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AnalyticsCreator Congress 2021 - Intro & Live Demo
Questions
- What was covered in the AnalyticsCreator Congress 2021 introduction?
- How does AnalyticsCreator create a data warehouse from source metadata?
- How does AnalyticsCreator automate historization and transformations?
- Can AnalyticsCreator deploy the same model on-premise and to Azure?
- How are SSIS packages and Azure Data Factory pipelines generated?
- How does AnalyticsCreator generate tabular and Power BI models?
Key Takeaways
- AnalyticsCreator Congress 2021 included sessions for users, partners, and interested companies.
- The first session introduced AnalyticsCreator and showed how to build a data warehouse from scratch.
- AnalyticsCreator manages the lifecycle of data warehouse, data mart, and analytical model development.
- The application supports metadata collection, preparation, historization, modelling, optimisation, and deployment.
- AnalyticsCreator is positioned as a pure design-time application with no runtime dependency.
- Generated code can run independently in the Microsoft SQL and Azure stack.
- New features discussed include SAP ODP, SAP metadata connectors, collaboration features, repository object locking, Azure Data Factory ARM template generation, Azure Blob Storage support, Power BI cloud model generation, and Data Vault enhancements.
- The demo uses Microsoft Northwind as the source database.
- The Data Warehouse Wizard generates source, staging, persisted staging, core, and data mart layers.
- Historization is configurable using SCD Type 1, SCD Type 2, and field-level settings.
- Macros are reusable SQL transformation blocks used for logic such as calendar key generation.
- Persisting materializes complex views into physical tables.
- The same model is deployed on-premise using SQL Server, SSIS, and tabular models.
- The same model is then deployed to Azure SQL Database, Azure Data Factory, and Power BI.
- The Q&A clarifies that AnalyticsCreator assists model generation, but users still need to understand the data and decide which tables should become facts and dimensions.
Transcript
Hello everyone, and welcome to AnalyticsCreator Congress 2021. My name is Peter Smoly, and I am the CEO of AnalyticsCreator. Thank you for joining us.
This congress is for AnalyticsCreator users, partners, and companies interested in data warehouse automation. Since many attendees are new to AnalyticsCreator, we will begin with a short introduction before moving into the live demos and partner sessions.
During the congress, we will cover AnalyticsCreator basics, a live product demo, Azure deployment, partner presentations, Data Vault topics, Power BI model automation, the AnalyticsCreator roadmap, and real customer project examples.
AnalyticsCreator manages the lifecycle of a data warehouse, data platform, or data mart in the Microsoft data stack. It supports source connection, metadata collection, data preparation, historization, business modelling, optimisation, and deployment.
AnalyticsCreator reduces complexity and risk in BI and analytics projects by generating source code from a metadata model. This helps teams deliver results faster and with fewer manual development steps.
A key part of the vision is independence. AnalyticsCreator is a pure design-time application. Once the generated code is deployed into the Microsoft environment, it runs without the AnalyticsCreator engine. Customers and partners also receive the rights to use the generated source code under the contract terms.
Typical use cases include greenfield data warehouse projects, modernisation of existing data warehouses, SAP-based analytics, Azure migration, near-real-time data platforms, and managed service scenarios for partners.
Recent features include SAP ODP support, SAP metadata connectors in the AnalyticsCreator cloud, collaboration features for distributed development, updated interface elements, Power BI and tabular OLAP security rules, configurable persistence procedures, Azure Data Factory pipeline generation, Azure Blob Storage support, Power BI cloud model generation, and Data Vault automation enhancements.
Dimitri starts the live demo by creating a new AnalyticsCreator repository based on the Microsoft Northwind demo database.
He adds a SQL Server connector, runs the Data Warehouse Wizard, imports all Northwind tables, enables historization, creates dimensions, and uses Order Details as the main fact transformation. AnalyticsCreator then generates the source, staging, persisted staging, core, and data mart layers.
The staging layer imports source data, while the persisted staging layer stores historized data. AnalyticsCreator compares imported data with historized records and creates new versions when changes are detected.
The core layer transforms historized data into facts and dimensions using generated SQL views. Snapshot historization allows fact transformations to access current or historical versions of data. Macros are used to convert date fields into calendar dimension IDs, and measures are added in the data mart layer.
Dimitri first creates an on-premise deployment package containing a DACPAC file, SSIS packages, configuration files, and XMLA scripts for a tabular OLAP model. The generated workflow package runs import, historization, and persisting in the correct order, and the resulting model can be used in Power BI Desktop.
He then deploys the same model to Azure. AnalyticsCreator generates and deploys an Azure SQL Database structure, Azure Data Factory pipelines through an ARM template, and a Power BI dataset through the XMLA endpoint. The same metadata model supports both on-premise and Azure deployment.
During the Q&A, one attendee asks whether AnalyticsCreator automatically identifies common dimensions and unifies them into a dedicated dimension table. Dimitri explains that AnalyticsCreator can help, but users still need to understand the data and decide which tables should become facts and dimensions.
The demo project will be available through the AnalyticsCreator cloud for subscribers to inspect. After this session, we take a short break before the BI Automation partner presentation.
