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."
First, you'll need to export your data from Delighted. Log in to your Delighted account, navigate to the 'Export' section, and choose the data you want to export. Delighted typically allows you to export data in CSV format, which is ideal for manual processing. Save the CSV file to your local machine.
Before uploading, ensure the CSV file is structured correctly. Check that the data types and column headers meet the requirements of your Snowflake database schema. Remove any unnecessary columns or data that won't be needed in Snowflake to streamline the import process.
Log in to your Snowflake account and create a stage where your data will be uploaded. Use the Snowflake console and run the SQL command:
```sql
CREATE OR REPLACE STAGE my_stage;
```
This stage will temporarily hold your CSV files before loading them into tables.
Use the SnowSQL command-line interface to upload the CSV file to the stage you created. If you haven't installed SnowSQL, download and configure it first. Use the following command:
```sh
snowsql -q "PUT file://path/to/your/file.csv @my_stage"
```
This command uploads your CSV file to the Snowflake stage.
Ensure that a table exists in Snowflake with a schema matching your CSV file. If it doesn't exist, create it using a SQL command in the Snowflake console. For example:
```sql
CREATE TABLE delighted_data (
column1 STRING,
column2 STRING,
column3 INT
);
```
Load your data from the stage into the Snowflake table. Execute a `COPY INTO` command:
```sql
COPY INTO delighted_data
FROM @my_stage/file.csv
FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"')
```
This command transfers the data from the stage to your designated Snowflake table.
Finally, verify that the data has been imported correctly. Run a `SELECT` query to check the first few rows and ensure all data points are accurate and complete:
```sql
SELECT * FROM delighted_data LIMIT 10;
```
Check for consistency between your original CSV and the Snowflake table data to confirm the integrity of the transfer.
By following these steps, you can successfully move data from Delighted to the Snowflake Data Cloud 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.
Delighted assists businesses connect with their customers learning, improving, and delighting.It is well known for delivering some of the most innovative functionality for customer experience management. Delighted is completely the self-serve experience management platform of choice for the worldwide top brands. It helps to collect and analyze survey feedback through Delighted. Get set up in minutes, no technical knowledge needed. Delight helps to build long-lasting relationships and deliver great service experience.
Delighted's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Delighted's API are:
1. Survey Responses: This includes all the responses received from customers through Delighted's surveys. It includes both quantitative and qualitative data.
2. Metrics: This includes various metrics related to customer satisfaction, such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES).
3. Trends: This includes trends related to customer feedback and satisfaction over time. It helps businesses to identify patterns and make data-driven decisions.
4. Segmentation: This includes data related to customer segments, such as demographics, location, and behavior. It helps businesses to understand their customers better and tailor their offerings accordingly.
5. Integrations: Delighted's API also provides access to data from various integrations, such as Salesforce, HubSpot, and Slack. It helps businesses to streamline their workflows and improve their customer experience. Overall, Delighted's API provides a comprehensive set of data that businesses can use to measure and improve their customer satisfaction.
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:
