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Begin by logging into your Yandex Metrica account. Use the Yandex Metrica API to extract the necessary data. You will need to write custom scripts, likely in Python, to query the API and download the data in a format like CSV or JSON. Ensure you have the necessary API access and permissions, and refer to the Yandex Metrica API documentation for query structure.
Once you have extracted the data, examine it for any necessary transformation or cleaning. This step involves removing duplicates, handling missing values, and converting data types if necessary. You can use Python libraries like Pandas for data manipulation to ensure the data is in a suitable format for analysis and storage in AWS.
Log into the AWS Management Console and navigate to Amazon S3. Create a new S3 bucket where the Yandex Metrica data will be stored. Configure the bucket settings, including region selection and access permissions, to ensure security and compliance with your organizational policies.
Use the AWS CLI or AWS SDKs to upload your cleaned and transformed data files to the S3 bucket. The AWS CLI can be installed locally, and you can use the `aws s3 cp` command to transfer files from your local system to the S3 bucket. Ensure you have configured your AWS CLI with the correct credentials and permissions.
Set up AWS Glue to catalog the data stored in S3. In the AWS Management Console, create a new Glue Crawler pointing to your S3 bucket. Configure the crawler to infer the schema of your data and create a Glue Data Catalog. This will enable you to query the data using AWS Athena later.
With the data cataloged in AWS Glue, navigate to AWS Athena to set up querying. Ensure the database created by Glue is selected, and you can now write SQL queries to analyze the data directly from S3. Athena allows you to run queries without needing to move the data again.
To make the process efficient and scalable, consider writing a script or setting up a cron job that automates data extraction, transformation, and upload to S3 at regular intervals. Use AWS Lambda for serverless execution of scripts and AWS CloudWatch for scheduling and monitoring the execution of these tasks.
By following these steps, you can manually move data from Yandex Metrica to an AWS Data Lake without relying on third-party connectors or integrations.
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:
