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Rocky1001's avatar
Rocky1001
Frequent Visitor
10 months ago
Solved

Netsuite connector for Fabric

Hi all, just wanted to pick your brains please.


I'm a newbie to Netsuite but have lots of PBI and some good Fabric experience but I was wondering if you suggest some options for extracting data from Netsuite into bronze layer within Fabric, probably parquet files I think will be the plan.

 

I want incremental loading so some CDC feature would be helpful, I know we can create a custom ODBC connection but that a bit too much maintenance for the company so depending on costs am looking at a third party option.

CData or Fivetran seem as if they would do the job, CData sync being the cheaper option as I understand.

 

There won't be huge amount of data volume but just wondered how others have approached this.

 

Many thanks in advance!

  • Hi Rocky1001,

     

    Currently, Microsoft Fabric does not offer a native connector for NetSuite. Commonly used approaches include:

     

    CData Sync -- OneLake (Parquet/Delta):
    This is a cost-effective solution compared to fully managed ELT, supporting incremental replication and flexible scheduling. However, it requires some operational management as you will be running the service.

     

    Fivetran -- OneLake (Delta):
    This fully managed option delivers data directly into OneLake with minimal setup. Most objects support incremental loads, though some tables may need full refreshes periodically. It is the lowest-maintenance approach but comes at a higher cost.

     

    DIY solutions (SuiteAnalytics ODBC/JDBC or REST/SuiteQL APIs):
    These can be integrated through Fabric pipelines or notebooks, but you will need to develop and maintain your own incremental logic, as well as manage authentication, pagination, and throttling.

     

    For smaller data volumes, CData Sync into OneLake offers a good balance between cost and effort. If budget allows and a fully managed solution is preferred, Fivetran → OneLake is the most straightforward option.

     

    Thank you.

6 Replies

  • Rocky1001 

    MS Fabric does not have Netsuite native connector for extracting data into Fabric.  As pointed out by you, CData is lower cost when comparte to Fivetran. 

     

    Use Cdata Sync -> Netsuite -> Onelake (parquet file) in Fabric lakehouse

    or 

    use Rest API to connect Netsuite and load into Fabric lakehouse using spark notebook.

     

    • Rocky1001's avatar
      Rocky1001
      Frequent Visitor

      Hi, thanks for the message, yes the the 2 options I've narrowed down is CData if using a mamanged option or using the customer ODBC option, I was just wondering if there are any other better options I'm not aware of that people are using 🙂

      • v-sgandrathi's avatar
        v-sgandrathi
        Community Support

        Hi Rocky1001,

         

        Currently, Microsoft Fabric does not offer a native connector for NetSuite. Commonly used approaches include:

         

        CData Sync -- OneLake (Parquet/Delta):
        This is a cost-effective solution compared to fully managed ELT, supporting incremental replication and flexible scheduling. However, it requires some operational management as you will be running the service.

         

        Fivetran -- OneLake (Delta):
        This fully managed option delivers data directly into OneLake with minimal setup. Most objects support incremental loads, though some tables may need full refreshes periodically. It is the lowest-maintenance approach but comes at a higher cost.

         

        DIY solutions (SuiteAnalytics ODBC/JDBC or REST/SuiteQL APIs):
        These can be integrated through Fabric pipelines or notebooks, but you will need to develop and maintain your own incremental logic, as well as manage authentication, pagination, and throttling.

         

        For smaller data volumes, CData Sync into OneLake offers a good balance between cost and effort. If budget allows and a fully managed solution is preferred, Fivetran → OneLake is the most straightforward option.

         

        Thank you.