Are you a beginner analysis and want to set up your first Data Warehouse? A Data Warehouse worthy of the name, well organized, scalable and reliable? We will explain how to do it.
If the company you work with wants to better leverage its data and make data-driven decisions, the question of implementing a Data Warehouse will necessarily arise at some point.
Let’s say it right off the bat: creating a Data Warehouse is not easy. A good part, if not the majority of Data Warehouse projects fail or result in a system being put in place that does not quite meet the initial expectations and needs.
How to design and build a Data Warehouse project? What are the pitfalls to avoid, the points of vigilance to have? And, this is the first key question to ask you, where to start?
To produce this comprehensive guide, we drew heavily on an article published in English on the Holistic blog.
What is a Data Warehouse? [Definition]
All modern businesses have data at their disposal. These data are stored in different places: these are the data sources, data sources. This data can come from:
Of database applications: for a start, it is generally the product developed. For a classic company, ERP.
Web applications, used to scale or simply manage an activity. We can think of email marketing software like SendinBlue , analytical applications like Google Analytics or Mix panel , accounting management software like Xero or Sage, etc.
Of spreadsheets: Excel, .csv, Google Spreadsheets, etc. This data can be updated manually or automatically through solutions like Zapier .
The role of a Data Warehouse is to synchronize all this data coming from all the sources of the company in a single place for reporting purposes.
How to design a Data Warehouse?
Here is, schematically, what the operation of a Data Warehouse architecture looks like:
What to take away from this diagram? That a Data Warehouse is used to carry out analyzes, to do BI, and reporting. The Data Warehouse is the technological device that allows a company to deploy a data-driven strategy (s).
You must therefore imagine, design and build the Data Warehouse system based on and constantly referring to the reporting needs of the company. This is the key. A Data Warehouse project must therefore begin with a work of formulating objectives, from which you can deduce the needs and decline the use cases to be implemented. Once you have identified the data you will need, you can build the data flows to set up.
Here are some key steps to follow to successfully build your Data Warehouse.
To go further if the subject of Data Warehouse interests you, I strongly invite you to browse these articles:
Data Warehouse Architecture: Traditional Approaches vs. Cloud
Is it time to set up a Data Warehouse in your business?
Comparison of possible technologies for your Data Warehouse Cloud
Create a schema for each data source
We recommend that you create a schema for all of the data sources that you want to synchronize with the Data Warehouse. This will allow you:
Quickly identify which data source each table comes from, which will be increasingly useful as the number of data sources increases. This will make it easier for future analysts to join the Folio3 App Development company in USA to identify the data coming from each data source.
To set up permissions (read / write) for each data source. For example, if you are a Data Engineer, you might want to grant write permissions to your student analyst, but not give them the ability to touch all data and data sources.
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