How to load data from LinkedIn Ads to PostgreSQL

This post will help you with syncing your LinkedIn Ads data to PostgreSQL. By doing this you will be able to perform advanced analytics on a system that is designed for this kind of data payload, like PostgreSQL. Alternatively, you can simplify the process of syncing data from LinkedIn Ads to PostgreSQL by using RudderStack, where the whole process will be handled by RudderStack and you can focus on what matters, the monitoring of your digital marketing spending and performance data. By analyzing the data produced by LinkedIn Ads you can launch campaigns tailored based on your business goals and effectively expand the reputation of your product to new audiences.

Access your data on LinkedIn Ads

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

Using the REST API that LinkedIn Ads offers 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.

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

• 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)
• 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.
• Pagination. API endpoints that return a collection of items are always paginated.

Transform and prepare your 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 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 for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly.

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.

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.

Each table is a collection of columns with a predefined data type as 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 LinkedIn Ads to a Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the LinkedIn 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.

Load data from LinkedIn Ads to PostgreSQL

For example, if an endpoint from LinkedIn Ads returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. tables can then be created on your database using the CREATE SQL statement.

Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database.

The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the Postgres instance, in this way much larger datasets can be inserted into the database in less time. COPY requires physical access to a file system in order to load data.

Nowadays, with cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.

Updating your LinkedIn Ads data on PostgreSQL

As you will be generating more data on LinkedIn Ads, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that for any reason have been updated on LinkedIn Ads.

You will need to periodically check LinkedIn Ads for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a PostgreSQL table is achieved by creating UPDATE statements.

Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because LinkedIn Ads does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.

In general, ensuring the quality of the data that is inserted in your database is a big and difficult issue and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.

The best way to load data from LinkedIn Ads to PostgreSQL

So far we just scraped the surface of what can be done with PostgreSQL and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from, and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.

A possible alternative, instead of writing, hosting, and maintaining a flexible data infrastructure, is to use a product like RudderStack that can handle this kind of problem automatically for you.

RudderStack integrates with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely move all your data from LinkedIn Ads to PostgreSQL and start generating insights from your data.