Twitter is a huge Social platform with more than 330 million monthly active users. It provides companies with an amazing niche to find their target users. Many companies use Twitter as a platform for their Marketing Campaigns. These Advertisements also act as a source of data that can be analyzed to improve the company’s Marketing Campaign. Twitter Ads, is a popular tool that generates data for this very purpose. It collects data from the company’s Advertisements posted on Twitter and is highly useful as it can tailor-make the data according to your requirements.

Does your company use Twitter Ads to build your brand and acquire more customers? Are you looking for easy ways to move this data to your BigQuery Cloud-based Data Warehouse for in-depth Analytics? If your answer is a yes, then you have landed on the right post. In this article, you will learn two methods to move data from Twitter Ads to BigQuery. The blog will also discuss the pros and cons of these approaches so that you can critically compare the two and pick the one that suits you best. Read along to understand the 2 methods for transferring data from Twitter Ads to BigQuery.

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 Logo
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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.

To learn more about Twitter Ads visit here.

Introduction to Google BigQuery

BigQuery's Logo
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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.

To learn more about Google BigQuery, click here.

Methods to Set up Twitter Ads to BigQuery Integration

Method 1: Manual ETL Process to Set up Twitter Ads to BigQuery Integration

This method involves, Extracting data from Twitter Ads, Transforming and Preparing data, and Loading the data to Google BigQuery. This would need you to deploy engineering resources that have experience with both Twitter Ads and BigQuery and can build the infrastructure from scratch.

Method 2: Using Hevo Data to Set up Twitter Ads to BigQuery Integration

Hevo Data provides a hassle-free solution and helps you directly transfer data from Twitter Ads to BigQuery and numerous other Databases/Data Warehouses or destinations of your choice without any intervention in an effortless manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Hevo’s pre-built integration with Twitter Ads along with 100+ other data sources (including 30+ free data sources like Twitter Ads) will take full charge of the data transfer process, allowing you to focus on key business activities.

GET STARTED WITH HEVO FOR FREE

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: 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

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. You can read more about bq commands here

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. 

Method 2: Using Hevo Data to Set up Twitter Ads to BigQuery Integration

Hevo Data Logo.
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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from Twitter Ads (a Free Data Source) and 100+ other data sources to Data Warehouses such as BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.

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. 

  • Authenticate Data Source: Link and authenticate your Twitter Ads account as data source with Hevo.
Authentication of Twitter Ads as Data Source.
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To get more details about Authenticating Twitter Ads with Hevo Data visit here.

  • Configure Destination: Connect the BigQuery Data Warehouse to load data.
Configuration of Google BigQuery as destination.
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To get more details about Configuring BigQuery with Hevo Data visit this link.

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.

Simplify your Data Migration with Hevo today! 

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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.

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Now that you have learned the two popular methods of loading data from Twitter Ads to BigQuery, which is your preferred method? Let us know in the comments.

Eva Brooke
Freelance Technical Content Writer, Hevo Data

Eva loves learning about data science, with an intense passion for writing on data, software architecture, and related topics. She enjoys creating an impact through content tailored for data teams, aimed at resolving intricate business problems.

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