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Begin by logging into your Yandex Metrica account. Navigate to the "Reports" section and select the data you wish to export. Use the "Export" option to download the data in a suitable format, such as CSV or TSV, which can be easily managed and transferred.
Open the exported file in a spreadsheet application like Microsoft Excel or Google Sheets. Review the dataset to ensure it is clean and organized, removing any unnecessary columns or rows. Make sure the data types (e.g., dates, numbers) are consistent and properly formatted to prevent issues during the import process.
Access your Starburst Galaxy account or create one if you haven't already. Follow the prompts to set up a new environment. This typically involves specifying your region and any other settings pertinent to your data storage needs.
In your Starburst Galaxy environment, navigate to the SQL Editor or the Schema Management section. Use SQL commands to create a new schema that will house your Yandex Metrica data. Define the schema with appropriate tables and data types that match the structure of your exported data file.
Convert your cleaned data into a SQL-compatible format if necessary. This involves ensuring that your data types align with those defined in your Starburst Galaxy schema. Use tools or scripts to generate SQL `INSERT` statements from your CSV or TSV data, which can be executed in the Starburst Galaxy environment.
Access the SQL Editor in your Starburst Galaxy environment. Copy and paste your SQL `INSERT` statements into the editor, or use the command line interface if available, to execute the statements and load the data into the schema you created. Monitor the process for any errors or issues.
After the data upload is complete, execute SQL queries in Starburst Galaxy to verify that the data has been imported correctly. Check for consistency, accuracy, and completeness. Compare the imported data against the original dataset to ensure no data loss or corruption has occurred during the transfer process.
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.
Yandex Metrica assists you to get narrative reports and record the actions of personal users, to detect what people are seeking for on your site. It is a web analytics tool that you can easily use to collect data about visitors to your website and their sessions. One can easily use Yandex Metrica web analytics tool to get visual reports and video recordings of user actions and track traffic sources. Yandex Metrica is the best plugin for WordPress.
Yandex Metrica's API provides access to a wide range of data related to website and mobile app performance. The types of data that can be accessed through the API can be categorized as follows:
1. User behavior data:
- Pageviews
- Sessions
- Bounce rate
- Time on site
- Clicks
- Goals and conversions
2. Traffic sources data:
- Referral sources
- Search engine traffic
- Direct traffic
- Social media traffic
- Paid traffic
3. Audience data:
- Demographics
- Geolocation
- Device type
- Browser type
- Language
4. Technical data:
- Page load time
- Error messages
- Server response time
- Browser and device compatibility
5. Custom data:
- Custom events
- Custom dimensions
- Custom metrics
Overall, Yandex Metrica's API provides a comprehensive set of data that can be used to analyze and optimize website and mobile app 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:





