Your business uses Google Ads heavily to acquire more customers and build your brand. Given the importance of this data, moving data from Google Ads to a robust Data Warehouse Redshift for advanced analytics is a step in the right direction. Google Ads is an Advertising Platform from Google that provides you the tools for launching Ad Campaigns, Product Listing, or Videos to your users. On the other hand, Amazon Redshift is a Cloud-based Data Warehousing solution from Amazon Web Services (AWS).
This blog will introduce you to Google Ads and Amazon Redshift. It will also discuss 2 approaches so that you can weigh your options and choose wisely while loading data from Google Ads to Redshift. The 1st method is completely manual and demands technical proficiency while the 2nd method uses Hevo Data.
Introduction to Google Ads
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Google Ads is an Online Advertising Platform that allows businesses to showcase highly personalized ads in various formats such as Text Ads, Video Ads, Image Ads. Advertising copy is placed on pages where Google Ads things are relevant. Businesses can choose to pay Google basis a flexible model (Pay Per Click or Pay for the advertisement shown).
Given the reach that Google has, this has become one of the most favorite advertising channels for modern Marketers.
For more information on Google Ads, click here.
Introduction to Amazon Redshift
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AWS Redshift is a Data Warehouse managed by Amazon Web Services (AWS). It is built using MPP (massively parallel processing) architecture and has the capacity to store large sets of data and perform advanced analytics. Designed to run complex analytical workloads in a cost-efficient fashion, Amazon Redshift has emerged to be a popular Cloud Data Warehouse choice for modern data teams.
For more information on Amazon Redshift, click here.
Methods to Load Data from Google Ads to Redshift
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Majorly there are 2 methods through which you can load your data from Google Ads to Redshift:
This section will discuss the above 2 approaches in detail. In the end, you will have a deep understanding of both and you will be able to make the right decision by weighing the pros and cons of each. Now, let’s walk through these methods one by one.
Method 1: Load Data from Google Ads to Redshift by Building ETL Scripts
This method includes Manual Integration between Google Ads and Redshift. It demands technical knowledge and experience in working with Google Ads and Redshift. Following are the steps to integrate and load data from Google Ads to Redshift:
Step 1: Extracting Data from Google Ads
Applications interact with the Google Ads platform using Google Ads API. The Google Ads API is implemented using SOAP (Simple Object Access Protocol) and doesn’t support RESTful implementation.
A number of different libraries are offered that could be used with many programming languages. The following languages and frameworks are officially supported.
- Python
- PHP
- JAVA
- .NET
- Ruby
- PERL
Google Ads API is quite complex and exposes many functionalities to the user. One can pull out a number of reports using Google Ads API. The granularity of the results you would need can also be specified by passing specific parameters. You can decide the data you want to get in 2 ways.
- By using an AWQL-based report definition
- By using XML-based report definition
Most Google Ads APIs are queried using AWQL which is similar to SQL. The following output formats are supported.
- CSV – Comma separated values format
- CSV FOR EXCEL – MS excel compatible format
- TSV – Tab separated value
- XML – Extensible markup language format
- GZIPPED-CSV – Compressed csv
- GZIPPED-XML – Compressed xml
You can read more about Data Extraction from Google Ads here.
Once you have the necessary data extracted from Google Ads, the next step would be to load it into Redshift.
Step 2: Loading Google Ads Data to Redshift
As a prerequisite, you will need to create a Redshift table and map the schema from the extracted Google Ads data. When mapping the schema, you should be careful to map each attribute to the right data types supported by Redshift. Redshift supports the following data types:
- INT
- SMALLINT
- BIGINT
- DECIMAL
- VARCHAR
- CHAR
- DATE
- TIMESTAMP
- REAL
- DOUBLE PRECISION
- BOOLEAN
Design a schema and map the data from the source. Follow the best practices published by Amazon when designing the Redshift database.
While Redshift allows us to directly insert data into its tables, this is not the most recommended approach. Avoid using the INSERT command as it loads the data row by row. This slows the process because Redshift is not optimized to load data in this way. Instead, load the data to Amazon S3 and use the copy command to load it to Redshift. This is very useful, especially when handling large volumes of data.
Limitations of Loading Data from Google Ads to Redshift Using Custom Code
- Accessing Google Ads Data in Real-time: After successfully creating a program that loads data from Google ads to the Redshift warehouse, you will be required to deal with the challenge of loading new and updated data. You may decide to replicate the data in real-time each time a new row or updated data is created. This process is slower and resource-intensive. Therefore, you will be required to write additional code and build cron jobs to run this in a continuous loop.
- Infrastructure Maintenance: Google ads may update their APIs or something may break at Redshift’s end unexpectedly. In order to save your business from irretrievable data loss, you will be required to constantly maintain the code and monitor the health of the infrastructure.
- Ability to Transform: The above approach only allows you to move data from Google Ads to Redshift as is. In case you are looking to clean/transform the data before loading to the warehouse – say you want to convert currencies or standardize time zones in which ads were run, this would not be possible using the previous approach.
Method 2: Load Data from Google Ads to Redshift using Hevo Data
Hevo Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources(including 40+ free sources) including Google Ads, etc., for free and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
Hevo can move data from Google Ads to Redshift seamlessly in 2 simple steps:
Step 1: Configuring the Source
- Navigate to the Asset Palette and click on Pipelines.
- Now, click on the +CREATE button and select Google Ads as the source for data migration.
- In the Configure your Google Ads page, click + ADD GOOGLE ADS ACCOUNT which will redirect you to the Google Ads login page.
- Login to your Google Ads account and click on Allow to authorize Hevo to access your Google Ads data.
- In the Configure your Google Ads Source page, fill all the required fields
Step 2: Configuring the Destination
- Once you have configured the source, it’s time to manage the destination. navigate to the Asset Palette and click on Destination.
- Click on the +CREATE button and select Amazon Redshift as the destination.
- In the Configure your Amazon Redshift Destination page, specify all the necessary details.
Hevo will now take care of all the heavy-weight lifting to move data from Google Ads to Redshift.
Get Started with Hevo for free
Advantages of Using Hevo
Listed below are the advantages of using Hevo Data over any other Data Pipeline platform:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Conclusion
The article introduced you to Google Ads and Amazon Redshift. It provided 2 methods that you can use for loading data from Google Ads to Redshift. The 1st method includes Manual Integration while the 2nd method uses Hevo Data.
With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, Hevo Data is the right choice for you! It will help simplify the Marketing Analysis. Hevo Data supports platforms like Google Ads, etc., for free.
Visit our Website to Explore Hevo
In order to do Advanced Data Analytics effectively, you will require to have reliable and updated Google Ads data.
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What are your thoughts on moving data from Google Ads to Redshift? Let us know in the comments.
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.