English
Azure Data Warehouse Automation For Beginners
AnalyticsCreator automates data warehouse development by generating models, ETL pipelines, and Power BI-ready datasets from metadata. It reduces manual work by up to 60% and enables rapid deployment on the Microsoft stack. This allows teams to build scalable, governed analytics solutions with minimal coding effort.
Questions
- How does AnalyticsCreator automate data warehouse development?
- What is a connector in AnalyticsCreator?
- How does the Data Warehouse Wizard work?
- How are flat files and databases combined?
- How is historization handled?
- How are fact and dimension tables created?
Key Takeaways
- Up to 60% reduction in development time
- Automated modeling via Data Warehouse Wizard
- Seamless integration with Microsoft stack
- Supports 250+ connectors (DB + flat files)
- Built-in historization and data lineage
- Standardized, repeatable development
- Rapid deployment to SQL Server + Power BI
- Strong support for self-service analytics
Transcript
Good morning, good evening, and welcome, everyone, to this webinar.
My name is Rosario Di Lorenzo, Vice President at AnalyticsCreator. We help data engineers automate data warehouses and reduce repetitive work.
AnalyticsCreator was founded in 2017, but the technology behind it has been developed and refined for more than 15 years.
We believe automation is the real solution for building data warehouses faster, more reliably, and with less manual effort.
AnalyticsCreator is based in Munich and was founded in 2017.
The technology has been a real authority in data automation for more than 15 years.
AnalyticsCreator simplifies and automates data warehouse development and can save up to 60% of development time.
We currently serve more than 120 customers.
Data engineers are facing an increasing workload.
More tools do not necessarily reduce complexity.
Automation is the real solution.
Companies need more control over their data and costs.
AnalyticsCreator integrates with the Microsoft stack.
Connectors extract metadata, and the repository stores all modeling information.
The wizard automatically builds the warehouse.
Deployment creates the SQL database, SSIS packages, and the tabular model.
BI Automation is located in Vienna and has been an AnalyticsCreator partner since 2020.
Their customers range from companies with 200 employees to organizations with 30,000 employees.
Their focus is on BI environments and dashboards.
Data is imported through connectors, and metadata is stored in the repository.
Modeling and transformations are then applied.
Synchronization updates the repository.
Deployment creates the SSIS jobs.
Power BI connects to the tabular model.
The goal is to visualize profit versus sales.
Users should be able to filter by category and drill into customer behavior.
We create the connectors: one for the database and one for the CSV file.
Then we test the connection and load the structure automatically.
We select the tables and assign them as facts or dimensions.
We configure the calendar, disable default transformations, and run the wizard.
The warehouse is then created automatically.
The returns file contains duplicates.
We create a transformation, apply DISTINCT, and clean the dataset.
We replace the original table and add historization.
Then we select the package and configure the SCD type.
We convert the return flag to a Boolean value.
We use SQL expressions and handle null values.
We convert the order date and the ship date.
Then we apply the calendar macro.
The calendar only maps once by default.
We create a second dimension for the ship date and synchronize the model.
Visual connections are visible in the model.
We can trace transformations and filter lineage by table.
We create the deployment package.
Then we enable the DACPAC, SSIS deployment, and configure the tabular model.
We run the deployment and monitor the deployment log.
The SQL tables are created, and the SSIS packages are generated.
We execute the workflow package.
SSIS loads the data, and the tables are populated.
Power BI connects to the tabular model.
The dataset is available, and the facts and dimensions are loaded.
The data warehouse was built in 30 minutes.
Historization is included.
Data lineage is visible.
Power BI is connected.
The process is fully automated.
