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 exporting the data you need from AppFollow. Navigate to the relevant section of the AppFollow dashboard where your data resides, such as reviews, app metrics, or analytics. Use the export functionality provided by AppFollow to download the data in a common format like CSV or Excel. Ensure you have all necessary fields and rows required for your analysis.
Once you have exported the data, inspect it for any inconsistencies or unnecessary columns that you may want to exclude. Clean and format the data to match the schema of your TiDB database. This may involve data cleaning operations like removing duplicates, handling missing values, and converting data types to match your TiDB schema.
Ensure that you have a running instance of TiDB. If not, you will need to install and configure TiDB on your local machine or server. Follow the installation documentation provided by TiDB to set up the necessary components, including TiKV and PD, for a complete TiDB environment.
In your TiDB instance, create a database and define the tables where you will import the data. Use SQL commands to define the schema, ensuring that the table structures align with the data format from AppFollow. This step may involve creating tables, defining data types, and setting up primary keys or indexes as needed.
Before importing the data into TiDB, perform any necessary transformations to ensure compatibility. This may include converting date formats, ensuring text encoding is consistent, and formatting numerical values. Use scripts or tools like Python or shell scripting for this purpose, applying changes directly to your exported data files.
Use TiDB's built-in tools such as `LOAD DATA` or `TiDB Lightning` to import the processed data into your TiDB tables. The `LOAD DATA` statement can be executed via a command-line interface or a SQL client, pointing to your CSV files and specifying the appropriate table. For larger datasets, consider using `TiDB Lightning` to perform a faster, more efficient import.
After importing the data, perform checks to ensure that the data in TiDB is accurate and complete. Use SQL queries to verify the row counts, inspect random samples of the data for accuracy, and compare with the original exported data from AppFollow. Address any discrepancies by re-importing or manually correcting the data as necessary.
By following these steps, you can effectively move data from AppFollow to TiDB without relying on third-party connectors or integrations, ensuring a smooth and controlled data transfer process.
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:
