English
New connector for exporting data from data warehouse to many target systems
Questions
- What is the AnalyticsCreator export connector?
- How can AnalyticsCreator export data from a data warehouse?
- Can AnalyticsCreator export data to CSV files?
- Can AnalyticsCreator export data to external databases?
- Does AnalyticsCreator support Azure Blob Storage export?
- What is the OData connector used for?
Key Takeaways
- AnalyticsCreator can now export data from a generated data warehouse to external targets.
- Export targets include CSV files, external databases, Azure Blob Storage, and Azure Data Factory pipelines.
- Export definitions can be added to objects in the data warehouse model.
- CSV files can be created automatically during export.
- External database targets must already contain the target tables.
- Export packages are generated as part of the deployment package.
- The workflow package can run import, historization, persisting, and export steps in the correct order.
- AnalyticsCreator can export from the data mart layer or from other warehouse objects.
- SAP export is limited because the Theobald connector is read-focused.
- The new OData connector can import data from OData services.
- OData can be useful for SharePoint lists and some SAP OData interfaces.
- OData sources can be processed through SSIS packages or Azure Data Factory pipelines.
Transcript
My name is Peter Smoly, and I am the CEO of AnalyticsCreator. Today, Dimitri Sorokin will present the new destination export connector, which allows data to be exported from an AnalyticsCreator-generated data warehouse into different target systems.
AnalyticsCreator is a data warehouse automation application for experts and non-experts. It generates source code instead of requiring manual programming and supports the lifecycle of data warehouses, data marts, and data lakes, including design, development, maintenance, change management, and deployment.
Typical use cases include greenfield projects, modernisation, SAP analytics, cloud migration, near-real-time data platforms, and managed service scenarios for partners.
A central part of the AnalyticsCreator vision is independence. AnalyticsCreator is a pure design-time application, so no runtime is required. The generated data warehouse can continue to run even if the AnalyticsCreator subscription ends.
AnalyticsCreator also uses a holistic metadata model that combines the business view and technical data model. The repository is open, so customers and partners can extend it or build additional generators and add-ons.
Dimitri creates a new project called Test Export using the AdventureWorks 2019 demo database. He adds a SQL Server connector, starts the Data Warehouse Wizard, and selects Human Resources tables.
AnalyticsCreator then creates the source, staging, persisted staging, core, and data mart layers. The model includes imports, historization, dimensions, and fact transformations. Import definitions contain mappings, filters, variables, and optional scripts. Historization is generated through stored procedures and can be configured at field level.
Dimitri exports data from the Dim Department data mart object into a CSV file. He creates a CSV connector, adds an export, maps source columns to target columns, and defines the output path. A new export layer appears in the model.
He then adds exports for Employee and Shift, generates a deployment package, and opens the generated Visual Studio solution. The workflow package runs import, historization, export, and persisting in the correct order. After execution, the CSV files for Department, Employee, and Shift are created successfully.
AnalyticsCreator can export data to SQL Server, Oracle, Azure Blob Storage, text files, or other databases reachable through ODBC or OLE DB. For text files, the files do not need to exist in advance. For external databases, the target tables must already exist.
Dimitri also introduces the new OData connector. OData can be used to import data from sources such as SharePoint lists or SAP systems with OData interfaces. OData imports can be processed through generated SSIS packages or Azure Data Factory pipelines.
The session closes with an invitation to test the functionality through a trial version and join future AnalyticsCreator sessions.
