How to load data from Bing Ads to Snowflake
Access your data on Bing Ads
The first step in loading your Bing Ads data to any data warehouse solution is to access your data and start extracting it.
Bing Ads has a very rich API (here) that is primarily offered to interact with the platform and create and run campaigns programmatically. A big part of it is also a rich reporting system that helps you understand how you 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 you will have to respect the 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 any data you get based on specific accounts.
Transform and prepare your Bing Ads data for Snowflake Replication
After you have accessed your data on Bing Ads, you will have to transform it based on two main factors,
1. The limitations of the database that is going to be used
2. 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 you want to push data into Google BigQuery, 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.
When 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 into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send any 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. Every data type you have to map is coming from XML and might have automatically been transformed into the primitive data types of the language 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 identify somehow what and how to map to a table into your database.
Data in Snowflake is organized around tables with a well-defined set of columns, with each one having a specific data type.
Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types is also supported. With Snowflake, is possible to load directly data in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.
There is also one notable common data type that Snowflake does not support. LOB or large object data type is not supported. Instead, you should use a BINARY or VARCHAR type. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from Bing Ads to Snowflake is to create a schema where you will map each endpoint to a table.
Each key inside the Bing Ads endpoint responses should be mapped to a column of that table, and you should ensure the right conversion to a Snowflake data type.
You will need to ensure that as the Bing Ads API’s data types might change, you will adapt your database tables accordingly; there’s no such thing as automatic data typecasting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.
Export data from Bing Ads to Snowflake
Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing data, usually in JSON format, are stored in a local file system or in Amazon S3 buckets. Then a COPY INTO command is invoked on the Snowflake instance, and data is copied into a data warehouse.
The files can be pushed into Snowflake using the PUT command into a staging environment before the COPY command is invoked.
Another alternative is to upload the data directly into a service like Amazon S3 from where Snowflake can access data directly.
If you are looking into other data warehouses, you may check our how to’s on Bing Ads to Redshift, Bing Ads to BigQuery, Bing Ads to MS SQL Server, Bing Ads to PostgreSQL.
Updating your Bing Ads data on Snowflake
As you will be generating more data on Bing Ads, you will need to update your older data on Snowflake. This includes new records together with updates to older records that for any reason have been updated on Bing Ads.
You will need to periodically check Bing Ads for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a Snowflake table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Bing Ads does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of data that is inserted into your database is a big and difficult issue.
The best way to load data from Bing Ads to Snowflake
So far we just scraped the surface of what you can do with Snowflake and how to ingest data into it. Things can get even more complicated if you want to integrate data coming from different sources.
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