How to load data from Amplitude to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Amplitude 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 Amplitude
Begin by exporting the data from Amplitude. Log into Amplitude and navigate to the "Data" section. Select the dataset you wish to export. Use Amplitude's built-in export functionality to download the data in a format like CSV or JSON. This will typically involve selecting the desired date range and metrics, then downloading the data to your local machine.
Step 2: Prepare Data for Transformation
Once the data is exported, prepare it for transformation. Check the data for any inconsistencies or errors, such as missing values or incorrect data types. Clean and pre-process the data as necessary. This step ensures that the data is ready for transformation and ingestion into the Databricks Lakehouse.
Step 3: Set Up Databricks Environment
If you haven't already, set up your Databricks environment. This involves creating a Databricks account and a new workspace if needed. Make sure you have the necessary permissions to create and manage clusters, as well as to import data into the Lakehouse.
Step 4: Upload Data to Databricks File System (DBFS)
Use the Databricks web interface or Databricks CLI to upload the exported Amplitude data to the Databricks File System (DBFS). You can use the Databricks CLI command `databricks fs cp local-file-path dbfs:/path/to/destination` to copy the data file(s) from your local machine to the DBFS.
Step 5: Create a Databricks Table
In the Databricks environment, create a new table that will hold the Amplitude data. Use a notebook to write a Spark SQL command or PySpark script to define the schema of the table based on the structure of your exported data. For example, if your data is in CSV format, you can use the `spark.read.csv` method to create a DataFrame and then write it as a table.
Step 6: Load Data into the Table
Load the data from DBFS into the newly created table. Use a Databricks notebook to execute a script that reads the data from DBFS into a DataFrame, and then writes the DataFrame to the table using Spark SQL or PySpark. For example, you might use a command like `df.write.format("delta").saveAsTable("amplitude_data")` to save the DataFrame as a Delta table.
Step 7: Verify Data Integrity and Perform Analysis
After loading the data, verify its integrity by running basic queries to ensure the data has been correctly imported and that all columns and records are present. Once verified, you can proceed to perform data analysis using Databricks' powerful analytics tools and SQL capabilities. This might include generating reports or visualizations based on your imported Amplitude data.
By following these steps, you can efficiently move data from Amplitude to the Databricks Lakehouse without relying on third-party connectors or integrations.