Access your data on Twitter Ads

The first step in loading Twitter Ads data to any data warehouse solution is to access your data and start extracting it.

By using their API, businesses can create, run and manage ad campaigns programmatically on Twitter. A big part of it is also a rich reporting system that helps you tailor your campaigns by selecting different targeting options and placement parameters. You can also retrieve detailed statistics on the performance of your campaigns by generating reporting or historical backfills.

Using this API, a user can retrieve details associated with the current account regarding the following resources:

• Campaigns
• Features
• Lineitem Apps & Lineitems
• Promoted Accounts & Promoted tweets reference
• Scheduled promoted tweets reference
• Funding Instruments
• Media Creatives
• Recommendations
• Targeting Criteria
• Account Media
• Scheduled/Promoted/Organic/Draft Tweets

Various reports can also be fetched as long as they are valid combinations between an entity and segmentation types, such as:

• Reach Campaigns Report
• Reach Funding Instruments Report
• Auction Insights Report

In addition to the above, the things that you have to keep in mind when dealing with the API, are:

• Rate limits. There is no restriction for concurrent calls. There is a restriction for API calls per endpoint in 15-minute windows. However, in general, limits are generous for most endpoints and should not impede use cases.
• Authentication. You authenticate on Twitter Ads using OAuth.
• Pagination. There is a pagination ability for retrieving data in some resources, with a page count that varies from 200 to 1000 depending on the specific resource endpoint. There is also a sorting method for retrieving data in some resources.

Transform and prepare your Twitter Ads data for Amazon Redshift

After you have accessed your data on Twitter Ads, you will have to transform it based on two main factors,

1. The limitations of the database that the data will be loaded onto
2. 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 you want to push data into Google BigQuery, you can send nested data like JSON directly.

Of course, when you deal 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 it into the database.

Also, you have to choose the right data types. Again, depending on the system 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.

Transform and prepare your Twitter Ads data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, to load your data into it, you will have to follow its data model which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.

Also, each attribute should adhere to the data types that are supported by Redshift.

As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift.

Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

• Amazon S3
• Amazon DynamoDB
• Amazon Kinesis Firehose

Load your Twitter Ads data into Amazon Redshift

To upload your data to Amazon S3 you will have to use the AWS REST API. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the REST endpoints for S3.

You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending your data to Amazon S3, using again the same API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.

The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Amazon Redshift documentation.

Redshift is not designed for INSERT-like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.

You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift can read multiple files simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.

The best way to load data from Twitter Ads to Amazon Redshift

So far we just scraped the surface of what you can do with Amazon Redshift and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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