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."
To begin, log in to your Aircall admin dashboard. Navigate to the ‘Analytics’ or ‘Data Export’ section. Depending on the available options, you can manually export call logs, user data, and other necessary information in formats like CSV or Excel. Ensure that your exports include all required fields and data points for your intended analysis.
Create a local directory on your computer or server to store the exported data files securely. Organize the directory structure to clearly separate different types of data (e.g., call logs, user data) to simplify data management and processing later on.
Open the exported data files using a tool like Excel or a script in Python or R. Transform the data into a format compatible with Firebolt, typically CSV or Parquet. During this step, clean the data by removing duplicates, handling missing values, and ensuring that the data types are consistent with Firebolt's table schema requirements.
Before loading the data, define the table schema in Firebolt that matches the structure of your transformed data. Access your Firebolt account and use SQL commands in the Firebolt console to create the necessary tables. Specify the data types and any indices that will optimize query performance.
Utilize Firebolt's data loading capabilities to manually upload the transformed data files. You can use the Firebolt SQL command `COPY` to load data from your local file system into Firebolt tables. Ensure your files are accessible from Firebolt by placing them in an accessible storage location if needed.
After loading the data into Firebolt, run SQL queries to verify that the data has transferred correctly. Check for any discrepancies in record counts, data types, and ensure all fields have been imported as expected. Use basic queries to test data retrieval and ensure the tables are operating correctly.
To streamline future data transfers, create scripts or use cron jobs (on Unix-based systems) to automate the data extraction, transformation, and loading process. Write scripts in a language like Python to automate data pulling from Aircall via their API, transforming the data, and loading it into Firebolt using SQL commands or Firebolt's SDK.
By following these steps, you can manually manage the data transfer from Aircall to Firebolt, ensuring that the data is accurately moved and available for further analysis and reporting.
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.
Aircall is a cloud-based phone system that allows businesses to make and receive calls from anywhere in the world. It offers a range of features such as call routing, call recording, voicemail, and analytics to help businesses manage their phone communications more efficiently. Aircall integrates with popular business tools such as Salesforce, HubSpot, and Slack, making it easy to manage customer interactions and track performance. With Aircall, businesses can set up a professional phone system in minutes, without the need for any hardware or technical expertise. It is ideal for remote teams, sales teams, and customer support teams who need a flexible and scalable phone solution.
Aircall's API provides access to a wide range of data related to phone calls and call center operations. The following are the categories of data that can be accessed through Aircall's API:
1. Call data: This includes information about incoming and outgoing calls, such as call duration, call status, call recording, and call notes.
2. Contact data: This includes information about the contacts associated with each call, such as contact name, phone number, email address, and company name.
3. User data: This includes information about the users who are making and receiving calls, such as user name, user ID, and user status.
4. Team data: This includes information about the teams that are using Aircall, such as team name, team ID, and team members.
5. Analytics data: This includes information about call center performance, such as call volume, call duration, and call wait time.
6. Integration data: This includes information about the integrations that are being used with Aircall, such as CRM integrations and helpdesk integrations.
Overall, Aircall's API provides a comprehensive set of data that can be used to optimize call center operations and improve customer service.
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:
