Access your data on LinkedIn Ads

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

Using their REST API, you can programmatically interact with your account to access your digital advertising data. By doing so, you can get aggregated metrics that, among others, include the following:

1. Counts of the clicks on the action button and the ad unit
2. The number of impressions or clicks for each card of the carousel ad and creative landing page clicks.
3. Count of comments and likes on each comment
4. Value of conversions and cost in the account's local currency.

You can retrieve all the available aggregated metrics for any user-defined period. Some other things to keep in mind are:

1. Rate limits. There are daily request Quotas per application, per user, and per-application developer as described in the documentation. These vary depending on the Application tier. Current day's usage and limits can be found in the application (choose application → Application Settings → Usage & Limits)
2. Authentication. Linkedin uses OAuth for authentication. An access token is valid for 60 days. Adding the access token to the requests' header (bearer authorization) is sufficient to get the reports.
3. Pagination. These endpoints that return a collection of items are always paginated.

Transform and prepare LinkedIn Ads data

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

1. The limitations of the database that you are going to use
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, for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly.

Additionally, you have to choose the right data types. Again, depending on the system, 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 your queries' expressivity and limit your analysts on what they can do directly out of the database.

Also, you have to consider that the reports you'll get from LinkedIn Ads are like CSV files in terms of their structure, and you need to identify somehow what and how to map a table into your database.

Transform and prepare LinkedIn Ads data for Amazon Redshift

Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, to load any data into it, you will have to follow its data model, a typical relational database model. 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 data is probably coming in a representation like JSON that supports a much smaller range of data types, you have to be careful about what data you feed into Redshift. This is to make sure that you have mapped your types into one of the data that Redshift supports.

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 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 supported as input by Redshift. These are the following:

• Amazon S3
• Amazon DynamoDB
• Amazon Kinesis Firehose

Load your LinkedIn 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 can do that by executing an HTTP PUT on the API 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 data to Amazon S3, using the same API and the endpoints for Object operations. As in the Bucket case, you can either access the HTTP endpoints directly or use your preferred library.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your 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 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 LinkedIn Ads to Redshift

So far, we just scraped the surface of what you can do with Redshift and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources. Instead of writing, hosting, and maintaining a flexible data infrastructure, RudderStack can handle everything automatically for you.

With one click, RudderStack seamlessly integrates with sources or services, creates analytics-ready data, and syncs your LinkedIn Ads to Redshift right away.