Summarize
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
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
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."
Begin by logging into your Mailchimp account. Navigate to the 'Audience' section and select the audience you wish to export. Use the 'Export Audience' option to download a CSV file of your audience data. Ensure that you also export any relevant campaign, subscriber, and analytics data that you might need.
Once you have your data exported as a CSV file, review it to ensure it includes all necessary fields and is free from errors or anomalies. You may need to clean the data or reformat certain fields to ensure compatibility with the Databricks Lakehouse environment.
Log in to your Databricks account and set up a new workspace if necessary. Ensure that you have access to a Databricks Lakehouse environment with the requisite permissions to create tables and upload data.
Use the Databricks UI or CLI to upload your CSV files to the Databricks File System. Navigate to the 'Data' tab in your workspace, select 'Add Data', and upload the CSV file from your local machine. This step stores your data in a location accessible by your Databricks notebooks.
Open a new notebook in your Databricks workspace. Use Spark SQL or PySpark to create a table schema that matches the structure of your Mailchimp data. For instance, you can use the `CREATE TABLE` SQL command to define the table structure and specify the location of your CSV file in DBFS.
With the table structure in place, load the CSV data from DBFS into your newly created table. Use Spark SQL commands like `COPY INTO` or PySpark functions to read the CSV data and insert it into the table. This step involves parsing the CSV and ensuring data types are correctly mapped.
Once the data is loaded, run queries to verify that the data in Databricks matches the original data from Mailchimp. Check for any discrepancies in record count, field values, and overall data integrity. Perform any necessary data transformations or cleaning to ensure the data is ready for analysis or further processing within the Databricks Lakehouse.
This guide covers the essential steps to manually transfer data from Mailchimp to a Databricks Lakehouse environment, providing you with the flexibility to manage the data integration process directly.
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.
Mailchimp is a global marketing automation platform aimed at small to medium-sized businesses. Mailchimp provides essential marketing tools for growing a successful business, enabling businesses to automate messages and send marketing emails, create targeted business campaigns, expedite analytics and reporting, and effectively and efficiently sell online.
Mailchimp's API provides access to a wide range of data related to email marketing campaigns. The following are the categories of data that can be accessed through Mailchimp's API:
1. Lists: Information about the email lists, including the number of subscribers, the date of creation, and the list name.
2. Campaigns: Data related to email campaigns, including the campaign name, the number of recipients, the open rate, click-through rate, and bounce rate.
3. Subscribers: Information about the subscribers, including their email address, name, location, and subscription status.
4. Reports: Detailed reports on the performance of email campaigns, including open rates, click-through rates, and bounce rates.
5. Templates: Access to email templates that can be used to create new campaigns.
6. Automation: Data related to automated email campaigns, including the number of subscribers, the date of creation, and the automation name.
7. Tags: Information about tags that can be used to categorize subscribers and campaigns.
Overall, Mailchimp's API provides a comprehensive set of data that can be used to analyze and optimize email marketing campaigns.
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:
