Outbrain specializes in presenting sponsored website links, typically in the form of lists of links to recommended stories on news and publishing sites presented in a box at the footer of the page.

Snowflake is a cloud-native data warehouse that runs on an Amazon Web Services platform. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet. Users pay for only the storage and compute resources they use, and can scale storage and compute resources separately.

Outbrain's RESTful Amplify API lets you extract information about marketers, campaigns, performance, and more. You can put together an API call that specifies performance items such as impressions, clicks, clickthrough rate, and spend with a call like GET /reports/marketers/[id]/content. You can specify any of a dozen optional parameters to limit, filter, and sort the output.

The JSON response from an an API call for performance data might look like this:

{
    "results": [
        {
            "metadata": 
            {
                "id": "00f4b02153ee75f3c9dc4fc128ab041962",
                "text": "Yet another promoted link",
                "creationTime": "2017-11-26",
                "lastModified": "2017-11-26",
                "url": "http://money.outbrain.com/2017/11/26/news/economy/crash-disaster/",
                "status": "APPROVED",
                "enabled": true,
                "cachedImageUrl": "http://images.outbrain.com/imageserver/v2/s/gtE/n/plcyz/abc/iGYzT/plcyz-f8A-158x114.jpg",
                "campaignId": "abf4b02153ee75f3cadc4fc128ab0419ab",
                "campaignName": "Boost 'ABC' Brand",
                "archived": false,
                "documentLanguage": "EN",
                "sectionName": "Economics",
            },
            "metrics":
            {
                "impressions": 18479333,
                "clicks": 58659,
                "conversions": 12,
                "spend": 9187.16,
                "ecpc": 0.16,
                "ctr": 0.32,
                "conversionRate": 0.02,
                "cpa": 765.6
            }
        }
    ],
    "totalResults": 27830,
    "summary": {
        "impressions": 1177363701,
        "clicks": 2615150,
        "conversions": 2155,
        "spend": 455013.97,
        "ecpc": 0.17,
        "ctr": 0.22,
        "conversionRate": 0.08,
        "cpa": 211.14
    },
    "totalFilteredResults": 1,
    "summaryFiltered": {
        "impressions": 18479333,
        "clicks": 58659,
        "conversions": 12,
        "spend": 9187.16,
        "ecpc": 0.16,
        "ctr": 0.32,
        "conversionRate": 0.02,
        "cpa": 765.6
    }
}

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. Outbrain'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.

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Snowflake's documentation includes a Data Loading Overview that guides you through the task of loading your data. A data loading wizard in the Snowflake web UI may be useful if you're not loading a lot of data, but for many organizations, the limitations on that tool will make it unsuitable. You can load your data with two manual steps:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion 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.

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 Outbrain.

And remember, as with any code, once you write it, you have to maintain it. If Outbrain 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.

Snowflake 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 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 BigQuery, To Postgres, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.