Criteo to BigQuery: 2 Easy Methods

• July 23rd, 2022

Criteo to BigQuery - Featured Image

To increase sales, boost conversions & enhance brand awareness, reaching out to the right audience with a personalized message at the right time is a challenging task. To ensure that your Ad Campaigns bring in the max ROI, several eCommerce businesses across the globe leverage online advertising tools like Criteo.

Combining your Ads data with CRM & sales data available in your data warehouse like Google BigQuery allows you to get a complete picture of your business health. You can easily replicate data from Criteo to BigQuery manually using CSV files or automate the process via third-party tools.

In this article, you will learn how to effectively connect Criteo to BigQuery using 2 different methods.

Table of Contents

What is Criteo?

Criteo to BigQuery - Criteo Logo
Image Source

Criteo is a dynamic retargeting firm that creates personalized ads for consumers on behalf of several eCommerce companies. According to your browsing history, Criteo works closely with your Internet Service Providers to design & display online ads. For instance, when you click on a particular ad, visit a website, or view a particular product, you may see that your upcoming ads relate to your earlier online activity. Using Criteo, you can easily create and publish static, video, and mobile app ads to meet different business goals, including increased traffic, sales, & app installs.

Key Features of Criteo

  • Campaign Performance Reporting: With Real-time Tracking, Criteo allows you to monitor your campaign performance from a single reporting dashboard and the ability to optimize your Ad strategy within the same environment.  
  • Personalized Messaging: You can leverage Criteo’s Audience Match feature that targets the perfect audience & matches with the perfect custom ad.
  • Data Security: With Criteo’s First Party Media Network, connect, enrich, and activate your first-party data in a privacy safe, and compliant manner.
  • Wider Audience Reach: Boost engagement with always-on campaigns. Criteo allows you to reach out to multiple segments of audiences across display, video, mobile, CTV, and cookie-less environments.

Reliably integrate data with Hevo’s Fully Automated No Code Data Pipeline

If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc can change, creating and managing these pipelines can be a soul-sucking exercise.

Hevo’s no-code data pipeline platform lets you connect over 150+ sources like Criteo in a matter of minutes to deliver data in near real-time to your Google BigQuery warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes. 

All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software in terms of user reviews.

Take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What is Google BigQuery?

Criteo to BigQuery - BigQuery Logo
Image Source

Launched in 2010, BigQuery is a Cloud-Based Data Warehouse service offered by Google. It is built to handle petabytes of data and can automatically scale as your business flourishes. Developers at Google have designed its architecture to keep the storage and computing resources separate. This makes querying more fluid as you can scale them independently without sacrificing performance.

Since there is no physical infrastructure present similar to the conventional server rooms for you to manage and maintain, you can focus all your workforce and effort on important business goals. Using standard SQL, you can accurately analyze your data and execute complex queries from multiple users simultaneously.

Key Features of Google BigQuery

Google BigQuery has continuously evolved over the years and is offering some of the most intuitive features :

  • User Friendly: With just a few clicks, you can start storing and analyzing your data in Big Query. An easy-to-understand interface with simple instructions at every step allows you to set up your cloud data warehouse quickly as you don’t need to deploy clusters, set your storage size, or compression and encryption settings.    
  • On-Demand Storage Scaling: With ever-growing data needs, you can rest assured that it will scale automatically when required. Based on Colossus (Google Global Storage System), it stores data in a columnar format with the ability to directly work on the compressed data without decompressing the files on the go.
  • Real-Time Analytics: Stay updated with real-time data transfer and accelerated analytics as BigQuery optimally allocates any number of resources to provide the best performance and provide results so that you can generate business reports when requested.
  • BigQuery ML: Armed with machine learning capabilities, you can effectively design and build data models using existing SQL Commands. This eliminates the need for technical know-how of machine learning and empowers your data analysts to directly evaluate ML models.
  • Optimization Tools: To boost your query performance, Google provides BigQuery partitioning and clustering features for faster results. You also change the default datasets and table’s expiration settings for optimal storage costs and usage.   
  • Secure: BigQuery allows administrators to set access permissions to the data by groups and individuals. You can also enable row-level security for access to certain rows of a dataset. Data is encrypted before being written on the disk as well as during the transit phase. It also allows you to manage the encryption keys for your data.
  • Google Environment: Maintained and managed by Google, BigQuery enjoys easy and fluid integrations with various applications present in the Google Ecosystem. With little to no friction at all, you can connect BigQuery to Google Sheets and Google Data Studio for further analysis.

Why Connect Criteo to BigQuery?

Setting up the Criteo to BigQuery integration allows you to reap the following benefits:

  • With your product usage, CRM, sales, support & Ads data combined, you can generate a complete and accurate customer profile. 
  • Leveraging BigQuery’s best-in-class query performance & On-Demand scalability, you can accelerate your reporting and get real-time insights into your marketing efforts by setting up the Criteo BigQuery migration.
  • You can easily connect BigQuery to Google Data Studio and start visualizing your data.   

How to Connect Criteo to BigQuery?

To set up the Criteo BigQuery connector, you can follow any of the 2 methods given below: 

Method 1: Automate Criteo to BigQuery Connection using Hevo

Criteo to BigQuery - Hevo Logo
Image Source

Hevo is a No-code Data Pipeline solution that can help you seamlessly replicate data in real-time from 150+ data sources(Including 40+ free sources like Criteo) to your Data Warehouses such as Google BigQuery 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 150+ data sources such as Criteo 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.

Using manual scripts and custom code to move data into the warehouse is cumbersome. Changing API endpoints and limits, ad-hoc data preparation, and inconsistent schema make maintaining such a system a nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.

  • 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, and SOC-2.
Sign up here for a 14-day free trial!

Without the need for manually extracting CSV files & uploading them to BigQuery, you can effortlessly replicate data from Critieo to BigQuery using Hevo by following the simple steps given below:

  • Step 1: To replicate data from Criteo to BigQuery, you can first configure Criteo as a source by providing your Criteo details such as your ClientID and Client Secret, etc. You will also need to give the timezone, currency, historical sync duration, and a unique name for this Pipeline. 
Criteo to BigQuery - Criteo as a source
Image Source
  • Step 2: For completing the process to replicate data from Criteo to BigQuery, you can start by providing your BigQuery details such as Project ID & Dataset ID after connecting to BigQuery using a Service or User Account. You will also need to give a unique name for this destination. 
Criteo to BigQuery - BigQuery Destination
Image Source

This completes the No-Code & Automated method of connecting Criteo to BigQuery using Hevo.

Method 2: Manually Connect Criteo to BigQuery using CSV Files

To get started with the Criteo to BigQuery Migration, follow these simple steps:

  • Step 1: Criteo allows you to export your reports in CSV, Excel & PDF formats. You can navigate to the report you like and click on the Export icon present in the top right corner to download Criteo data. 
Criteo to BigQuery - Criteo Export Data button
Image Source
  • Step 2: You can start uploading your CSV files from Criteo to BigQuery. If your file size is less than 10 MB, then you can directly load CSV files from your local system to BigQuery. Otherwise, you need to first upload your files to a bucket in Google Cloud Storage and from there you can send them to your BigQuery account. For that, go to your Cloud Storage Browser page and navigate to the bucket where you want to upload your CSV files. Click on the Upload Files button, select the files & click on the OK button.
  • Step 3: Now navigate to your BigQuery page in the Google Cloud Console. In the Explorer pane on the left, expand it and click on the dataset in which you want to upload your CSV files from Criteo to BigQuery.
  • Step 4: Click on the + Create Table option in the Dataset info section. In the source section, select Upload from the drop-down menu for loading your files from your local system or choose Google Cloud Storage for files more than 10 MB. 
Criteo to BigQuery - Create Table option
Image Source
  • Step 5: Click on the Browse button to enter the location of your file on your system or the Google Storage bucket. Also, select CSV as the File format.
Criteo to BigQuery - Upload Option
Image Source
  • Step 6: Select your destination dataset & enter a unique name for your new table. Also, check if the Table type field is set to Native table. 
Criteo to BigQuery - Table destination
Image Source
  • Step 7: Click on the Auto Detect Schema option to allow BigQuery to automatically read & prepare the schema for you in your new table. You can also manually add fields to customize the schema according to your needs.
  • Step 8: To complete connecting Criteo to BigQuery, click on the Create Table button.     
Criteo to BigQuery - Auto Detect Schema
Image Source

Conclusion

In this article, you have learned how to effectively connect Criteo to BigQuery using 2 different methods. Since there is no manual Criteo to BigQuery Connector, you can download your data from Criteo as a CSV file and upload it into your BigQuery account. This approach is good if you rarely need to replicate data from Criteo to BigQuery. However, if you need to frequently replicate data in real-time with complex transformations, then Hevo is the right choice for you!    

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline can replicate Data in Real-Time from a vast sea of 150+ sources like Criteo to a Data Warehouse like BigQuery or a Destination of your choice. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!  

If you are using Google BigQuery as your Data Warehousing & Analytics platform and searching for a seamless alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo’s strong integration with 150+ Connectors (Including 40+ Free Sources like Criteo), allows you to export, load, transform & enrich your data to make it analysis-ready.

Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of setting up Criteo BigQuery Integration! Let us know in the comments section below!

No-code Data Pipeline for Google BigQuery