Facebook Ads to Redshift: Steps to Move Data Seamlessly

on Tutorial • June 20th, 2019 • Write for Hevo

Your organization must be spending many dollars to market and acquire customers through Facebook Ads. Given the importance and cost-share, this medium occupies, moving all important data to a robust warehouse such as Redshift becomes a business requirement for better analysis, market insight, and growth. This post talks about moving your data from Facebook Ads to the Redshift in an efficient and reliable manner.

Understanding Facebook Ads and Redshift

Facebook is the world’s biggest online social media giant with over 2 billion users around the world, making it one of the leading advertisement channels in the world. Studies have shown that Facebook accounts for over half of the advertising spends in the US. Facebook ads target users based on multiple factors like activity, demographic information, device information, advertising, and marketing partner-supplied information, etc. 

Redshift is a simple, cost-effective and yet very fast and easily scalable cloud data warehouse solution capable of analyzing petabyte-level data. Redshift provides new and deeper insights into the customer response behaviour, marketing, and overall business by merging and analyzing the Facebook data as well as data from other sources simultaneously. You can read more on the features of Redshift here.

Let us, deep-dive, into both Methods one by one.

How to transfer data from Facebook Ads to Redshift?

Data can be moved from Facebook Ads to Redshift in either of two ways:

Method 1: Use a fully managed Data Integration Platform like Hevo Data

Using an easy-to-use Data Integration Platform like Hevo helps you move data from Facebook Ads to Redshift within a couple of minutes and for free. There’s no need to write any code as Hevo offers a graphical interface to move data. Hevo is a fully managed solution, which means there is zero monitoring and maintenance needed from your end.

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Method 2: Write custom ETL scripts to load data

The manual method calls for you to write a custom ETL script yourself. So, you will have to write the script to extract the data from Facebook Ads, transform the data (i.e select and remove whatever is not needed) and then load it to Redshift. This method would you to invest a considerable amount of engineering resources

Method 1: Moving your data from Facebook Ad to Redshift using Hevo

The Hevo Data Integration Platform allows you to seamlessly transfer data from Facebook Ad to Redshift in two easy steps:

  • Configure the data source by authenticating your Facebook Ads Account

Adding Facebook Ads Source on Hevo

  • Next, configure the Redshift warehouse where you want to move your Facebook Ads data

Yes, it’s that easy. Hevo takes care of everything in the background automatically and ensures your data is transferred reliably to Redshift in real-time.

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Method 2: Copying your data from Facebook Ads to Redshift using Custom Scripts

The fundamental idea is simple – fetch the data from Facebook Ads, transform the data so that Redshift can understand it, and finally load the data into Redshift. Following are the steps involved if you chose to move data manually:

  1. To fetch the data you have to use the Facebook Ads Insight API and write scripts for it. Look into the API documentation to find out all the endpoints available and access it. These Endpoints (impressions, clickthrough rates, CPC, etc.) are broken out by time period. The endpoints will return a JSON output. Once you receive the output then you need to extract only the fields that matter to you.
  2. To get newly updated data as it appears in Facebook Ads on a regular basis, you also need to set up cron jobs. For this, you need to identify the auto-incrementing key fields that your written script can use to bookmark its progression through the data
  3. Next, to map Facebook ad’s JSON files, you need to identify all the columns you want to insert and then set up a table in Redshift matching this schema. Next, you would have to write a script to insert this data into Redshift. Datatype compatibility between the two platforms is another area you need to be careful about. For each field in the Insights API’s response, you have to decide on the appropriate data type in the redshift table.
  4. In the case of a small amount of data, building an insert operation seems natural. However, keep in mind that Redshift is not optimized for row-by-row updates. So for large data, it is always recommended to use an intermediary like Amazon S3 (AWS) and then copy the data to Redshift. In this case, you are required to –
    1. Create a bucket for your data
    2. Write an HTTP PUT for your AWS REST API using Postman, Python, or Curl
    3. Once the bucket is in place, you can then send your data to S3
    4. Then use a COPY command to load data from S3 to Redshift
  5. Additionally, you need to put in place proper frequent monitoring to detect any change in the Facebook Ad schema and update the script in case of any change in the source data structure.

Limitations of Using the Custom Code method to Move Data:

On the surface, implementing a custom solution to move data from Facebook Ads to Redshift may seem like a more viable solution. However, you must be aware of the limitations of this approach as well.

  1. Since you are writing it yourself, you have to maintain it too. If Facebook updates its API or the API sends a field with a datatype which your code doesn’t recognize, then you will have to modify your script likewise. Script modification is also needed even if slightly different information is needed by users.
  2. You also need a data validation system in place to ensure all the data is being updated accurately.
  3. The process is time-consuming and you might want to put your time to better use if a better less time-consuming process is available.
  4. Though maintaining in this way is very much possible, this requires plenty of engineering resources which is not suited for today’s agile work environment.

On the other hand, using a robust Data Integration platform like Hevo rids you of the above limitations. 

The Hevo Advantage:

  1. Being code-free, the data loading process is very easy in Hevo.  
  2. It just takes a couple of minutes to copy data in real-time from Facebook Ads to Redshift using Hevo. You just need to connect to Facebook Ad through API token and transfer your data to Redshift.
  3. Hevo’s AI-powered fault-tolerant architecture allows it to transfer your latest data accurately from Facebook Ad to Redshift in real-time.
  4. Hevo has automatic schema detection, mapping, update features. Therefore Hevo always scans for any schema changes in Facebook Ads, and if detected it quickly makes the relevant changes to Redshift. This is the most important feature of Hevo making it a necessity in database transfer.
  5. Hevo sends you real-time email and Slack notifications regarding data replication status, any detected schema changes, etc. Also, Hevo’s activity log is very handy as it enables you to be updated with user activities, successful executions, data transfer failures, etc.
  6. Hevo offers support through email and Slack – 24×7.

Opting for a custom ETL might grant you more freedom but you have to pay a rather high and ongoing price in terms of engineering resources. The array of benefits and hassle-free experience Hevo brings to the table makes it more than a value-for-money proposition.

Additionally, Hevo’s versatile data platform helps you move data from not just Facebook Ads for free, but numerous other data sources like databases, applications, cloud storage, SDK, etc. (www.hevodata.com/integrations) to Redshift making it a great choice for building a robust data infrastructure for your business.

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