Amazon Advertising (Amazon Ads) was formerly known as Amazon Marketing Services. It was launched in 2018 as a search advertising solution for Amazon vendors. It is a service that works similarly to Google Ads, i.e., the Pay-Per-Click model, which means it will pay when the customer clicks on the Advertisements.
Google BigQuery is a cloud-based enterprise Data Warehouse that allows users to run SQL queries quickly and analyze large datasets interactively. BigQuery is a read-only data processing engine based on Google’s Dremel Technology.
This article explains how to connect Amazon Ads (Coming Soon For Hevo!) to BigQuery using various methods. It also gives an overview of BigQuery and Amazon Ads.
Why Integrate Amazon Ads to BigQuery?
A marketing tool called Amazon Ads produces pertinent data like impressions, user behavior, clicks, and product details. Connecting Amazon Ads to BigQuery helps solve many data problems.
A seller uses the following formula to determine his profits:
Profit/Losses= Sales- Expenses.
The cost includes money spent on advertising. This cost information will be accessible through Amazon Ads. The seller must retrieve sales information from Seller Central reports calculating sales. Additionally, costs for shipping, packaging, warehousing, and commissions will be incurred; these costs will be stored in other programs or databases.
The above formula is changed for Amazon sellers to read as follows:
Profits/Losses = (Seller Central Sales Data – Amazon Ads Advertising Costs).
Data on ad spend and campaign performance is included in Amazon Advertising Reports, which must be downloaded from the web UI. The equations must be processed in Excel after the Amazon product and sales data have been downloaded from Amazon Seller Central. Every day, on every channel, data analysts must run the reports and manually complete the calculations. These manual tasks cut down on the time and effort needed for crucial data analysis. A seller is thus compelled to restrict his response through better pricing, discounts, stopping pointless campaigns, or other trends.
Data analysts can automate important reporting tasks with a potent solution, freeing up their time for in-depth data analysis. They can automate the basic data extraction process from websites like Amazon Ads and Amazon Marketplaces. It is possible to load data from Amazon Ads to BigQuery. Decision-makers can assess the effects of their choices thanks to consolidated data, which can offer better insights with Amazon Ads to Snowflake Integration.
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.
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Methods to Integrate Amazon Ads to BigQuery
Method 1: Using Hevo to Set Up Amazon Ads to BigQuery
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Hevo provides an Automated No-code Data Pipeline that helps you move your Amazon Ads(Coming Soon for Hevo!) to BigQuery. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ data sources(including 40+ free sources)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 securely and consistently with zero data loss.
Amazon Ads is an upcoming source to connect Amazon Ads to BigQuery. Hevo supports a variety of Data Warehouses and Databases like Amazon Redshift, Snowflake, MySQL, Databricks, etc as sources.
Having said that, you can setup Google BigQuery as a destination with the following steps:
- Step 1: To set up Google BigQuery as a destination in Hevo, follow these steps:
- Step 1.1: In the Asset Palette, select DESTINATIONS.
- Step 1.2: In the Destinations List View, click + CREATE.
- Step 1.3: Select Google BigQuery from the Add Destination page.
- Step 1.4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.
- Step 1.5: Choose one of these:
- Using a Service Account to connect:
- Service Account Key file, please attach.
- Note that Hevo only accepts key files in JSON format.
- Go to CONFIGURE GOOGLE BigQuery ACCOUNT and click it.
- Using a user account to connect:
- To add a Google BigQuery account, click +.
- Become a user with BigQuery Admin and Storage Admin permissions by logging in.
- To grant Hevo access to your data, click Allow.
- Step 1.6: Set the following parameters on the Configure your Google BigQuery page:
- Destination Name: A unique name for your Destination.
- Project ID: The BigQuery Project ID that you were able to retrieve in Step 2 above and for which you had permitted the previous steps.
- Dataset ID: Name of the dataset that you want to sync your data to, as retrieved in Step 3 above.
- GCS Bucket: To upload files to BigQuery, they must first be staged in the cloud storage bucket that was retrieved in Step 4 above.
- Sanitize Table/Column Names: Activate this option to replace the spaces and non-alphanumeric characters in between the table and column names with underscores ( ). Name Sanitization is written.
- Step 1.7: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
- Step 1.8: Once the test is successful, click SAVE DESTINATION.
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 makes 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.
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- 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 you don’t face the pain of schema errors.
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Method 2: Using Custom Code to Move Data from Amazon Ads to BigQuery
You use an indirect method for Amazon Ads to BigQuery migration. First, you connect Amazon Ads to Snowflake and then Snowflake to BigQuery.
Amazon Ads to Snowflake
Here, Snowflake is a connector for Amazon Ads to BigQuery. You will need to download and then transfer the Amazon search Term Report to Snowflake for analysis to manually connect Amazon Ads to Snowflake. The Amazon Search Term Report contains actual customer information and details the exact searches customers are making to find your products on Amazon. The reports contain the precise keyword data for your products in their raw form. The report includes Keyword targeting, Keyword Match Type, Customer search term used, Sales and Conversion Rate for that term during a specified period, Clicks, CTR, Impressions, CPC and Spend.
To extract Amazon Ads Report, log in to your Amazon Seller Central, navigate to Seller Central Homepage and select Reports then select Advertising Reports. Select the Campaign Type, and period of the campaign, and select Create Report after that.
To Load the Report from Amazon Ads to BigQuery, it is important to have a well-defined schema of the data to ensure the data integrity is maintained. Data in Snowflake is well organized as Snowflake supports a rich set of data types. In Snowflake, it is possible to load the data directly in JSON, AVRO, Parquet, XML, and many other formats of data.
Check the files on the stage directory in Amazon Ads to Snowflake Integration for the Amazon Ads to Bigquery Integration. Once the data is copied into the stage folder, create a table in the Snowflake providing all the column names and their data types in Amazon Ads to Snowflake Migration. Once the data is loaded into Snowflake tables, you can build and execute the SQL queries to generate insights from the data in Amazon Ads to Snowflake Integration.
Snowflake to BigQuery
Before connecting Snowflake to BigQuery, it is important to understand a few parameters that make up this connection. Some of those parameters are:
Cloud Storage Environment
To connect Amazon Ads to Bigquery, it is important to set up the Cloud storage environment. You can rely on a cloud storage bucket to stage your data for initial loading and querying as an external source of data. If the location of the BigQuery dataset has been set to another value other than the United States, you should provide a regional or multi-regional cloud storage bucket in a similar region as the BigQuery instance. The architecture of Snowflake’s Cloud storage environment is given below.
Schema
Database schema plays an important role when you are connecting Amazon Ads to BigQuery.
When data is imported in bulk from a file such as CSV, JSON, or an Avro, BigQuery automatically detects the schema, hence, there is no need to predefine it. If you want to change the schema during migration, first migrate the schema as-is. BigQuery supports different data model design patterns like the Snowflake schema and the Star schema.
Note that BigQuery uses a case-sensitive naming convention while Snowflake supports a case-insensitive naming convention. This means that you must rectify any table-naming inconsistencies in Snowflake as well as those that arise during migration to BigQuery.
BigQuery does not support some schema modifications, hence, they will require some manual workarounds. Examples include changing a column name, changing column data type, deleting a column, and changing the column mode.
Supported Data Types, File Formats, and Properties
Snowflake and BigQuery support similar data types but sometimes use different names. To connect Amazon Ads to BigQuery, Snowflake can export data to BigQuery in three file formats namely CSV, JSON (newline-delimited), and Parquet. If you need a quick load time, choose Parquet.
Migrating Tools
There are different tools that you can use to migrate data from Snowflake to BigQuery. Examples include the COPY INTO command, BigQuery Data Transfer Service, gsutil, bq, cloud storage client libraries, BigQuery client libraries, BigQuery Query Scheduler, etc.
Migrating the Data
You can export your Snowflake data into a CSV, Parquet, or JSON file and load it into the cloud storage. You can then use the BigQuery Data Transfer Service to load the data from cloud storage into BigQuery.
You can build a pipeline that unloads data from Snowflake. The following steps can help you connect Snowflake to BigQuery:
Step 1: Unloading the Data from Snowflake
Unload the data from Snowflake into Cloud Storage. You can also use tools such as gsutil or the Cloud Storage client libraries to copy the data into Cloud Storage.
Step 2: Copy the Data onto BigQuery
Use one of the following ways to copy the data from the Cloud Storage into BigQuery:
- bq command-line tool.
- BigQuery Data Transfer Service.
- BigQuery client libraries.
What are Amazon Ads?
Amazon is one of the biggest markets. Due to how well-liked the retailer is, some online customers won’t even consider shopping anywhere else but Amazon.
Amazon’s dominant position and the fierce competition among Amazon sellers go hand in hand. As a result, Amazon Advertising is expanding, and sellers need to develop a strategic and adaptable marketing strategy that will yield the best ROI.
Amazon Advertising is growing at a rapid pace, and by having hundreds of millions of customers across the world, Amazon has an excellent understanding of how shoppers engage with Products and their behavior on product browsing and purchase.
Key Features Of Amazon Ads
- Digital Way to Reach Shoppers: With the rise of digitalization, there has been a significant increase in online shoppers. Amazon Ads allows sellers and businesses to interact more effectively and efficiently with shoppers.
- Quick Execution: Amazon Ads provide the ability to execute and optimize the campaigns faster than the traditional models. This results in a quick turnaround, and you have the opportunity to reach audiences at the right place and at the right time.
- Real-time Insights: Amazon Ads provides real-time insights on the advertisement that allows sellers to track real-time customer behavior and perform optimization. It will enable sellers to track results, see how the ads are performing and make changes to the campaign as you go.
What is Google BigQuery?
Google BigQuery is a highly scalable, serverless, multi-cloud Data Warehouse that uses a build-in query engine. It is a highly scalable serverless, fully-featured, fully manageable Data Warehouse that enables scalable analysis over petabytes of data. It is developed by Google and launched on 19th May 2010. It is designed such that it uses the processing power of Google’s infrastructure that makes a single SQL query to analyze petabytes of data in seconds.
BigQuery is also called SQL-based Data Warehouse as a Service (DWaaS) with zero infrastructure management. It is a serverless warehouse that does not require any upfront hardware provisioning or management. BigQuery runs SQL Queries and all requests are to be authenticated. Google provides a complete package to their users with Big Data loading features on Google Cloud Storage and connectivity with various Google apps like Apps Script. Google BigQuery has many built-in features like Machine Learning and AI Capabilities, Geospatial Analysis, and Business Intelligence.
Key Features of Google BigQuery
- Flexible Scaling: You don’t have to explicitly tweak the cluster with BigQuery since computing resources are automatically adjusted according to the workload, and it can easily extend storage to Petabytes on demand. Patching, updates, computing, and storage resource scaling are all handled by Google BigQuery, making it a fully managed service.
- Storage: Google BigQuery leverages Google’s global storage system, Colossus, to store and optimize your data for free and with no downtime. To store data, Google BigQuery uses the opinionated Capacitor format in Colossus, which achieves various enhancements behind the scenes while burning a large amount of CPU/RAM, all without affecting query performance or imposing a bill limit.
- Programming Access: Google BigQuery may be easily accessed in applications using Rest API queries, Client Libraries such as Java, .Net, Python, the Command-Line Tool, or the GCP Console. It also includes query and database management tools.
Conclusion
This article discusses the different methods to Connect Amazon Ads to BigQuery extensively. In addition to that, it also describes Amazon Ads and BigQuery briefly.
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This platform allows you to transfer data from 150+ Data sources (including 40+ Free Sources) such as Amazon Ads(Coming Soon For Hevo!) and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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Harshita is a data analysis enthusiast with a keen interest for data, software architecture, and writing technical content. Her passion towards contributing to the field drives her in creating in-depth articles on diverse topics related to the data industry.