Migrating data from RabbitMQ to Google BigQuery using CData SSIS Components.

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 RabbitMQ data to Google BigQuery using the CData SSIS Components for RabbitMQ and BigQuery.

Data Type Mapping

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

• 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.
• 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.
• INTERVAL data types:
  • The component represents INTERVAL types as strings. Whenever a query requires an INTERVAL type, it must specify the INTERVAL using the BigQuery SQL INTERVAL format:

    YEAR-MONTH DAY HOUR:MINUTE:SECOND.FRACTION

  • For example, the value "5 years and 11 months, minus 10 days and 3 hours and 2.5 seconds" in the correct format is:

    5-11 -10 -3:0:0.2.5

Prerequisites

• Visual Studio 2022
• SQL Server Integration Services Projects extension for Visual Studio 2022
• CData SSIS Components for Google BigQuery
• CData SSIS Components for RabbitMQ

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 RabbitMQ Source control and a CData GoogleBigQuery Destination control to the data flow task.

Configure the RabbitMQ source

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

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

   About RabbitMQ Management HTTP API

   RabbitMQ is an open-source message broker that supports multiple messaging protocols. The RabbitMQ Management HTTP API provides HTTP-based access to management and monitoring data for a RabbitMQ server. The API exposes information about virtual hosts, exchanges, queues, bindings, connections, channels, consumers, users, permissions, policies, and cluster-wide statistics.

   The Management plugin must be enabled on the RabbitMQ server for the HTTP API to be available. By default, the management interface listens on port 15672.

   Using Basic Authentication

   RabbitMQ Management HTTP API uses HTTP Basic authentication. You must supply the username and password of a RabbitMQ management user.

   To enable access to the management API:

   1. Ensure the RabbitMQ Management plugin is enabled on your server (rabbitmq-plugins enable rabbitmq_management).
   2. Use an existing management user or create one with the appropriate management tag (management, policymaker, monitoring, or administrator).
   3. Note the full base URL of your RabbitMQ Management HTTP API (e.g., http://localhost:15672).

   After configuring your RabbitMQ server, set the following connection properties to connect:

   • AuthScheme: Set this to Basic.
   • URL: Set this to the base URL of your RabbitMQ Management HTTP API (e.g., http://localhost:15672).
   • User: Set this to your RabbitMQ management username (e.g., guest).
   • Password: Set this to your RabbitMQ management password.

   Example connection string:

   Profile=C:\profiles\RabbitMQ.apip;AuthScheme=Basic;URL=http://localhost:15672;User=guest;Password=guest;
   

   Available Tables

   The RabbitMQ profile provides access to the following tables:

   • Overview - Cluster-wide statistics and information about the RabbitMQ node
   • Nodes - Information about individual nodes in the RabbitMQ cluster
   • NodeMemory - Detailed memory usage breakdown for a specific cluster node
   • Connections - List of all open AMQP connections to the broker
   • Channels - List of all open AMQP channels across all connections
   • Consumers - List of all consumers registered across all queues
   • Exchanges - List of exchanges declared across all virtual hosts
   • Queues - List of queues declared across all virtual hosts
   • Bindings - List of all bindings between exchanges and queues
   • VirtualHosts - List of virtual hosts configured on the broker
   • VhostPermissions - User permissions within a specific virtual host
   • Users - List of all RabbitMQ users
   • Permissions - Permission records for all users across all virtual hosts
   • TopicPermissions - Topic-level permission records for all users
   • Policies - List of policies applied to queues and exchanges in virtual hosts
   • OperatorPolicies - List of operator policies applied to queues in virtual hosts
   • Parameters - List of component parameters (e.g., federation, shovel) per virtual host
   • GlobalParameters - List of global parameters that apply across all virtual hosts
   • VhostLimits - Resource limits configured for specific virtual hosts
   • UserLimits - Resource limits configured for specific users
   • FeatureFlags - List of feature flags and their enabled/disabled state on the node
   • DeprecatedFeatures - List of deprecated features and their usage state
   • AuthAttempts - Authentication attempt statistics for the node
   • ClusterName - The name of the RabbitMQ cluster
   • WhoAmI - Information about the currently authenticated management user
   • ExchangeBindingsSource - Bindings for which a specific exchange is the source
   • ExchangeBindingsDestination - Bindings for which a specific exchange is the destination
   • QueueBindings - Bindings for a specific queue within a virtual host
   
3. After saving the connection, select "Table or view" and select the table or view to export into Google BigQuery, then close the CData RabbitMQ Source Editor.

Configure the Google BigQuery destination

With the RabbitMQ Source configured, we can configure the Google BigQuery connection and map the columns.

1. Double-click the CData Google BigQuery Destination to open the destination component editor and add a new connection.
2. In the CData GoogleBigQuery Connection Manager, configure the connection properties, then test and save the connection.
   • 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

   • QueryPassthrough: When this is set to True, queries are passed through directly to Google BigQuery.
   • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
   • 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.
   • 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.
   
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.