How to Connect Twitter Ads to BigQuery Within Minutes?

Twitter has over 586 million monthly active users¹; therefore, it is gold for companies seeking to reach their ideal target. I have seen many companies use Twitter as a platform to market their campaigns and use Twitter Ads to gather vital data that helps fine-tune their strategy.

You are one of those whose priority is your brand, and you’re using Twitter Ads to grow that brand and access many customers. But now, you might want to know how to move that data from the Twitter Ads environment to the BigQuery Cloud-based Data Warehouse efficiently for deeper analytics. You’ve come to the right place. 

In this blog, I will explain two easy ways of moving your Twitter Ads to BigQuery and outline the pros and cons of each to help you decide which method best suits your specific requirements. Now, let’s dive in!

Prerequisites

• Working knowledge of Databases and Data Warehouses.
• An understanding of working with RESTful APIs.
• An active Twitter developers account.
• A set-up BigQuery Data Warehouse.
• Clear idea regarding what data is to be transferred.
• Working Knowledge of SQL.

Introduction to Twitter Ads

Twitter Ads is an Advertising platform owned and operated by Twitter Inc.  It allows you to connect with the audience present on Twitter globally, and get action-driven results with a goal to add value to your business. You can create Objective-based campaigns, Analyze your performance, and Reach the right target audience using Twitter Ads. 

Twitter Ads provides a robust set of APIs that enable you to extract data from Twitter. They also allow you to manually export data from the user interface as well. This data can then be moved to a modern Data Warehouse such as BigQuery to answer deeper questions about the ROI from this channel.

Introduction to Google BigQuery

Google BigQuery is a scalable, cost-effective Data Warehouse that enables fast analysis of Big Data. It supports RESTful web services and works in conjunction with GCS (Google Cloud Storage). It has many features such as Data Management, Data Querying, Access Control, and Machine Learning capabilities.

Some features of Google BigQuery that make it a popular Data Warehouse are:

• Managed Service: BigQuery’s performance tuning and backend configuration are handled by Google. This makes it easier to use than other Data Warehouses where you may be required to manually handle these.
• Distributed Architecture: Google manages to compute resources dynamically and so you do not have to handle them.
• Easy to use: You do not have to build your own data center when using BigQuery as you only have to load your data into BigQuery and pay for what you use.
• Fast and detailed insights: BigQuery enables seamless integration with many widely-used analytics tools like Looker and Google Data Studio. This makes it easy to understand your data.

Methods to Set up Twitter Ads to BigQuery Integration

The following two methods can be used to set up Twitter Ads to Bigquery Integration:

Method 1: Best Way to Automatically Connect Twitter Ads to BigQuery- Using Hevo

Step 1: Authenticate Data Source

Step 2: Connect the BigQuery Data Warehouse as your destination.

Method 2: Manual ETL Process to Set up Twitter Ads to BigQuery Integration

The broad steps to this approach are as follows:

• Step 1: Extract Data Using Twitter Ads API
• Step 2: Convert Data into BigQuery Format
• Step 3: Load Data to BigQuery

Step 1: Extract Data Using Twitter Ads API

Twitter Ads API allows businesses to create, run, and manage the Ads programmatically. It enables you to tailor Ad Campaigns by selecting different target options and parameters. Twitter has a dedicated set of APIs that allow you to extract analytics on the content you promote. This may include Impressions, Clicks, Engagement, Spend, and more. 

Below is a sample REST API endpoint request that is used to retrieve all promoted tweets. You can run this command on tools like Postman or using curl commands in your terminal.

https://ads-api.twitter.com/7/accounts/:account_id/promoted_tweets

The API response is in JSON format as follows:

{
  "request": {
    "params": {
      "promoted_tweet_ids": [
        "1efwlo"
      ],
      "account_id": "18ce54d4x5t"
    }
  },
  "next_cursor": null,
  "data": [
    {
      "line_item_id": "96uzp",
      "id": "1efwlo",
      "entity_status": "ACTIVE",
      "created_at": "2017-06-29T05:06:57Z",
      "updated_at": "2017-06-29T05:08:46Z",
      "approval_status": "ACCEPTED",
      "tweet_id": "880290790664060928",
      "deleted": false
    }
  ]
}

Here are some of the other aspects to bear in mind when dealing with Twitter API:

• Rate Limits: Twitter imposes rate limits on API calls. There is a 15 minutes window restriction of API calls per endpoint but there is no restriction for concurrent API calls. 
• Authentication: Twitter has a mandatory oAuth authentication. To access Ads API, Twitter mandates the oAuth 1.0a authentication requirement.
• Pagination: Some sources have the pagination ability for retrieving data with a page count varying from 200 – 1000. This depends on specific endpoint resources. In some sources, there is a sorting method for retrieving data.

Step 2: Convert Data into BigQuery Format

Before loading Twitter Ads data to BigQuery, we will need to create a data schema to store Twitter Ads data. Google BigQuery supports different data types such as INTEGER, BOOLEAN, DATETIME, etc.

Build a table to receive each value in the API response data. The response data was received in the previous step is in the JSON format. While this can be loaded directly to BigQuery, you may also choose to flatten the JSON and then store it. The choice would depend on the use case that you are trying to solve. 

Step 3: Load Data to BigQuery

GCP (Google Cloud Platform) provides a very useful guide on loading data into Google BigQuery. You can either load the data using BigQuery GUI or can use bq load command to load data.

Repeat the above steps until all your data is loaded into BigQuery.

Challenges of Loading Data from Twitter Ads to BigQuery using ETL scripts

1. Infrastructure Maintenance: Twitter has a rich set of APIs. These APIs may be updated at any given time. So, you will need to invest in the engineering team to constantly monitor and maintain the ETL code. 
2. Real-time Data: You have successfully created a program that loads data from Twitter Ads to BigQuery. However, the program does not load data in real-time. To solve this challenge, you will need to write additional code.
3. Data Transformation: Often, the data extracted from Twitter Ads would need to be cleaned, transformed, and enriched before loading to the warehouse so that it is ready to be analyzed. For example, you may want to transform currencies into a common denomination or standardize time zones. Achieving this needs you to build additional code. This adds to the engineering overhead. 

To overcome these challenges, try Hevo. Loading data into BigQuery using Hevo is easier, reliable, and fast. Hevo is a no-code automated data pipeline platform that solves all the challenges described above. You move data from Twitter Ads to BigQuery in the following two steps without writing any piece of code. 

To get more details about Configuring BigQuery with Hevo Data visit our documentation.

More reasons to try Hevo:

• Minimal Setup: Hevo can be set up using a point and click interface without any engineering assistance needed. 
• No Data Loss: Hevo ensures that the data is moved reliably and accurately to BigQuery. 
• 100’s of Out of the Box Integrations: In addition to Twitter Ads, Hevo supports a wide range of data sources including Databases, SDKs, and Cloud Applications, and more. This ensures that all data pipeline needs of your rapidly growing business are met on demand. 
• Automatic Schema Detection and Mapping: Hevo scans the schema of incoming Twitter Ads data. When changes are detected, Hevo handles them and incorporates the required changes into the BigQuery, automatically. This saves you the manual effort of having to deal with schema changes.
• Exceptional Support: Hevo provides 24/7 technical support on both emails and chats to ensure that you have a reliable partner to help you in your hour of need.

Use Cases of Connecting Twitter Ads to BigQuery

1. Performance Analysis: In a central data warehouse, you can measure the campaign’s effectiveness using engagement metrics, conversion rates, and ROAS.
2. Audience Insights: These insights help us understand how audiences behave and are demographically, enabling us to better segment and target them in future campaigns.
3. Trend Analysis: You can identify trends and patterns over time so that they can improve ad spend and bring overall alignment to historic performance data.
4. Cross-channel reporting: You can integrate Twitter Ads data with other marketing channels like Google Ads or Facebook Ads to get a better picture and make some over reports on general marketing performance.
5. BigQuery predictive analytics: Take advantage of machine learning models found within BigQuery, in order to give you some predictive forecasts on future ad performance and customer acquisition trends that will make for more data-driven decisions.

Resources

1. Twitter Statistics

Conclusion

The article gave you an introduction to Twitter Ads and Google BigQuery. It also explained 2 step by step procedures that you can use to transfer data from Twitter Ads to BigQuery. Furthermore, the article discussed the certain challenges that accompany the Manual process of custom coding the ETL process.

If you have the time and resources to set up the Twitter Ads to BigQuery integration manually, then you can opt for the custom coding method. However, if you want to automate the data transfer process and save time, go for the data pipeline method. Hevo Data can replicate your Twiter Ads data to any Data Warehouse such as BigQuery, Redshift, Snowflake, or a destination of your choice without writing code in just a few minutes.

Frequently Asked Questions