Get trial
English
New connector for exporting data from data warehouse to many target systems
This session introduces the AnalyticsCreator export connector and OData connector. The demo shows how data can be exported from an AnalyticsCreator-generated data warehouse to CSV files, external databases, Azure Blob Storage, or Azure Data Factory pipelines, and how OData sources can be imported.
Duration: 44:16
Updated: Jul 2022
Level: intermediate
Platform: AnalyticsCreator, SQL Server, SSIS, Azure Data Factory, Azure Blob Storage, OData
For: Data Engineers, BI Developers, AnalyticsCreator Users, Data Warehouse Teams
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?
- What is the OData connector used for?
- Can export processes be generated as Azure Data Factory pipelines?
Platform shown
AnalyticsCreator
Related tooling
SQL Server, SSIS, Azure Data Factory, Azure Blob Storage
Key Takeaways
- AnalyticsCreator can export data from generated warehouse objects to external targets.
- Export targets include CSV files, external databases, Azure Blob Storage, and Azure Data Factory pipelines.
- CSV target files can be created automatically during export.
- External database tables must already exist before export.
- Export definitions include source-to-target column mappings and optional filters.
- A new export layer appears in the model.
- The generated workflow package can run import, historization, persisting, and export steps.
- Data can be exported from the data mart layer or other warehouse objects.
- The OData connector can import data from OData services.
- OData can be useful for SharePoint lists and some SAP OData interfaces.
Transcript
Peter introduces AnalyticsCreator as a metadata-driven design application for data warehouse automation. The session focuses on a new destination export connector that can export data from AnalyticsCreator-generated data warehouses to external target systems.
Dimitri creates a new repository and connects to the AdventureWorks 2019 SQL Server demo database. Using the Data Warehouse Wizard, he selects Human Resources tables, imports them, historizes them, creates dimensions, and generates fact transformatio
The generated model contains source, staging, persistent staging, core, and data mart layers. Dimitri explains import mappings, field-level historization, generated stored procedures, snapshot historization, and persisting for improving access to complex transformations.
Dimitri creates a CSV connector and adds export definitions for objects such as Department, Employee, and Shift. The export layer is created in the model, mappings are defined, output paths are configured, and the generated package exports the data to CSV files.
AnalyticsCreator generates a Visual Studio solution with a DACPAC file and SSIS packages. The workflow package runs import, historization, persisting, and export steps in the correct order. After execution, the CSV files are available in the export directory.
Dimitri introduces the OData connector using a Northwind OData test service. OData can be used to import data from services such as SharePoint lists or SAP OData interfaces. The session closes with an invitation to test the functionality through an AnalyticsCreator trial version.
