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.

Panoply is a fully managed data warehouse service that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

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}.

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
}

Once you have identified all of the columns you want to insert, you can use the CREATE TABLE statement in Panoply's Redshift data warehouse to create a table to receive all of the data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

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.

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.