By Rudderstack Team

How to load data from the Bing Ads to Redshift

This post will help you with syncing your Bing Ads data to Amazon Redshift. By doing this you will be able to perform advanced analytics on a system that is designed for this kind of data payloads, like Amazon Redshift. Alternatively, you can simplify the process of syncing data from Bing Ads to Amazon Redshift by using RudderStack, where the whole process will be handled by RudderStack and you can focus on what matters, the analysis of your marketing spending and performance data.

Access your data on Bing Ads

The first step in loading your Bing Ads data to any kind of data warehouse solution is to access your data and start extracting it. Bing Ads has a very rich API that is primarily offered for interacting with the platform and to create and run campaigns programmatically. A big part of the API is also a rich reporting system that helps you understand how the campaigns you run perform. By default, Bing Ads exposes a large number of different reports, the current number of available reports based on the latest documentation is 35. Reports can have different types of aggregation defined by you. The default is set to “Summary” which means that you will end up with a report for the whole time of your account. For analytic purposes, it is advised to set the aggregation to a daily basis. This is also the lowest time granularity that you can have on Bing Ads. The Bing Ads API is implemented using the SOAP protocol, this will add some complexity to your development as you will have to manage SOAP and XML responses. Also, keep in mind that as in every other API, you will have to respect the API usage limits and make sure that you handle errors correctly. Reports in Bing Ads are configured by selecting the following:

  1. Columns, the columns that you want to be included in the report
  2. Filters, you should provide custom filters to filter the report data
  3. Scope, to reduce the data you get based on specific accounts.

About Bing Ads

Bing Ads, formerly known as Microsoft AdCenter is a service from Microsoft that provides Search Engine Marketing based on the Bing and Yahoo! search engines. As of 2015, Bing Ads had a 33% market share in the United States, so it’s a channel that advertisers always consider in addition to Google AdWords. As with the rest of the competition, Bing Ads provides pay-per-click advertising, ads are displayed based on keywords on the search engine, where different customers bid on the price and they are getting charged when a user of the search engine is actually clicking on a promoted search result. Bing Ads is a rich advertisement platform that also offers an API that allows you to programmatically interact with it and also to pull out metrics and reports about the performance of your campaigns. In this post, we are interested mainly in accessing these reports through the API for further analysis.

Transform and prepare your Bing Ads data

After you have accessed your data on Bing Ads, you will have to transform it based on two main factors,

  • The limitations of the database that the data will be loaded onto
  • The type of analysis that you plan to perform

Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly, but keep in mind that in the case of a SOAP API like Bing Ads, you get XML responses. Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading it into the database. Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. With Bing Ads, you have two main additional sources of complexity. When it comes to data types you have to keep in mind that SOAP is using XML to describe the service and the data, so the data types that you have to map are coming from XML and might have automatically be transformed into the primitive data types of the language that you are using. Also, you have to consider that the reports you’ll get from Bing Ads are like CSV files in terms of their structure and you need to somehow identify what and how to map to a table into your database.

Transform and prepare your Bing Ads data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load your data into it, you will have to follow its data model which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource. Also, each attribute should adhere to the data types that are supported by Redshift. As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift. Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

Load your Bing Ads data into Amazon Redshift

To upload your data to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3. You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation. After you have created your bucket you can start sending your data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference. Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then