Facebook Ads to Snowflake: 2 Hassle-Free Ways to Connect

Unlock the full potential of your Facebook Ads data by integrating it seamlessly with Snowflake. With Hevo’s automated pipeline, get data flowing effortlessly—watch our 1-minute demo below to see it in action!

This article will provide you two major pathways for setting up your Facebook Ads to Snowflake integration. Your Facebook Ads generate a lot of transactional data about the performance of the Marketing campaigns by the minute. Pushing this data to a centralized Data Warehouse like Snowflake increases your ability to analyze the data in-depth, compare campaigns, run forecasts, and make predictions.

This article will first introduce you to Facebook Ads and Snowflake and then will elaborate on the two approaches of their integration. Furthermore, it will discuss the challenges that you may face when implementing the manual ETL approach. Read along to know the step-by-step integration of Facebook Ads to Snowflake.

Prerequisites

• Working knowledge of Databases and Data Warehouses.
• A Snowflake account.
• A Facebook account.
• Clear idea regarding what data is to be transferred.
• Working Knowledge of SQL.

Introduction to Facebook Ads

Facebook is arguably one of the most popular platforms for marketers to advertise on. Facebook ads generate a lot of data about campaigns across Impressions, Clicks, Cost Spread, and so on. The Facebook Marketing API makes this treasure trove of data available for authorized users. 

Facebook offers a variety of advertising formats, and the Facebook advertising manager will help you choose a format based on your company’s marketing goals. Whether you want to Increase visibility, Quickly check your business, or Increase your conversion rates, Facebook provides services for all of these tasks.

Both businesses and consumers benefit from paid advertising. Companies can reach audiences in ways that conventional advertising cannot achieve, and can easily track the effects of online and offline advertising. Offline tracking data shows companies the true value of their Facebook payments. The more visible your post is to your audience, the more likely it is to bring in sales. Also, customers will be able to easily access company information by viewing relevant advertisements.

Introduction to Snowflake

Snowflake is a leading cloud data warehouse platform that has enjoyed steady growth and increasing popularity in recent times. Snowflake provides a scalable cloud platform that is both business and developer-friendly and supports advanced data analytics. The Robustness, Cost-effectiveness, and Scalability of Snowflake make it very attractive for businesses to adopt.

There are multiple Data Warehouses available, however, what makes Snowflake unique is its architecture skills and its data-sharing abilities. Snowflake’s architecture enables storage and computing to scale independently, so customers can use storage and computing separately and pay for it. Also, users can protect their data in real-time.

The best feature of the snowflake is that it offers data storage and data computation capabilities separately. Snowflake is designed to ensure that minimum effort and interaction are required from the users for any performance or maintenance-related activity. Snowflake’s auto concurrency property provides users the facility to set a minimum and maximum cluster size and the scaling will then happen automatically over this range at a very rapid speed.

Methods to Set up Facebook Ads to Snowflake Integration

You can implement the following two methods to set up your Facebook Ads to Snowflake Integration:

Method 1: Using Hevo to Set up Facebook Ads to Snowflake Integration

Hevo , an official Snowflake partner, offers you seamless data migration from Facebook Ads to Snowflake in two very simple steps: 

• Authenticate Source: Authenticate and connect your Facebook Ads account as a data source.

To get more details about this Authentication step visit this link.

• Configure Destination: Configure the Snowflake Data Warehouse you will be migrating your data to as a data destination.

To get more details about the Configuration step, visit this link.

You now have a real-time pipeline for syncing data from Facebook Ads to Snowflake.

More Reasons to Choose Hevo Data:

• Faster Implementation: A very quick 2-stage process to get your pipeline setup. After that, everything’s automated while you watch data sync to Snowflake or any other destination in real-time. 
• Wide Range of Connectors – Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
• In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
• Near Real-Time Replication – Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.   
• Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
• Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
• 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
• Security – Discover peace with end-to-end encryption and compliance with all major security certifications including HIPAA, GDPR, SOC-2.

Method 2: Manual ETL Process to Set up Facebook Ads to Snowflake Integration

This approach involves setting up a manual ETL process to transfer data from Facebook Ads to Snowflake. The ETL process will pull data from the Facebook Ads platform using the Facebook Marketing API at intervals, transform the data to conform to an appropriate user-defined schema, and then push the same to Snowflake using SnowSQL or any of the Snowflake connector clients(Python, Node.js, .NET, etc) 

The steps you can use to set up such a manual ETL process are as follows:

• Step 1: Set up Facebook Marketing API
• Step 2: Setting Up the Connection from Facebook Ads to Snowflake
• Step 3: Create a Data Loading Mechanism
• Step 4: Write the Code for Data Sourcing and Transformation
• Step 5: Write the Code for Data Loading
• Step 6: Writing Data into Snowflake

Step 1: Set up Facebook Marketing API

Setup your Facebook Marketing API app on the Facebook developer portal that will allow you to have programmatic access to your Facebook ads. This corresponds to setting up a data source in any typical ETL operation.

Step 2: Setting Up the Connection from Facebook Ads to Snowflake

Select a language for the destination side – SQL or your choice of Python, Node.js, Go, etc. Depending on your language choice, set up the appropriate client on a server and test your connection to the remote Snowflake destination.

Step 3: Create a Data Loading Mechanism

Once you can connect to the destination, you will need to create the loading mechanism. This is essentially an SQL stored procedure or a script depending on whether you are using SQL or a language client. 

Step 4: Write the Code for Data Sourcing and Transformation

The use of a loading mechanism means that the overall process is split into two tranches: (i) Data sourcing and transformation and (ii) Data loading. In the first tranche, you will need to write and deploy a script that has the following components:

• An HTTP client to make an API request to the Facebook Marketing API to pull the data
• Write a script to convert the(typically) JSON data to any desired schema probably a CSV schema if you are using an SQL client like SnowQL and it is easier to load CSV correctly via SQL stored procedure. This can be also done by using an online tool like the one provided in this link.
• Code for writing the CSV(or other formats) transformed data to disk in preparation for eventual loading into Snowflake

Step 5: Write the Code for Data Loading

For the loading tranche, you will need an SQL stored procedure or a script written in any language of your choice, to load the data to Snowflake. If you are using SQL, then you will likely need to stage your created CSV files in the cloud – Snowflake allows you to stage your files across all major clouds in the following locations: Amazon S3, Google Cloud Storage, and Microsoft Azure.

Step 6: Writing Data into Snowflake

After the staging, the data can be moved into tables in Snowflake using the COPY_INTO command as part of your stored procedure. If you’re not going through the SQL route, then depending on the language you are using, the client library should support pushing the data directly to Snowflake from your script — so it’s an easier process with a single script to read, transform and write the data to Snowflake. The python script will look similar to the code given below.

import dask.dataframe as dd
df = dd.read_csv('YOUR Large CSV')
df = df.partion(npartions=100)
df.to_csv('folder_name/export_*.csv')#files will be based on number of partions
import glob
lst_files = glob.glob('folder_name/*csv')
#your sqlalchemy engine or snowflake conn object
@dask.delayed
def run_put_query(conxn):
    conxn.execute(
        f"PUT file://{csv_file} @{staging_area_path}/{folder_name} overwrite=true")
    return
delayed_objects = []

for csv_file in lst_files:
    res = run_put_query(
        conxn=conxn
    )
    delayed_objects.append(res)
output = dask.compute(*delayed_objects,scheduler='threads',num_workers=10)

Challenges with the Manual ETL Process

The manual approach of setting up an ETL process that connects Facebook Ads to Snowflake is effective but it comes with its own set of challenges:

• A lot of technical knowledge is required to set up this manual ETL process, and it may place additional skill/personnel requirements on your business.
• Most standalone ETL scripts do not scale well — this means that, depending on the size of data to be transferred from Facebook Ads to Snowflake in each run, as well as the number of runs per day, the single ETL script may be insufficient to handle all your organization’s data transfer needs.
• The above steps would not be enough if you are looking to bring data from Facebook Ads to Snowflake in real-time. This would need to write additional code and set up cron jobs to move data in a timely fashion
• In case your use case involves cleaning or transforming data in any way, the above process would need to be tweaked to handle that. This is an additional overhead.
• A lot of the tasks involved in setting up and maintaining a manual process like this may be touch-and-go, and things will inevitably break. Without formal support and assistance, mission-critical data migration processes from Facebook Ads to Snowflake will fail and that can be costly to the business.

Conclusion

The article provided you an introduction to Snowflake and Facebook Ads, explaining their individual importance in the current business context. It also explained the step-by-step process of setting up Facebook Ads to Snowflake integration. The two methods discussed here are both equally effective in terms of final results, but as mentioned above, there are numerous challenges that you will face while implementing the manual method. If you have no qualms about investing an excessive amount of time and resources, go for the first method. Otherwise, you can opt for Hevo to seamlessly transfer your data from Facebook Ads to Snowflake for free.

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