This page provides you with instructions on how to extract data from AdWords and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Google AdWords?
Google AdWords is a popular paid marketing tool. With AdWords, you set a budget, select keywords, and publish ads that appear on Google search results pages relevant to your keywords. AdWords collects data about campaigns that businesses can use to measure their effectiveness.
What is Snowflake?
Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.
Getting data out of AdWords
Google provides a SOAP API for AdWords. The first step of getting your AdWords data into your data warehouse is pulling the data off of Google's servers by using the AdWords API's Reporting features. This is a subset of the API's functionality, which also includes the ability to manage ads.
You can also link your Google Analytics and Google AdWords accounts to allow the data to cross-pollinate. This can provide richer reporting due to the breadth of knowledge that exists in Google Analytics about the people who may have viewed or clicked your ads.
You can extract granular data from AdWords API reports, allowing you to see things like impressions, clickthrough rates, and CPC broken out by time period.
Preparing data for Snowflake
Depending on your data structures, you may need to prepare your data before loading. Check the supported data types for Snowflake and make sure that your data maps neatly to them.
Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.
Loading data into Snowflake
Snowflake's Data Loading Overview documentation can help you with loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are that that tool's limitations will make it unsuitable as a reliable ETL solution. Another approach involves two steps for getting data into Snowflake:
- Use the PUT command to stage files.
- Use the COPY INTO table command to load prepared data into an awaiting table.
You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.
Keeping AdWords data up to date
So, now what? You've built a script that pulls data from AdWords and loads it into your data warehouse, but what happens tomorrow when you have thousands of new impressions?
The key is to build your script in such a way that it can also identify incremental updates to your data. If you can identify some fields that auto-increment, you could use them to give your script the ability to recognize new data. You can then set your script up as a cron job or continuous loop to keep pulling down new data as it appears.
Other data warehouse options
Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Panoply.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your AdWords data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.