CDM and synapse datawarehouse

Anshal 2,206 Reputation points
2024-07-02T18:13:42.5333333+00:00

Hi friends, I have some questions on CDM common data model and what use cases it best suited for ? Since CDM has attributes , entities and relationships does it fair to assume that we don't need to do data model such as star schema and snowflake schema? And if there are mulitple reporting systems in place does it make sense to go for CDM approach or do data modeling in Synapse data warehouse? What are the data limitions of CDM and complexities in implementation?

Azure Data Lake Storage
Azure Data Lake Storage
An Azure service that provides an enterprise-wide hyper-scale repository for big data analytic workloads and is integrated with Azure Blob Storage.
1,425 questions
Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
4,670 questions
Azure Data Factory
Azure Data Factory
An Azure service for ingesting, preparing, and transforming data at scale.
10,131 questions
0 comments No comments
{count} votes

Accepted answer
  1. Amira Bedhiafi 19,626 Reputation points
    2024-07-02T18:17:28.4666667+00:00

    I am breaking your post into 4 parts :

    CDM Common Data Model and Its Use Cases

    The Common Data Model (CDM) is designed to provide a standardized and unified data schema that helps organizations manage and analyze data from multiple sources consistently. It is particularly useful in scenarios where there is a need to integrate disparate data sets, enable interoperability between systems, and facilitate data governance. CDM is well-suited for applications in customer relationship management (CRM), enterprise resource planning (ERP), healthcare analytics, and other domains where standardized data definitions can improve data quality and usability.

    CDM vs Traditional Data Modeling (Star and Snowflake Schemas)

    While CDM does encompass attributes, entities, and relationships, it does not entirely replace the need for traditional data modeling approaches such as star and snowflake schemas. Traditional data modeling techniques are optimized for performance in specific analytical scenarios, particularly in data warehousing contexts where dimensional modeling can simplify complex queries and improve performance. CDM, on the other hand, provides a high-level abstraction that is beneficial for data integration and interoperability but may not always offer the same performance benefits for analytical queries. Therefore, for complex analytical workloads, especially in a data warehouse like Synapse, traditional data models may still be necessary alongside CDM.

    CDM for Multiple Reporting Systems vs. Synapse Data Warehouse Modeling

    When dealing with multiple reporting systems, using CDM can simplify data integration by providing a common schema and metadata definitions, making it easier to standardize reports and analyses across different systems. However, if performance and complex query optimization are critical, traditional data modeling in Synapse Data Warehouse might be more suitable. Synapse provides advanced features for data warehousing, such as optimized storage, indexing, and distributed query processing, which can significantly enhance the performance of reporting systems. The decision should be based on the specific requirements of your reporting systems, considering factors like data volume, query complexity, and integration needs.

    Data Limitations and Complexities of Implementing CDM

    Implementing CDM comes with its own set of limitations and complexities. One limitation is that CDM might not fully capture the unique data requirements and nuances of specific applications, leading to potential gaps in data representation. Additionally, aligning existing data to the CDM schema can be a complex process, requiring significant data transformation and validation efforts. The implementation also involves integrating with existing data infrastructure, which can be challenging in terms of compatibility and performance. Moreover, maintaining the CDM schema and keeping it synchronized with evolving business requirements can add to the complexity. Organizations need to carefully plan and manage the implementation to mitigate these challenges effectively.

    1 person found this answer helpful.

0 additional answers

Sort by: Most helpful