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Begin by exporting the required data from Yandex Metrica. Log into your Yandex Metrica account, navigate to the relevant counter, and select the "Reports" section. Customize the report to include the metrics and dimensions you need. Once configured, export the data in CSV format using the "Export" option.
To load data into BigQuery, you'll first need to store the CSV file in Google Cloud Storage (GCS). If you haven't already, create a Google Cloud project and set up a GCS bucket. Ensure you have the necessary permissions to upload files to this bucket.
Use the Google Cloud Console, `gsutil` command-line tool, or the Google Cloud SDK to upload your CSV file to the designated GCS bucket. For example, using `gsutil`, you can run the command: `gsutil cp [local-file-path] gs://[bucket-name]/[file-name].csv`.
In the Google Cloud Console, navigate to BigQuery and create a dataset if you don't have one already. Within this dataset, define a table schema that matches the structure of your CSV data. You can do this manually by specifying each column's name and data type.
With the CSV file in GCS, use the BigQuery console or command-line tool to load data into your BigQuery table. In the BigQuery console, select "Create table", choose "Google Cloud Storage" as your source, and specify the path to your CSV file in GCS. Configure the schema, ensure the correct data format (CSV), and start the import process.
Once the data is loaded, verify its integrity by running basic SQL queries in BigQuery. Check for data completeness and accuracy by comparing sample data with the original Yandex Metrica report. Pay attention to data types and field parsing to ensure there are no discrepancies.
If you need to regularly import data, consider automating the process using Google Cloud's native tools. You can write a script using Google Cloud Functions or a cron job on a VM instance that uses `gsutil` and `bq` command-line tools to automate the export, upload, and import process at scheduled intervals.
By following these steps, you can efficiently transfer your data from Yandex Metrica to Google BigQuery without relying on third-party connectors.
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:
