How to load data from Yandex Metrica to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Yandex Metrica data into Databricks Lakehouse within minutes.
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Export Data from Yandex Metrica
Begin by logging into your Yandex Metrica account. Navigate to the "Reports" section and select the data you wish to export. Use the "Export" feature to download the data in a format suitable for transfer, such as CSV or TSV. Ensure the export captures all necessary data dimensions and metrics for your analysis.
Step 2: Prepare Data for Transfer
Once downloaded, inspect the exported files for consistency and completeness. Clean the data if necessary by handling missing values or correcting any formatting issues. This step ensures that the data is ready for ingestion into Databricks and prevents errors during the import process.
Step 3: Set Up Databricks Environment
Log into your Databricks account. If you do not already have a cluster running, create a new cluster by selecting a suitable instance type and configuration. Ensure the cluster has sufficient resources to handle your data processing needs. Upload the exported Yandex Metrica files to the Databricks environment, typically through the Databricks File System (DBFS).
Step 4: Upload Data to Databricks File System (DBFS)
Use the Databricks UI or the command-line interface to upload your prepared CSV/TSV files to DBFS. You can do this via the Databricks workspace by navigating to the "Data" section and selecting "Upload Data". Alternatively, use the Databricks CLI with the command `dbfs cp dbfs:/` to upload files programmatically.
Step 5: Create a Databricks Table from Uploaded Data
Once your data is in DBFS, use Databricks notebooks to create a table. Use the `CREATE TABLE` SQL statement or the `spark.read` method in PySpark to load and transform the data from the CSV/TSV files into a Databricks table. This involves specifying the schema and handling any data type conversions.
Step 6: Transform Data as Needed
Perform any necessary data transformations within Databricks. Use SQL or Spark operations to filter, aggregate, or join your data as required for your analysis. This step is crucial for tailoring the data to meet your specific analytical needs and ensuring it is in the correct format for downstream processing.
Step 7: Store Processed Data in Databricks Lakehouse
Finally, store the transformed data in the Databricks Lakehouse, ensuring that it is organized and accessible for analysis. Use Databricks Delta Lake to provide ACID transactions and enable efficient querying. Save the table as a Delta table with `df.write.format("delta").save("")`, ensuring your data is durable and optimized for future queries.
By following these steps, you can efficiently move and process data from Yandex Metrica to Databricks Lakehouse without relying on third-party connectors.