Databases
Marketing Analytics

How to load data from Yandex Metrica to MS SQL Server

Learn how to use Airbyte to synchronize your Yandex Metrica data into MS SQL Server within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Yandex Metrica as a source connector (using Auth, or usually an API key)
  2. set up MS SQL Server as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Yandex Metrica

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.

What is MS SQL Server

Microsoft SQL Server is a relational database management (RDBMS) built by Microsoft. As a database server, its primary function is to store and retrieve data upon the request of other software applications, either from the same computer or a different computer across a network—including the internet. To serve the needs of different audiences and workload sizes, Microsoft offers multiple editions (at least 12) of its Microsoft SQL Server.

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Prerequisites

  1. A Yandex Metrica account to transfer your customer data automatically from.
  2. A MS SQL Server account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Yandex Metrica and MS SQL Server, for seamless data migration.

When using Airbyte to move data from Yandex Metrica to MS SQL Server, it extracts data from Yandex Metrica using the source connector, converts it into a format MS SQL Server can ingest using the provided schema, and then loads it into MS SQL Server via the destination connector. This allows businesses to leverage their Yandex Metrica data for advanced analytics and insights within MS SQL Server, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Yandex Metrica as a source connector

1. First, you need to have a Yandex Metrica account and access to the API key. If you don't have one, you can create it by following the instructions on the Yandex Metrica website.
2. Once you have the API key, go to the Airbyte dashboard and click on "Sources" on the left-hand side menu.
3. Click on the "Create a new source" button and select "Yandex Metrica" from the list of available connectors.
4. Enter a name for your source and click on "Next".
5. In the "Connection Configuration" section, enter your Yandex Metrica API key in the "API Key" field.
6. Select the desired data range for your source in the "Date Range" field.
7. Choose the metrics and dimensions you want to include in your source by selecting them from the dropdown menus in the "Metrics" and "Dimensions" sections.
8. Click on "Test" to verify that the connection is working properly.
9. If the test is successful, click on "Create" to save your Yandex Metrica source connector on Airbyte.

Step 2: Set up MS SQL Server as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "MSSQL - SQL Server" connector and click on it.
3. Click on the "Create new destination" button.
4. Fill in the required information, including the destination name, host, port, database name, username, and password.
5. Click on the "Test connection" button to ensure that the connection is successful.
6. Once the connection is successful, click on the "Save" button to save the destination.
7. Navigate to the "Sources" tab on the left-hand side of the screen and select the source that you want to connect to the MSSQL - SQL Server destination.
8. Click on the "Create new connection" button.
9. Select the MSSQL - SQL Server destination that you just created from the drop-down menu.
10. Fill in the required information for the source, including the source name, host, port, database name, username, and password.
11. Click on the "Test connection" button to ensure that the connection is successful.
12. Once the connection is successful, click on the "Save" button to save the connection.13. You can now start syncing data from your source to your MSSQL - SQL Server destination.

Step 3: Set up a connection to sync your Yandex Metrica data to MS SQL Server

Once you've successfully connected Yandex Metrica as a data source and MS SQL Server as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Yandex Metrica from the dropdown list of your configured sources.
  3. Select your destination: Choose MS SQL Server from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Yandex Metrica objects you want to import data from towards MS SQL Server. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Yandex Metrica to MS SQL Server according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your MS SQL Server data warehouse is always up-to-date with your Yandex Metrica data.

Use Cases to transfer your Yandex Metrica data to MS SQL Server

Integrating data from Yandex Metrica to MS SQL Server provides several benefits. Here are a few use cases:

  1. Advanced Analytics: MS SQL Server’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Yandex Metrica data, extracting insights that wouldn't be possible within Yandex Metrica alone.
  2. Data Consolidation: If you're using multiple other sources along with Yandex Metrica, syncing to MS SQL Server allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Yandex Metrica has limits on historical data. Syncing data to MS SQL Server allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: MS SQL Server provides robust data security features. Syncing Yandex Metrica data to MS SQL Server ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: MS SQL Server can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Yandex Metrica data.
  6. Data Science and Machine Learning: By having Yandex Metrica data in MS SQL Server, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Yandex Metrica provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to MS SQL Server, providing more advanced business intelligence options. If you have a Yandex Metrica table that needs to be converted to a MS SQL Server table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Yandex Metrica account as an Airbyte data source connector.
  2. Configure MS SQL Server as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Yandex Metrica to MS SQL Server after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
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What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Frequently Asked Questions

What data can you extract from Yandex Metrica?

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 data can you transfer to MS SQL Server?

You can transfer a wide variety of data to MS SQL Server. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Yandex Metrica to MS SQL Server?

The most prominent ETL tools to transfer data from Yandex Metrica to MS SQL Server include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Yandex Metrica and various sources (APIs, databases, and more), transforming it efficiently, and loading it into MS SQL Server and other databases, data warehouses and data lakes, enhancing data management capabilities.