Google Campaign Manager to Databricks

This page provides you with instructions on how to extract data from Google Campaign Manager and load it into Delta Lake on Databricks. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Campaign Manager?

Campaign Manager (formerly DoubleClick Campaign Manager) is a web-based ad management system that advertisers and agencies use to manage creative assets and run ad campaigns.

What is Delta Lake?

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

Getting data out of Campaign Manager

Campaign Manager has an API that you can use to get information about advertisers, campaigns, creative assets, and more. For example, to get information about a campaign for a given profile, you would call GET /userprofiles/{profileId}/campaigns/{id}.

Sample Campaign Manager data

Here's an example of the kind of response you might see with a query like the one above.

{
  "kind": "dfareporting#campaign",
  "id": long,
  "idDimensionValue": dimensionValues Resource,
  "accountId": long,
  "subaccountId": long,
  "advertiserId": long,
  "advertiserIdDimensionValue": dimensionValues Resource,
  "advertiserGroupId": long,
  "name": string,
  "archived": boolean,
  "startDate": date,
  "endDate": date,
  "comment": string,
  "billingInvoiceCode": string,
  "audienceSegmentGroups": [
    {
      "id": long,
      "name": string,
      "audienceSegments": [
        {
          "id": long,
          "name": string,
          "allocation": integer
        }
      ]
    }
  ],
  "eventTagOverrides": [
    {
      "id": long,
      "enabled": boolean
    }
  ],
  "clickThroughUrlSuffixProperties": {
    "overrideInheritedSuffix": boolean,
    "clickThroughUrlSuffix": string
  },
  "defaultClickThroughEventTagProperties": {
    "overrideInheritedEventTag": boolean,
    "defaultClickThroughEventTagId": long
  },
  "creativeGroupIds": [
    long
  ],
  "creativeOptimizationConfiguration": {
    "optimizationModel": string,
    "optimizationActivitys": [
      {
        "floodlightActivityId": long,
        "floodlightActivityIdDimensionValue": dimensionValues Resource,
        "weight": integer
      }
    ],
    "id": long,
    "name": string
  },
  "additionalCreativeOptimizationConfigurations": [
    {
      "optimizationModel": string,
      "optimizationActivitys": [
        {
          "floodlightActivityId": long,
          "floodlightActivityIdDimensionValue": dimensionValues Resource,
          "weight": integer
        }
      ],
      "id": long,
      "name": string
    }
  ],
  "lookbackConfiguration": {
    "clickDuration": integer,
    "postImpressionActivitiesDuration": integer
  },
  "createInfo": {
    "time": long
  },
  "lastModifiedInfo": {
    "time": long
  },
  "traffickerEmails": [
    string
  ],
  "externalId": string,
  "nielsenOcrEnabled": boolean,
  "adBlockingConfiguration": {
    "enabled": boolean,
    "overrideClickThroughUrl": boolean,
    "clickThroughUrl": string,
    "creativeBundleId": long
  },
  "defaultLandingPageId": long
}

Loading data into Delta Lake on Databricks

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

Keeping Campaign Manager data up to date

Now what? You've built a script that pulls data from the Campaign Manager API and loads it into your data warehouse, but what happens tomorrow when you have new data?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, many of the API results include fields like createInfo that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Google Campaign Manager to Delta Lake on Databricks automatically. With just a few clicks, Stitch starts extracting your Google Campaign Manager data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake on Databricks data warehouse.