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- Log in to your Google Ads account.
- Navigate to the Reports section.some text
- You can create custom reports if you need specific data or use predefined reports.
- Select the data you want to extract.some text
- Choose the metrics and dimensions that are relevant to your analysis.
- Export the report.some text
- Google Ads allows you to export data in various formats such as CSV, Excel, or Google Sheets.
- Clean and format the data.some text
- Open the exported file and make sure the data is clean (e.g., no missing values, correct data types).
- Ensure that the format of the data matches the schema you intend to use in Snowflake (e.g., dates in YYYY-MM-DD format).
- Save the cleaned data as a CSV file.some text
- CSV is a common format for data loading and is supported by Snowflake.
- Log in to your Snowflake account.
- Create a file stage.some text
- Use the CREATE STAGE command to create a stage for your data files. For example:
CREATE STAGE my_google_ads_stage
FILE_FORMAT = (TYPE = 'CSV' FIELD_DELIMITER = ',' SKIP_HEADER = 1);
- You can manually upload the CSV file through the Snowflake web interface, or use the PUT command in Snowflake to upload the file from your local machine to the stage you created.
- For the PUT command, you’ll need SnowSQL or Snowflake’s command-line tool.
PUT file://path_to_your_csv_file @my_google_ads_stage;
Create a table that matches the structure of the Google Ads data.
CREATE TABLE google_ads_data (
column1_name column1_datatype,
column2_name column2_datatype,
...
);
Load the data from the stage into the Snowflake table.
COPY INTO google_ads_data
FROM @my_google_ads_stage
FILE_FORMAT = (FORMAT_NAME = 'CSV');
- Check the loaded data.some text
- Execute a SELECT query to verify the data has been loaded correctly.
SELECT * FROM google_ads_data LIMIT 10;
- Look for any errors or warnings.some text
- If there are any issues with the data load, Snowflake will provide error logs that you can review and correct.
- Schedule the data extraction from Google Ads.some text
- Use Google Ads scripts or scheduled reports to automate data extraction.
- Automate the data upload to Snowflake.some text
- Write a script that uses SnowSQL to automate data staging and loading into Snowflake.
- Consider using cron jobs (Linux) or Task Scheduler (Windows) to schedule the script.
After successful data loading, clean up the staged files to save storage space.
REMOVE @my_google_ads_stage/pattern='*.csv';
- Monitor the data loading process.some text
- Regularly check for any issues or failures in the data loading process.
- Maintain the data pipeline.some text
- Update the process as necessary, for example, if Google Ads changes their reporting structure or if you need to modify the Snowflake table schema.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The Google Ads API is the modern programmatic interface to Google Ads and the next generation of the AdWords API and it is a paid online advertising platform offered by Google. Google Ads is a paid search channel. Google Ads is a key digital marketing tool for any business which is looking to get meaningful ad copy in front of its target audience. Google AdWords is a well known marketplace where companies pay to have their website ranked at the top of a search results page, based on keywords.
Google Ads API provides access to a wide range of data related to advertising campaigns, including:
- Campaigns: Information about the campaigns, such as name, status, budget, and targeting settings.
- Ad groups: Details about the ad groups, including name, status, and targeting criteria.
- Ads: Information about the ads, such as type, format, and performance metrics.
- Keywords: Data related to the keywords used in the campaigns, including search volume, competition, and performance metrics.
- Bidding: Details about the bidding strategies used in the campaigns, such as manual bidding or automated bidding.
- Conversions: Information about the conversions generated by the campaigns, including conversion rate, cost per conversion, and conversion tracking settings.
- Audience: Data related to the audience targeting used in the campaigns, such as demographics, interests, and behaviors.
- Location: Information about the geographic targeting used in the campaigns, including location targeting settings and performance metrics.
Overall, the Google Ads API provides a comprehensive set of data that can be used to optimize advertising campaigns and improve their performance.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: