After you have accessed your data on DoubleClick, you will have to transform it based on two main factors,
- The limitations of the database that is going to be used
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example you want to push data into Google BigQuery, then you can send nested data like JSON directly, but keep in mind that in the case of a SOAP API like DoubleClick, you get XML responses.
Of course, when dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.
With DoubleClick data, you have two main additional sources of complexity. When it comes to data types you have to keep in mind that SOAP is using XML to describe the service and data, so the data types that you have to map are coming from XML and might have automatically be transformed into the primitive data types of the language that you are using.
Also, you have to consider that the reports you’ll get from DoubleClick are like CSV files in terms of their structure and you need to somehow identify what and how to map to a table into your database. This way you will be able to join, combine and query your data in order to assess the performance of various ads and finally improve ROI for display ad campaigns.
Data in Snowflake is organized around tables with a well-defined set of columns with each one having a specific data type.
Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types are also supported. With Snowflake, it is possible to load data directly in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.
There is also one notable common data type that is not supported by Snowflake. LOB or large object data type is not supported, instead, you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from DoubleClick to Snowflake is to create a schema where you will map each API endpoint to a table.
Each key inside the DoubleClick API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Snowflake data type.
Of course, you will need to ensure that as any data type from the DoubleClick API might change, you will adapt every database tables accordingly, there’s no such thing as automatic data type casting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.