How to load data from LinkedIn Ads to Redshift

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This post helps you with loading your LinkedIn Ads data to Redshift. If you are looking to get analytics-ready data without the manual hassle you can integrate LinkedIn Ads to Redshift with Blendo, so you can focus on what matters, getting value out of your advertising data.

Access your data on LinkedIn Ads

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

Using their REST API, you can programmatically interact with your account in order to gain access to 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 on 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 of likes on each comment
  4. Value of conversions and cost in the account’s local currency.

All the available aggregated metrics can be retrieved for any user-defined time period.

You will need more time to read this post than integrating LinkedIn Ads to Redshift.

Effortlessly Sync All Your LinkedIn Ads Data to Redshift

In addition to the above, the things that you have to keep in mind when dealing with their API, 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 in order to get the reports.
  3. Pagination. These endpoints that return a collection of items are always paginated.

 

About LinkedIn Ads

LinkedIn Ads is an advertising platform that enables businesses to reach their ideal customers on the world’s largest professional network, consisting of more than 560 million users. By using LinkedIn Ads you can effectively:

  1. Increase awareness of your products and services
  2. Lure potential users to click to a new page on your website
  3. Attract new followers to your company’s company page thus increasing its visibility

Additionally, LinkedIn allows getting pretty smart about targeting the right audiences by aiming at a specific company size, job title, industry and geographic location. This granularity enables you to easily reach people and companies that matter to your business.

Regarding the different types of ads that LinkedIn currently supports, the options are quite many:

  1. Sponsored Content: used for promoting content from your company’s page
  2. Direct Sponsored Content: used for promoting personalized content without having to publish it on your company’s page.
  3. Sponsored InMail: allows you to delivered personalized private message to potential customers.
  4. Text Ads: Just like with other advertising platforms like Google and Bing, these ads are placed on the right rail of the news feed page and are available only on the desktop version.
  5. Dynamic Ads: refers to dynamically generated, highly personalized ads that drive users to your company page or apply to a job post.
  6. Lead Gen Forms: refers to prefilled forms with user’s LinkedIn information that aim to generate quality leads.

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 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.

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 somehow identify what and how to map to 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, in order to load any data into it, you will have to follow its data model which is 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 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:

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 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 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 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 is able to read multiple files in 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.

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