Social Media Performance Data Extraction - Third-Party Connectors vs Hardcore Data Engineering using PySpark

TL;DR

Using PySpark with direct API integrations (Meta, Google Ads, etc.), it's possible to pull two full years of granular ad performance data in under 2 hours at the agency level! I'm talking about more than 400 client accounts , thousands of campaigns, millions of rows at Ads level granularity! —the kind of scale that used to take days or simply fail when using third-party connectors due to timeouts or data volume limits.

That said, building these pipelines isn't a walk in the park! It took me around three weeks per API to fully develop, test, and productionise the ingestion scripts, complete with robust error handling, pagination logic, retry mechanisms, and Spark optimisations!

Third-party connectors can have you up and running in a day. But you’ll quickly run into limitations around flexibility, performance, and recurring costs—especially as your data volume grows.

If you’re aiming for scalable, cost-efficient, and fully controlled social performance data ingestion, PySpark is the way to go—just be prepared to invest some engineering time up front!

When it comes to extracting and processing social performance data from platforms like Meta (Facebook) Marketing API and Google Ads API, organizations (especially agencies) often face a crucial decision: Should they rely on third-party connectors like Fivetran, Adverity, Growth Nirvana, Supermetrics etc or build their own robust pipelines using tools like PySpark? Both approaches have their merits and challenges.

I’ve done both—and here’s what I’ve learned!

In this article, I am going to compare these two strategies, focusing on performance, flexibility and cost!

Third-Party Connectors: Pros & Cons

Pros:

• Quick Setup: Most third-party connectors offer plug-and-play integration with major APIs and data environments like Databricks, Fabric or Google BigQuery. You can be up and running in hours rather than days or weeks.
• User-Friendly: These tools often come with graphical interfaces and require minimal coding, making them accessible even to non-engineers!
• Maintenance Included: The provider handles updates, bug fixes, and API version changes, reducing your operational burden.
• Standardization: Pre-built connectors enforce consistent data schemas and transformations.

Cons:

• Limited Flexibility: Customizing data extraction logic or handling complex business rules is usually restricted or cumbersome. If you need to create a custom schema, that's usually not possible with many connectors.
• Performance Bottlenecks: Many connectors process data sequentially or in small batches, which can be slow for large datasets. Even Google's own Google ADS connector is slow if you have lots of ads data!
• Opaque Operations: Debugging issues or understanding exactly how data is fetched and transformed can be difficult due to black-box implementations.
• Recurring Costs: Licensing fees can add up, especially as data volumes grow or more users need access. If you are an agency managing hundreds of clients' campaigns, be prepared to pay a fortune every month!

PySpark Data Engineering: Power and Control

Pros:

• Full Flexibility: With PySpark, you can control every aspect of data extraction, transformation, loading (ETL) and even apply machine learning, time series analysis, or data cleaning directly within your pipelines.. This is invaluable when dealing with evolving API structures or custom reporting needs.
• Scalability: PySpark is built for distributed data processing and parallel execution which allows you to process massive datasets efficiently!
• Optimized Performance: Fine-tune API calls, batch sizes, retry logic, and error handling to maximize throughput and minimize latency. You have full control over every bits and bytes!
• Transparency: Every step is visible and customizable, making debugging and auditing straightforward. If you are following clean code standards of course!
• Cost Efficiency: No per-connector/per user/amount of data processed fees! Infrastructure costs are easier to optimize at scale. For example, Fabric F2 capacity is just around 220£/month. If it's not powerful enough, just upgrade or use auto-scale up/down! You will know exactly how much you are going to pay each month and quite likely it’ll be a drop in the ocean compared to the connector fee!
• Portability of the code: As I mentioned, it took me approximately 3 weeks per API to write a notebook that ingests data from META or Google Ads. The code was originally written in Microsoft Fabric. It only took me a few days to adapt these notebooks for Databricks — and I wouldn’t even call myself an experienced Databricks user! (Yet! I'm learning Databricks these days!) + If you have a code running properly, you can deploy the very same notebook to another agency in a day!

Cons:

• Steeper Learning Curve: Setting up PySpark pipelines requires stronger engineering skills and familiarity with both Spark and the APIs involved.
• Maintenance Load: You’re responsible for handling API changes, errors, and scaling logic! But, although everyone I speak to says that APIs change frequently, that’s not entirely true. If you're working with well-established APIs like META or Google Ads, you'll have plenty of time to maintain your code. These kinds of stable APIs don't change overnight without notice!
• Initial Development Time: Building robust, production-grade pipelines takes longer upfront compared to third-party solutions. Also, you need to understand the API mechanics that you are working on !(OAuth2, pagination, rate limits etc). You need to spend lots of time reading the related API's documentation!

Performance Comparison

Let’s be clear: PySpark wins at scale!

Its ability to parallelize API requests and handle large-scale transformations means you spend less time waiting for your data and more time analysing it.

3rd party connectors may batch requests inefficiently or store data in formats that limit downstream use. With PySpark, you can create your own schema, optimize joins, and control refresh frequencies—something that makes a big difference in enterprise-grade analytics.

Pulling 2 years worth of data for over 400 clients with thousands of campaigns in under 2 hours! That's simply not achievable with 3rd party connectors!

You can also set your incremental refresh logic freely! Like, pull the initial 1 year-data first, then get your last 2 days or 2 hours data, append it, drop duplicate rows! In less than 10 mins!

Why Favour PySpark?

If you're a small team or agency looking for basic insights and fast delivery, 3rd party connectors may suffice. But if you're building a data platform, handling multi-source integration, transformations, and data science workflows, going API-first with PySpark is the clear winner.

Think of third-party connectors as training wheels! When you’re ready to build something robust, repeatable, and fully under your control—switch to PySpark.

While the initial development is more demanding, the long-term benefits in performance, cost, and adaptability make PySpark the superior choice for robust social performance data engineering!

Where do you want your social media data?

Well, it can be a Fabric lakehouse, warehouse, Databricks or Google BigQuery!

Any data platform that allows you to run notebooks will do the job!