Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
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.
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.
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.
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.
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.
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.
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.
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.
Amplitude is a cross-platform product intelligence solution that helps companies accelerate growth by leveraging customer data to build optimum product experiences. Advertised as the digital optimization system that “helps companies build better products,” it enables companies to make informed decisions by showing how a company’s digital products drive business. Amplitude employs a proprietary Amplitude Behavioral Graph to show customers the impact of various combinations of features and actions on business outcomes.
Amplitude's API provides access to a wide range of data related to user behavior and engagement on digital platforms. The following are the categories of data that can be accessed through Amplitude's API:
1. User data: This includes information about individual users such as their demographics, location, and device type.
2. Event data: This includes data related to user actions such as clicks, page views, and purchases.
3. Session data: This includes information about user sessions such as the duration of the session and the number of events that occurred during the session.
4. Funnel data: This includes data related to user behavior in a specific sequence of events, such as a checkout funnel.
5. Retention data: This includes data related to user retention, such as the percentage of users who return to the platform after a certain period of time.
6. Revenue data: This includes data related to revenue generated by the platform, such as the total revenue and revenue per user.
7. Cohort data: This includes data related to groups of users who share a common characteristic, such as the date they signed up for the platform.
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:
