Google BigQuery is a serverless, highly scalable, and cost-effective data warehouse designed to help organizations turn big data into actionable insights.
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 BigQuery and walk through how to migrate Odoo data to Google BigQuery using the CData SSIS Components for Odoo and BigQuery.
Data Type Mapping
| Google BigQuery Schema |
CData Schema |
|
STRING, GEOGRAPHY, JSON, INTERVAL
|
string
|
|
BYTES
|
binary
|
|
INTEGER
|
long
|
|
FLOAT
|
double
|
|
NUMERIC, BIGNUMERIC
|
decimal
|
|
BOOLEAN
|
bool
|
|
DATE
|
date
|
|
TIME
|
time
|
|
DATETIME, TIMESTAMP
|
datetime
|
|
STRUCT
|
See below
|
|
ARRAY
|
See below
|
STRUCT and ARRAY Types
Google BigQuery supports two kinds of types for storing compound values in a single row, STRUCT and ARRAY. In some places within Google BigQuery, these are also known as RECORD and REPEATED types.
A STRUCT is a fixed-size group of values that are accessed by name and can have different types. The component flattens structs so their fields can be accessed using dotted names. Note that these dotted names must be quoted.
An ARRAY is a group of values with the same type that can have any size. The component treats the array as a single compound value and reports it as a JSON aggregate. These types may be combined such that a STRUCT type contains an ARRAY field, or an ARRAY field is a list of STRUCT values.
Special Considerations
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Google BigQuery has both DATETIME (no timezone) and TIMESTAMP (with timezone) data types that the CData SSIS Components map to datetime based on the timezone of your local machine.
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In Google BigQuery, the NUMERIC type supports 38 digits of precision and up to 9 digits after the decimal point, while the BIGNUMERIC type supports 76 digits of precision and up to 38 digits after the decimal point. The CData SSIS Components for Google BigQuery automatically detects the precision/scale, but with the Destination Component users can manually map any high-precision columns.
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INTERVAL data types:
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.
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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
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Open Visual Studio and create a new Integration Services Project.
👁 Create the SSIS project
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Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
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Add a CData Odoo Source control and a CData GoogleBigQuery Destination control to the data flow task.
👁 Add the source and destination controls (Salesforce is shown)
Configure the Odoo source
Follow the steps below to specify properties required to connect to Odoo.
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Double-click the CData Odoo Source to open the source component editor and add a new connection.
👁 Open the source component editor (Salesforce is shown)
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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.
👁 Configure the source connection (Salesforce is shown)
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After saving the connection, select "Table or view" and select the table or view to export into Google BigQuery, then close the CData Odoo Source Editor.
👁 Select the table to export (Salesforce is shown)
Configure the Google BigQuery destination
With the Odoo Source configured, we can configure the Google BigQuery connection and map the columns.
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Double-click the CData Google BigQuery Destination to open the destination component editor and add a new connection.
👁 Open the destination component editor
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In the CData GoogleBigQuery Connection Manager, configure the connection properties, then test and save the connection.
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Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.
OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.
In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.
Helpful connection properties
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QueryPassthrough: When this is set to True, queries are passed through directly to Google BigQuery.
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ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
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FlattenObjects: By default the component reports each field in a STRUCT column as its own column while the STRUCT column itself is hidden. When this is set to False, the top-level STRUCT is not expanded and is left as its own column. The value of this column is reported as a JSON aggregate.
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SupportCaseSensitiveTables: When this property is set to true, tables with the same name but different casing will be renamed so they are all reported in the metadata. By default, the provider treats table names as case-insensitive, so if multiple tables have the same name but different casing, only one will be reported in the metadata.
👁 Configure the destination connection
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After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
👁 Choose the destination table
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On the Column Mappings tab, configure the mappings from the input columns to the destination columns.
👁 Map the columns (Salesforce is shown)
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.
