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Guide to Building Data Warehouse from Scratch

A data warehouse is a system that recovers and combines data automatically from the primary source systems into a standardised data store for further analytical questioning and reporting. There are three common types of data warehouses (DWH). They include; the data mart, operating data store, and enterprise data warehouse.


A data warehouse ensures that data is of quality and consistency and unifies the data entry approach. It also automates management processes, saves time and creates a common platform for advanced analytics initiatives. If you have in place a DWH architecture, your managing efforts in data storing and analysis within the business will become a breeze, saving you a lot of time in the long run. 


Steps on how to build a data warehouse from scratch


For a business intelligence to be successful, there are factors considered in the data warehouse construction process. Those factors include; duration of the process, data analytics objectives, data quality, the complexity of the systems and many more. 


Set the data warehouse goals


Depending on your company, you can set your business objectives clearly to archive the data warehouse progress expectations. Consider a favourable time frame that can allow your company to complete this critical process successfully within a short period.


In this part, you can also identify and rank the company’s activities in detail, like users and department expectations and the company’s requirements. Carry out a fundamental data source analysis and a review of its current technological growth and resources to assist in the decision-making.


In this step, consider all the basic requirements to enable an efficient and effective data warehouse construction process. This part is crucial since it surveys all your business conditions for the construction process and puts all company members on board.


Select a favourable environment and set up concepts


In this part, it is essential to create a suitable physical environment to complete the steps. After that, select DWH technologies like data modelling and ETL/ELT tools to allow the possibility of; data security necessities, data flows applied and the number of data sources to be stored in the data warehouse. This process can take approximately fifteen days to complete.


Carry out a DWH work plan


Determine a DHW design and testing activities plans to ensure it works out as planned. Also, in the process, develop strategies for development and testing to facilitate a better work plan. It is advisable to create a risk management plan to act as a backup plan for the process. This process can consume a maximum of fifteen days.


System analysis and DWH architecture


Carry out detailed data enquiry from each data source to enable continuous data updates and promote data quality. The enquiry will also help you attain sufficient daily data creation and determine the type of data required. This section uses the ETL/ELT tools to enable proper data integration and data flow. You can design data models for the DWH and data marts to facilitate your warehouse’s data classification.


After that, you can create data security policies to monitor the data access and have a data backup plan. You can use twenty days to complete this part, depending on your company.


DWH formation


When forming DWH, the duration can be five months if you want a successful completion. Here, you create the data security software and implement data security rules. Have DWH architecture to facilitate ETL/ELT pipeline and ETL/ELT testing and complete the performance testing process.


Launching DWH


Here, you can introduce the DWH to your employees to give final opinions and tests. After that, you can have training sessions for specific people.


Parting words


Using the above steps, you can have a profitable data warehouse for your company. However, they can be options since there are other different data warehouse varieties. Always have your data stored in a secure platform to ensure successful business intelligence.


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