Marketo's marketing automation platform helps organizations automate and measure marketing engagement, tasks, and workflows, including those for email, mobile, social, and digital ads.

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Marketo provides two categories of REST APIs for customers who want to export data programmatically. Lead Database APIs allow for retrieval of Marketo person records and associated object types such as Opportunities and Companies. Asset APIs expose marketing collateral and workflow-related records.

To use the APIs, visit the Admin panel and click on LaunchPoint. Create a new service and get an authorization token. Then choose the API appropriate to the data you want. For example, to get a list of opportunities, you could call GET /rest/v1/opportunities.json. You can add half a dozen optional parameters to tailor the data Marketo returns.

Marketo returns data in JSON format. Here's an example of data returned from an opportunities query:

{  
   "requestId":"e42b#14272d07d78",
   "success":true,
   "result":[  
      {  
         "seq":0,
         "marketoGUID":"dff23271-f996-47d7-984f-f2676861b5fa ",
         "externalOpportunityId":"19UYA31581L000000",
         "name":"Chairs",
         "description":"Chairs",
         "amount":"1604.47",
         "source":"Inbound Sales Call/Email"
      },
      {  
         "seq":1,
         "marketoGUID":"dff23271-f996-47d7-984f-f2676861b5fc ",
         "externalOpportunityId":"29UYA31581L000000",
         "name":"Big Dog Day Care-Phase12",
         "description":"Big Dog Day Care-Phase12",
         "amount":"1604.47",
         "source":"Email"
      }
   ]
}

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Marketo's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Google offers an overview document that covers loading data into BigQuery. Use the bq command-line tool, and in particular the bq load command, to upload data to your datasets and define schema and data type information. You can learn how to use bq from the Quickstart guide for bq. Iterate through the process as many times as it takes to load all of your tables into BigQuery.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Marketo.

And remember, as with any code, once you write it, you have to maintain it. If Marketo modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

BigQuery 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, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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 Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.