Yahoo Gemini is the advertising platform for Verizon Media's owned and operated web properties: Yahoo, AOL, Tumblr, TechCrunch, and the Huffington Post, along with their publishing partners.

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.

Gemini provides a RESTful API that lets developers retrieve data stored in the platform about advertisers, ads, campaigns, and more. For example, to retrieve information about an ad, you would call GET https://api.gemini.yahoo.com/v3/rest/ad/id/.

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

{
  "errors": null,
  "response": {
      "id": 320325,
      "description": "latest version",
      "adGroupId": 46758,
      "status": "ACTIVE",
      "campaignId": 31369,
      "advertiserId": 87292,
      "title": "batch call ad 2",
      "landingUrl": "http://www.yahoo.com",
      "imageUrl": "http://image.jpg",
      "sponsoredBy": "Sandboxes Inc",
      "displayUrl": "http://www.yahoo.com",
      "imageUrlHQ": null,
      "contentUrl": null,
      "callToActionText": null,
      "videoPrimaryUrl": null,
      "adName": null,
      "impressionTrackingUrls": {
                 "impression": null
              }
  }
}

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. The Yahoo Gemini 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. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

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.

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.

The key is to build your script in such a way that it can identify incremental updates to your data. 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.

Delta Lake on Databricks 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, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. 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 Panoply, and To S3.