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 Appfollow account and navigate to the section from which you want to export data. Identify the available options for exporting data, such as CSV or Excel downloads. Appfollow typically allows data export for app reviews, app performance metrics, etc.
Use the export feature to download your required data in a suitable format, such as CSV. Ensure that the data is comprehensive and includes all necessary fields. Save this file to a local directory on your computer where you can easily access it for the next steps.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket by clicking on "Create Bucket." Name your bucket and choose the AWS region closest to your location. Configure any necessary permissions based on your needs, but remember that for sensitive data, it's important to keep the bucket private.
Download and install the AWS Command Line Interface (CLI) on your local machine if you haven't already. After installation, configure the AWS CLI by running `aws configure` in your terminal. You will need to provide your AWS Access Key ID, Secret Access Key, region, and preferred output format. Ensure these credentials have permission to access S3.
Ensure that your exported data file from Appfollow is ready for upload. Review the file to ensure it is in the correct format and contains no errors. Rename the file if necessary to reflect its contents clearly for easy identification once it’s uploaded to S3.
Open your terminal and navigate to the directory containing your exported data file. Use the AWS CLI to upload the file to your S3 bucket by executing the following command:
```
aws s3 cp your-data-file.csv s3://your-bucket-name/
```
Replace `your-data-file.csv` with the name of your exported file and `your-bucket-name` with the name of your S3 bucket. This command will transfer your data file from your local system to the specified S3 bucket.
Once the upload is complete, return to the AWS Management Console and navigate to your S3 bucket. Check that the file appears in the bucket. You can also use the AWS CLI to list the contents of your bucket to verify the presence of the uploaded file:
```
aws s3 ls s3://your-bucket-name/
```
Ensure that the data file is correctly listed with the expected size and timestamp, confirming a successful upload.
By following these steps, you can manually move data from Appfollow to Amazon S3 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.
Appfollow is a one-stop platform for app analytics, app reviews management, and app store optimization. Get reviews from the App Store, Google Play to monitor and analyse them. AppFollow is on a mission to help teams working on mobile apps to turn insights from reviews into new product experiences that users love. Mobile teams are responding to feedback in a timely manner, building products they know users will love, and optimizing their performance in the app stores with AppFollow.
Appfollow's API provides access to a wide range of data related to mobile apps and their performance. The following are the categories of data that can be accessed through Appfollow's API:
1. App Store Optimization (ASO) data: This includes data related to app store rankings, keyword rankings, and user reviews.
2. Competitor analysis data: This includes data related to competitor app rankings, keyword rankings, and user reviews.
3. User acquisition data: This includes data related to app installs, uninstall rates, and user retention rates.
4. App performance data: This includes data related to app crashes, bugs, and other performance issues.
5. Social media data: This includes data related to social media mentions and sentiment analysis.
6. Analytics data: This includes data related to app usage, user engagement, and user behavior.
7. Advertising data: This includes data related to app advertising campaigns, ad performance, and ad spend.
Overall, Appfollow's API provides a comprehensive set of data that can help app developers and marketers make informed decisions about their app's performance and user engagement.
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:
