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25 sty 2017 · In this blog I will elaborate a detailed approach on how to implement CI for your Data Warehouse. I will explain the life cycle of a business user story starting from code branching, pull-request-triggered-build, Azure resources and environment provisioning, schema deployment, seed data generation, daily-integration releases with automated ...
The CI/CD pipeline is a continuous code, test, and deploy cycle. Every time code is tested, developers can react to feedback and improve the code. A CI/CD pipeline allows a more collaborative and integrated process with everyone across the organisation who needs to access the data warehouse.
13 gru 2019 · Starting from SAP S/4HANA 2020 and SAP S/4HANA Cloud Edition you can use the CDS view I_DataExtractionEnabledView to identify CDS views available for data extraction. Let's dive into the details now. A CDS view can be enabled for data extraction by just adding the following annotation
17 lis 2019 · Common Data Service (CDS) is a data storage system, like a database. CDS includes a set of base entities (tables), but you can add custom entities to it. You can access CDS through other Power Platform services (Power BI, Power Apps, Power Automate…) and some other Microsoft services.
13 mar 2023 · Data warehouses help you run logical queries, build accurate forecasting models, improve real-time data analysis, and identify trends impacting your organization. But what goes into designing a data warehouse? In short here are the 8 steps to data warehouse design:
1 wrz 2023 · Modern data warehouses integrate and consolidate data from various sources, like operational systems, databases, social media feeds, and IoT devices. The data can be structured, semi-structured, or unstructured. It is then cleaned and organized into a unified repository.
9 cze 2023 · Data warehousing (DWH) is the process of the consolidation of data from various sources into a centralized repository designed for efficient querying and analysis. Key DWH concepts include data integration, data modeling, data quality, metadata management, and ETL.
