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 understanding the data structure used by ConfigCat. Identify which data you need to transfer to AWS Data Lake. This typically includes feature flags, configurations, and any associated metadata. Document these data structures and the format they are stored in (e.g., JSON, XML).
Ensure you have an AWS account set up with the necessary permissions. Create an S3 bucket that will serve as the storage location for your data lake. Ensure that proper IAM roles and policies are configured to allow access to this bucket for data upload.
Manually export the data from ConfigCat using their API. Write a script in a language like Python to interact with ConfigCat's API to fetch the required data. Use HTTP requests to pull the data, and store it locally in a structured format like JSON.
Once exported, transform the data into a format suitable for AWS. If the data is already in JSON, minimal transformation might be needed. However, ensure that the data structure aligns with how you plan to query and analyze it in AWS. Consider converting to CSV if necessary for compatibility with AWS services like Athena.
Use AWS CLI or SDKs (like Boto3 for Python) to upload the transformed data to your S3 bucket. Ensure that you organize the data within the bucket using a logical folder structure that aligns with your data analysis needs.
Use AWS Glue to catalog your data. Create a Glue Crawler to automatically scan the data in your S3 bucket and create a metadata catalog. This will make the data queryable with AWS Athena. Configure the Crawler to run at regular intervals if you plan to update the data frequently.
Finally, use AWS Athena to query and analyze the data. Athena integrates directly with AWS Glue, allowing you to write SQL queries against your data in S3. Set up any necessary dashboards or reports using Amazon QuickSight for visualization, if needed.
By following these steps, you can effectively transfer and manage your ConfigCat data in an AWS Data Lake without relying on external 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.
ConfigCat is a hosted service for feature flag and configuration management. It lets you decouple feature releases from code deployments. Config Cat offers a beautiful easy to understand UI. It has complete a lot of features and more than just enabling and disabling flags. Configcat supplied us with the capability we required for both environment and user specific feature mapping. ConfigCat provides a Supervisor that must be attached to your applications supervision tree and an API for accessing your ConfigCat.
ConfigCat's API provides access to various types of data related to feature flags and configuration management. The following are the categories of data that can be accessed through the API:
- Feature flags: The API provides access to all the feature flags created in ConfigCat, including their name, description, and status (enabled or disabled).
- Configurations: The API allows access to the configurations associated with each feature flag, including their values and data types.
- Environments: The API provides access to the environments created in ConfigCat, including their name and description.
- User targeting: The API allows access to the user targeting rules associated with each feature flag, including their conditions and percentage rollout.
- Analytics: The API provides access to the analytics data related to feature flags, including the number of evaluations, impressions, and conversions.
- Integrations: The API allows access to the integrations configured in ConfigCat, including their name and status.
Overall, ConfigCat's API provides a comprehensive set of data related to feature flags and configuration management, enabling developers to easily manage and monitor their feature flags and configurations.
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:
