Creating and Managing Data Warehouses
A data warehouse allows us to build a single place where the data we want to work with resides and helps us to secure it.
Why create data warehouses
A data warehouse encourages us to:
- a unified approach to data security,
- readiness and optimization for advanced analytics,
- a data-driven culture across the company, and
- leveraging tools to visualise and present data to business users.
It also allows us to:
- Automated data management processes (such as collection, transformation, cleansing, structuring, etc.) to improve data quality and reliability,
- statistical analysis, reporting and data mining capabilities; and
- other, more sophisticated analytical applications that generate actionable information through the application of data science and artificial intelligence (AI) algorithms or graphs that enable multiple types of data analysis at scale.
Thus, a data warehouse can be defined as a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. The primary purpose of data warehouses is to perform queries, create analysis models, and store large amounts of historical data.
A data warehouse is most often thought of as a relational or other database, which usually sits within an ETL process.
You could say that the purpose of a data warehouse is to get to data as quickly as possible, or to save time. A data warehouse centralises, analyses and consolidates large amounts of data from multiple sources. Its analytical capabilities allow organisations to derive valuable business insights from their data to improve business managers’ decision making. Over time, a historical record is created that can be highly invaluable to data scientists and business analysts. With these characteristics, we can consider a data warehouse as a valuable source of true information or a reflection of reality.
The most commonly used tools in Archetix
Other tools used:
- Microsoft Azure
- Amazon Redshift
- Aure Synapse Analytics
- Oracle Autonomous Data Warehouse
- MySQL server
For companies that don’t have their own technology stack and that deal with web analytics, creating a data warehouse using BigQuery is a must. With the advent of GA4 comes direct data export to BigQuery. Due to the technical shortcomings of the GA4 UI, users are forced to solve more difficult analyses in another environment – ideally Google BigQuery.
For companies that already have a technology stack in place, bringing in Archetix as a data warehouse manager is an advantage to relieve their IT that does not have the resources for BI or data pipelines, due to the fact that its primary business is running e.g. web servers, accounting systems, etc.
So whether your company has a technology stack or not, we at Archetix are always able to bring a lot of quality ideas and solutions to your project. We are more than competent in creating data warehouses and managing them at the same time. So feel free to contact us and get a free initial consultation.