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

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

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.

Once you know all of the columns you want to insert, use the CREATE TABLE statement in the Redshift data warehouse to set up a table to receive all the data.

Next, migrate your data. It may seem like the easiest course would be to build INSERT statements to add data to your Redshift table row by row. That would be a mistake; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to copy the data into Amazon S3 and then use the COPY command to load it into Redshift.

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.

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, 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 BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.