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 your data from ConfigCat. Log into your ConfigCat account and navigate to the relevant feature flags or settings you want to export. Use the export functionality (typically in JSON or CSV format) to download the data to your local machine. Ensure you have the necessary permissions and that data integrity is maintained during the export.
Once you have the exported file, open it using a text editor (for JSON) or spreadsheet software (for CSV). Review the data structure and format it to align with the schema requirements of your MS SQL Server database. This may involve restructuring JSON objects or cleaning up CSV data to eliminate any inconsistencies or errors.
Access your MS SQL Server instance using SQL Server Management Studio or a similar tool. Create a new table that matches the structure of your data. Use SQL statements to define the table schema, ensuring that data types and constraints are compatible with your imported data. Example:
```sql
CREATE TABLE ConfigCatData (
SettingKey VARCHAR(255),
SettingValue NVARCHAR(MAX),
Description NVARCHAR(255),
LastUpdated DATETIME
);
```
Convert your prepared data into a format suitable for SQL insertion. For JSON data, this might involve parsing and flattening hierarchical structures into flat rows. For CSV data, ensure it aligns with the column order and types defined in your SQL table. Tools like Python or Excel can be helpful for this conversion.
Manually or programmatically generate SQL `INSERT` statements for each row of data you need to import. This can be done using a script or tool that reads your formatted data and outputs a series of SQL commands. For instance:
```sql
INSERT INTO ConfigCatData (SettingKey, SettingValue, Description, LastUpdated) VALUES ('FeatureA', 'true', 'Enables feature A', '2023-10-01 12:00:00');
```
Open your SQL Server Management Studio and execute the generated `INSERT` statements. You can either execute them directly in a query window or use a batch file to run them sequentially. Monitor the process for any errors and ensure all data is imported correctly.
After importing, run a series of queries to verify that the data in MS SQL Server matches the original data from ConfigCat. Check for discrepancies in row counts, data values, and data types. This step helps ensure that the migration process was successful and that the data is ready for use in your applications.
This guide should help you manually move data from ConfigCat to MS SQL Server without relying on any 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.
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:
