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Automate Data Warehouse & Power BI with AnalyticsCreator
This video demonstrates how to build and deploy a data warehouse using AnalyticsCreator, from connecting a source system to generating and executing ETL pipelines. It shows how metadata-driven modeling produces architecture, transformations, and deployment artifacts automatically.
Duration: 38:09
Updated: Mar 2026
Level: beginner
Platform: AnalyticsCreator
For: Data Engineers, BI Developers
What this video answers
- How do you build a data warehouse using AnalyticsCreator?
- How do you import and model data from the Northwind dataset?
- How does AnalyticsCreator generate architecture and code automatically?
- How do you deploy a data warehouse using SSIS?
- How can you extend and debug the generated data model?
Platform shown
AnalyticsCreator
Related tooling
SQL Server 2022, SQL Server Management Studio, SSIS, Azure Data Factory, Power BI, Tableau
Key takeaways
- AnalyticsCreator generates data warehouse architecture automatically from metadata.
- Fact tables, dimensions, joins, and calculations are defined through the GUI and translated into SQL.
- Deployment artifacts such as SSIS packages are generated automatically.
- Errors and inconsistencies can be identified and debugged directly within the application.
- The entire workflow (from ingestion to deployment) can be executed from scratch within minutes.
Transcript
I'm Rosario D Lorenzo and I'm in charge of the global partner sales organization here at AnalyticsCreator. I'm very pleased that I have two guests with me today who are very happy to talk about AnalyticsCreator, and above all, technical experts which I will introduce in seconds. I would like to introduce Bagat, founder of OR Analytics and a BI enthusiast for more than 50 years. Welcome Bagat. Hi everyone, pleasure to meet you all and looking forward to give you a nice show here. Thank you. And of course we have Nazar with us, who is a BI consultant with extensive expertise in data management. Welcome Nazar and happy that you're with us. Thank you for inviting me. I'm happy to show you the demo today.
AnalyticsCreator is located in Munich since 2017 and we are in the data automation domain for over 16 years. What is really particular is that it is a collection of real projects, and our partners through feedback made sure that AnalyticsCreator is one of those tools based on experience. So far we have more than 820 users worldwide, and with the help of 50 value partners we were able to fulfill more than 8,000 projects worldwide. What AnalyticsCreator does is automate the design and development of your data platform. It starts with ingestion, transformation, code generation, documentation, and all tasks engineers usually do like pipeline cleansing and pipeline management. While you connect the data, you already get a running prototype, and as you make changes it generates artifacts much faster than manual methods. AnalyticsCreator is vendor lock-in free, meaning whatever it generates belongs to you and continues to run even without subscription.
I will share my screen. I want to start by outlining what we will cover in this demo. We will add data from a dataset called Northwind and use it in AnalyticsCreator to create an architecture for the data warehouse and finally deploy it. I'm using SQL Server 2022 as my management system and SQL Server Management Studio. I already have the Northwind data and nothing is prepared, I will do everything from scratch. AnalyticsCreator is on premise, so you download and install it. You can specify the database where you store the repository, which contains all metadata.
We log in and see the GUI. It is simple and functional. We create a new repository called webinar. In SQL Server we now see repo webinar created. Next we create a connector to the Northwind database by specifying server and database, testing the connection, and saving it. Then we use the data warehouse wizard to import tables. We select employees, employee territories, territories, orders, and order details. We define historization, fact table, and naming conventions. AnalyticsCreator then generates the architecture automatically.
We now see the visualization of the data warehouse architecture with layers such as source, staging, core, and data mart. We go into the fact table and add additional tables and references. We define joins using sequence numbers and relationships. The joins are automatically generated. We then add columns like quantity, unit price, and discount, and create a calculated column total sales using SQL expressions. We also add a calendar dimension using a macro. Errors can be detected and debugged directly in the interface.
We deploy the model by creating a deployment package. We choose SSIS as the output, which defines the ETL pipeline. The package is generated and saved locally. The database schema is created automatically, including tables, views, and historization structures. The SSIS workflow contains the full process to extract, transform, and load the data.
After execution, the data warehouse is populated. We see tables, stages, and views such as the fact order details. The calculated fields like total price and foreign keys are present. This matches what we defined in AnalyticsCreator. The data can now be used in dashboards to analyze sales by territory. This shows how quickly a data warehouse can be built and used.
What we saw was speed and automation. A real consultant built a data warehouse from scratch in minutes. This is important because analysts need accurate data from trusted sources. Traditional data warehouse projects used to take months or years, but with AnalyticsCreator it can be done in days. The tool provides a unified interface and automates many technical steps. It also supports deployment to SSIS or Azure Data Factory. The approach includes proof of concept, pilot, and scaling across domains such as finance, sales, and supply chain. Organizations can implement and scale data platforms within months.
