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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
1. Create a new Google Sheet where you want to import the MailerLite data.
2. Name the sheet appropriately to identify the data it will contain.
3. Define the columns based on the data you will import from MailerLite (e.g., Email, Name, Signup Date, etc.).
1. Log in to your MailerLite account.
2. Navigate to the 'Integrations' page.
3. Find the 'Developer API' section and click on the 'Use' button.
4. Copy the API key provided. This key will be used to authenticate API requests to MailerLite.
1. Go to the Google Developers Console.
2. Create a new project or select an existing one.
3. Enable the Google Sheets API for your project.
4. Go to 'Credentials' and create a new service account.
5. Download the JSON file with your service account credentials.
1. Choose a programming language and set up your development environment.
2. Install the necessary libraries for making HTTP requests and for interacting with the Google Sheets API (e.g., `requests` for Python, `googleapis` for JavaScript).
3. Write the script to fetch data from MailerLite using the API key:
- Construct the appropriate API endpoint URL for fetching subscribers or other data.
- Use the HTTP GET method to retrieve the data in JSON format.
4. Parse the JSON response to extract the data you need.
5. Use the Google Sheets API and service account credentials to authenticate and access the Google Sheet you set up earlier.
6. Format the extracted MailerLite data to match the Google Sheets columns.
7. Use the Google Sheets API to append the data to the sheet.
1. Run the script in a controlled environment to ensure it is working correctly.
2. Verify that the data is being fetched from MailerLite and accurately inserted into the Google Sheets document.
3. Check for any errors or issues and debug as necessary.
1. Depending on your needs, you may want to automate the script to run at regular intervals (e.g., daily, weekly).
2. You can schedule the script using a task scheduler like cron (for Linux/macOS) or Task Scheduler (for Windows).
3. Alternatively, you can deploy the script to a cloud function (e.g., AWS Lambda, Google Cloud Functions) and trigger it on a schedule.
1. Set up logging and error notifications to monitor the script's performance.
2. Regularly check the logs to ensure the script is running as expected.
3. Update the script as necessary if MailerLite or Google Sheets API changes.
Note: The above steps provide a general outline. The actual implementation details will depend on the programming language and tools you choose to use. Always ensure that you handle sensitive information such as API keys and service account credentials securely.
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.
MailerLite is an intuitive email marketing solution for people of all skill levels. Simplicity is the core principle behind our solutions. We provide drag-and-drop content editors, simplified subscriber management, and advanced automation that are easy to set up. MailerLite is a distributed team of over 130 people living and working in 40 countries. Our international team enables us to better serve our customers around the world.
MailerLite'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 MailerLite's API:
1. Subscribers: This category includes data related to subscribers such as their email address, name, location, and subscription status.
2. Campaigns: This category includes data related to email campaigns such as the subject line, content, delivery time, and open and click-through rates.
3. Lists: This category includes data related to email lists such as the name of the list, the number of subscribers, and the date the list was created.
4. Segments: This category includes data related to segments such as the name of the segment, the criteria used to create the segment, and the number of subscribers in the segment.
5. Automation: This category includes data related to automated email campaigns such as the trigger, content, and delivery time.
6. Forms: This category includes data related to forms such as the name of the form, the number of submissions, and the date the form was created.
7. Reports: This category includes data related to email campaign reports such as the number of opens, clicks, bounces, and unsubscribes.
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:
