After you have accessed your data on Twitter Ads, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- 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.
Of course, when you are 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 the 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.
Each table is a collection of columns with a predefined data type like an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types.
A typical strategy for loading data from Twitter Ads to a Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the Twitter Ads API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Postgres compatible data type.