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Start by exporting your data from Google Ads. Use the Google Ads interface to create a report containing the data you need. Once the report is generated, export it as a CSV file. This format is widely supported and will make it easier to manipulate the data later.
Before uploading, ensure that your CSV file is formatted correctly. Check for any errors or inconsistencies in the data. Ensure that all necessary fields are included and that there is no missing data. This can involve cleaning the data using tools like Excel or Google Sheets.
Log in to your Starburst Galaxy account and set up your environment if it’s not already configured. Ensure that you have the necessary permissions to create tables and upload data. Familiarize yourself with the interface and tools available within Starburst Galaxy.
In Starburst Galaxy, you’ll need to create a table to hold your Google Ads data. Write a SQL statement to define the table structure, ensuring that it matches the schema of your CSV file. For instance, use the appropriate data types for each column to ensure compatibility.
Use the Starburst Galaxy interface to upload your CSV file. Navigate to the data upload option, select your CSV file, and specify the target table you created. Follow any prompts to map CSV columns to table columns if necessary and start the upload process.
After the upload, it’s crucial to verify that the data has been transferred correctly. Run SQL queries within Starburst Galaxy to check the number of rows and key data points against your original CSV file. This step ensures there are no discrepancies or data loss.
Finally, to ensure optimal performance of queries, consider indexing your data. Analyze query patterns and add indexes on frequently queried columns. Additionally, optimize your table by reviewing and updating data types, and consider partitioning strategies if applicable to improve query performance.
This guide allows you to manually transfer data from Google Ads to Starburst Galaxy, providing a hands-on approach to ensure data accuracy and integrity.
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?
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