Migrating data from Odoo to Databricks using CData SSIS Components.



Easily push Odoo data to Databricks using the CData SSIS Tasks for Odoo and Databricks.

Databricks is a unified data analytics platform that allows organizations to easily process, analyze, and visualize large amounts of data. It combines data engineering, data science, and machine learning capabilities in a single platform, making it easier for teams to collaborate and derive insights from their data.

The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.

In this article, we explore the data type mapping considerations when exporting to Databricks and walk through how to migrate Odoo data to Databricks using the CData SSIS Components for Odoo and Databricks.

Data Type Mapping

Databricks Schema CData Schema

int, integer, int32

int

smallint, short, int16

smallint

double, float, real

float

date

date

datetime, timestamp

datetime

time, timespan

time

string, varchar

If length > 4000: nvarchar(max), Otherwise: nvarchar(length)

long, int64, bigint

bigint

boolean, bool

tinyint

decimal, numeric

decimal

uuid

nvarchar(length)

binary, varbinary, longvarbinary

binary(1000) or varbinary(max) after SQL Server 2000


Special Considerations

  • String/VARCHAR: String columns from Databricks can map to different data types depending on the length of the column. If the column length exceeds 4000, then the column is mapped to nvarchar (max). Otherwise, the column is mapped to nvarchar (length).
  • DECIMAL Databricks supports DECIMAL types up to 38 digits of precision, but any source column beyond that can cause load errors.

About Odoo Data Integration

Accessing and integrating live data from Odoo has never been easier with CData. Customers rely on CData connectivity to:

  • Access live data from both Odoo API 8.0+ and Odoo.sh Cloud ERP.
  • Extend the native Odoo features with intelligent handling of many-to-one, one-to-many, and many-to-many data properties. CData's connectivity solutions also intelligently handle complex data properties within Odoo. In addition to columns with simple values like text and dates, there are also columns that contain multiple values on each row. The driver decodes these kinds of values differently, depending upon the type of column the value comes from:
    • Many-to-one columns are references to a single row within another model. Within CData solutions, many-to-one columns are represented as integers, whose value is the ID to which they refer in the other model.
    • Many-to-many columns are references to many rows within another model. Within CData solutions, many-to-many columns are represented as text containing a comma-separated list of integers. Each value in that list is the ID of a row that is being referenced.
    • One-to-many columns are references to many rows within another model - they are similar to many-to-many columns (comma-separated lists of integers), except that each row in the referenced model must belong to only one in the main model.
  • Use SQL stored procedures to call server-side RFCs within Odoo.

Users frequently integrate Odoo with analytics tools such as Power BI and Qlik Sense, and leverage our tools to replicate Odoo data to databases or data warehouses.


Getting Started


Prerequisites

Create the project and add components

  1. Open Visual Studio and create a new Integration Services Project.
  2. Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
  3. Add a CData Odoo Source control and a CData Databricks Destination control to the data flow task.

Configure the Odoo source

Follow the steps below to specify properties required to connect to Odoo.

  1. Double-click the CData Odoo Source to open the source component editor and add a new connection.
  2. In the CData Odoo Connection Manager, configure the connection properties, then test and save the connection.

    To connect, set the Url to a valid Odoo site, User and Password to the connection details of the user you are connecting with, and Database to the Odoo database.

  3. After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Odoo Source Editor.

Configure the Databricks destination

With the Odoo Source configured, we can configure the Databricks connection and map the columns.

  1. Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
  2. In the CData Databricks Connection Manager, configure the connection properties, then test and save the connection. To connect to a Databricks cluster, set the properties as described below.

    Note: The needed values can be found in your Databricks instance by navigating to Clusters, selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

    • Server: Set to the Server Hostname of your Databricks cluster.
    • HTTPPath: Set to the HTTP Path of your Databricks cluster.
    • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

    Other helpful connection properties

    • QueryPassthrough: When this is set to True, queries are passed through directly to Databricks.
    • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
    • UseUploadApi: Setting this property to true will improve performance if there is a large amount of data in a Bulk INSERT operation.
    • UseCloudFetch: This option specifies whether to use CloudFetch to improve query efficiency when the table contains over one million entries.
  3. After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
  4. On the Column Mappings tab, configure the mappings from the input columns to the destination columns.

Run the project

You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.

Ready to get started?

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