Summarize this article with:
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 Customer.io account and navigating to the data export section. Use the built-in tools to export your desired data in a CSV or JSON format. Ensure that you adhere to the data privacy policies and export only the necessary data that you need to analyze or store.
Once you've exported the data, perform any necessary cleaning and transformation using local scripts or command-line tools like Python or Bash. This step ensures the data is in a suitable format for AWS Datalake ingestion. Remove any unnecessary columns, standardize date formats, and ensure data consistency.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket in your desired AWS region. This bucket will serve as the storage location for your data before it is ingested into the AWS Datalake. Configure the bucket's permissions to ensure it is secure and only accessible by authorized users.
Use the AWS CLI or AWS Management Console to upload your cleaned and formatted data files to the S3 bucket you created. If using the CLI, the command `aws s3 cp /local/path/to/your/data s3://your-bucket-name/` can be used to transfer files. Ensure the file paths and bucket names are correctly specified.
In the AWS Management Console, navigate to AWS Glue. Set up a new Glue Crawler to catalog the data stored in your S3 bucket. This process involves configuring the Glue Crawler to scan the data files and automatically create a schema in the AWS Glue Data Catalog. Ensure you specify the correct IAM roles with the necessary permissions to access the S3 bucket.
Create an AWS Glue ETL (Extract, Transform, Load) job to process the cataloged data. Using Glue's built-in ETL capabilities, you can transform the data as needed, such as filtering, aggregating, or joining datasets. Write the transformed data back into a different S3 bucket or make it available for querying using AWS Athena.
Finally, integrate the processed data into your AWS Datalake. Depending on your setup, this could involve using AWS Lake Formation to manage and secure your Datalake environment. Use AWS Lake Formation to grant the necessary permissions and ensure data governance policies are adhered to. Now, your data is ready for analysis and exploration within the AWS ecosystem.
By following these steps, you can successfully move data from Customer.io to AWS Datalake 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.
Salesloft is a comprehensive sales engagement platform designed to help sales teams streamline their prospecting, communication, and pipeline management processes. It provides a centralized hub for sales professionals to execute targeted outreach campaigns, track email opens and clicks, schedule meetings, and manage their sales cadences. One of its key strengths is its ability to integrate with various other tools, amplifying its capabilities. Salesloft can connect with popular CRM systems like Salesforce, HubSpot, and Microsoft Dynamics, enabling seamless data synchronization and centralized contact management.
Customer.io's API provides access to a wide range of data related to customer behavior and interactions with a business. The following are the categories of data that can be accessed through the API:
1. Customer data: This includes information about individual customers, such as their name, email address, and other demographic information.
2. Behavioral data: This includes data related to how customers interact with a business, such as their website activity, email opens and clicks, and other engagement metrics.
3. Campaign data: This includes data related to specific marketing campaigns, such as the number of emails sent, open rates, click-through rates, and conversion rates.
4. Segmentation data: This includes data related to how customers are segmented based on various criteria, such as their behavior, demographics, and interests.
5. A/B testing data: This includes data related to A/B tests conducted on various marketing campaigns, such as the performance of different subject lines, email content, and calls to action.
6. Revenue data: This includes data related to the revenue generated by specific campaigns or customer segments, as well as overall revenue trends over time.
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:
