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How to Build a Data Warehouse from Scratch?

Editor’s note: ScienceSoft’s data warehouse consultants share their 14 years of experience and guide you through the thorny path of building a data warehouse (DWH). If you’d like to hand over building your DWH to the team straight away, get a personalized offer.

When a company is implementing a data warehouse solution, the first thing it needs to decide is whether to opt for on-premises or cloud deployment. Although most of the statements in this overview are also true for cloud DWHs, we will mainly focus on the on-premises option, including points to consider to avoid possible risks during DWH development.

If you are more interested in building your data warehouse in the cloud, you are welcome to check ScienceSoft’s guide to cloud DWH implementation for more specific details.

The overall process of building a data warehouse from scratch can be divided into two steps – building the staging area and the storage area.

process of building a data warehouse

Building the staging area

Before data is ready for analysis, it undergoes the process of extraction (retrieval of the source data from original data sources), transformation (conversion of the original data structures into the target one) and loading (deposition of the information into a data storage system). This preparatory process can be of two types – traditional ETL and modern ELT, which differ according to whether the transformation step happens inside or outside the DWH.

With the traditional ETL process type, only transformed data is loaded into the data warehouse, so you need to decide in advance what data is necessary for your DWH and further analysis to derive actionable insights. With the modern ELT process type, the transformation is performed during query time and users can decide later what data to transform for analysis. This way, you grant the freedom to experiment with the pool of unfiltered data to certain teams in your company (for example, finance, production, engineering, sales, etc.).

Building the storage (repository) area

In order to store data, you need to choose a data model, which predetermines how data is structured in relation to other data, and a system architecture.

In ScienceSoft’s projects, we use two approaches to data modeling: normalized and dimensional. According to the normalized approach, data is stored in relational tables grouped together by subject areas. And in the dimensional approach, data is partitioned into facts (generally, numeric transaction data) and dimensions (information that gives context to the facts).

DWH System architecture

The top-down approach is more suitable for those who want to get an overall picture of the entire business process in the first place. The bottom-up approach is more suitable for those companies who need to quickly retrieve specific data for local optimization.

Want to skip the complicated process of building a DWH?

ScienceSoft will design a data warehouse solution to meet your particular business needs.

Lack of data integrity

Having multiple poorly integrated data sources, companies may have problems defining trustworthy data, tracking incompatible and duplicate data, registering omissions and semantic conflicts in original data sources. The challenge can be overcome by defining mandatory processes to decrease the data quality issues and setting up data integrity testing processes at the DWH design stage.

Technological complexity

Ill-defined or changing business requirements complicate the process of choosing appropriate DWH technologies. Therefore, whatever hardware and software you choose, it should be scalable and flexible. ScienceSoft’s team may help with choosing the right technology stack for building a DWH.

Delivering instant data availability

Business users expect steady and quick access to information, as even brief downtime can result in profit losses due to delayed decisions. To get quick insights, it is essential your DWH architecture, as well as hardware and software used to build it, provide for uninterrupted DWH functioning and quick data retrieval.

Get your data warehouse right

Once implemented, a data warehouse requires constant after-deployment support activities, for example, data administration, performance monitoring, cybersecurity activities, staff training, etc.

To make your data warehouse project result in long-term success and increase your business intelligence maturity, turn to ScienceSoft’s data warehouse services for consulting and support.

Get a consultation on building a DWH

A DWH vendor with 14 years of experience, we can develop, migrate, and support your data warehouse or consult on any issue concerning your DWH.

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