ETL depuis LinkedIn Ads vers Databricks en quelques clics

Integrating LinkedIn Ads with other data sources can streamline your data management and supercharge your marketing analytics.

Here are a few key reasons to extract data from LinkedIn Ads:

• Understanding What Works: Extracting LinkedIn Ads data lets you see how well your campaigns are performing—like which ads are driving clicks, engaging professionals, or converting leads. It’s essential for figuring out what’s working and optimizing your strategy.
• Getting to Know Your Audience: LinkedIn Ads provide deep insights into the professionals engaging with your content—their job titles, industries, and company sizes. By analyzing this data, you can refine your targeting and focus on reaching the decision-makers who matter most to your business.
• Measuring ROI: To know if your ad spend is delivering value, you need to track performance metrics. By extracting LinkedIn Ads data, you can compare your spend with the outcomes, ensuring your investments are driving the right results.
• Creating Custom Reports: Standard reports often don’t tell the full story. By extracting your LinkedIn Ads data, you can combine it with other datasets, like CRM or financial data, to create reports tailored to your specific business needs.
• Connecting the Dots: Integrating LinkedIn Ads data with other marketing tools or platforms helps you see the bigger picture—how LinkedIn fits into your overall marketing strategy and its impact on your other campaigns.
• Optimizing Your Ads: Use your data to automate ad optimizations—adjusting bids or pausing underperforming ads—ensuring every dollar spent is driving the best possible results.

With Rivery, extracting and leveraging your LinkedIn Ads data has never been easier.

All changes made to the LinkedIn Ads API can be found here.

On average the API updates once a month. Rivery manages the LinkedIn Ads integration for you, so you don’t need to worry about API upgrades breaking your data pipelines. Rivery will continually maintain the integration with the latest API versions. Most upgrades would be seamless requiring no action on your end. If any API version change require your attention — like added, removed, or modified reports/data fields that you may be using — Rivery will notify you ahead of time with the details and any possible actions required.

Rivery offers a set of Predefined Reports for LinkedIn Ads for rapid data integration. Using these reports, you can quickly analyze the data without having to learn the intricacies of the way the data is organized within the LinkedIn Ads API. Simply provide your LinkedIn Ads credentials and choose the report to load into your cloud data warehouse.
The LinkedIn Ads data source in Rivery comes with the below predefined reports (click to navigate to the full data structure documentation):

• Accounts
• Campaigns
• Campaign Groups
• Creatives Shares
• Ad Statistics
• Ad Analytics

Utiliser les connecteurs de données de Rivery est très simple. Il suffit d’entrer vos identifiants, de définir la cible où vous souhaitez charger les données (par exemple, Snowflake, BigQuery, Databricks ou tout data lake) et de mapper automatiquement le schéma pour le générer sur la cible. Vous pouvez contrôler les données que vous avez besoin d’extraire de la source et la fréquence de synchronisation de vos données. Pour en savoir plus, suivez la documentation spécifique.

Oui. Tous les connecteurs de données au sein de Rivery sont conformes aux normes de sécurité et de confidentialité les plus élevées, y compris : RGPD, HIPAA, SOC2 et ISO 27001. De plus, lorsque les données sont transférées vers votre entrepôt de données cible, vous pouvez configurer le transfert via votre propre système de fichiers cloud plutôt que via Rivery.

Les connecteurs de données les plus populaires concernent des cas d’usage comme le marketing, les ventes et la finance. Ils incluent Salesforce, HubSpot, Google Analytics, Google Ads, LinkedIn Ads, Facebook Ads, TikTok et plus encore.

Rivery prend en charge la réplication de base de données CDC et l’extraction SQL standard, vous pouvez donc choisir la méthode qui vous convient le mieux. En savoir plus ici.