather
This layer comprises of different silos of data which are inclusive of Excel Spreadsheets, CRM, ERP systems, access databases housing corporate. The storage subsystems which are used by such type of applications do not have the typical structure for easy navigation or querying.
Night reporting is possible in few of such applications. However, you need to keep in mind that the functionality is really limited whereas the reporting should be limited to the data, present in the single system.
Clean
This layer is applicable to the logic of the business or other data calculations which will be available in the next layer. The business logic is inclusive of the KPI's of the customer, calculations which are specific to business, data hierarchies as well as new derived columns which cannot be availed from the other source system. Data is known to exist in this layer temporarily. However, this layer is meant just for the creation of custom values as well as passing through to the data warehouse.
Store
This layer is considered to be the representation of the de-normalized data warehouse. Though there is a wide array of the design models, the Kimball approach is considered to be the latest design via which it is possible to organize the details into fact tables as well as dimensions which offers ease of use.
Share
It is not any kind of formal layer. Instead, it is the representation of the different ways in which the data present in the data warehouse is consumed. It is inclusive of direct SQL querying, dash boarding tool, BI reporting, and automated extracts for feeding others.
Reasons to choose a data warehouse
Data warehouses are considered to be the best options for making an informed purchase decisions owing to a wide array of reasons. It plays a vital role in bringing an improvement in the business intelligence. As a wide array of sources is integrated, it is possible to make different decisions, on the basis of the data.
It offers access to the crucial data in a single centralized location in no time. With data warehouse, the data across the entire business firm can be standardized as well as stored in the similar format and thus the departments can make numerous decisions, on the basis of uniform data. Thus, data warehouses play a vital role in increasing the consistency and quality of data.
As the data warehouse is capable of storing a huge volume of the historical data, it is possible to identify different trends throughout the year on a month to month analysis. Majority of the data warehouses are known to be built, modeled and optimized for the purpose of read access for the purpose of quicker report generation.
Data warehouse are useful in exploring the big data for predicting the future trends. In case you have a bunch of historical data which should be consolidated, the data warehouse lets them get access to the same in a common place with the capabilities of scaling in near future.
