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, log in to your Freshcaller account. Navigate to the "Reports" or "Analytics" section, where you can find options to export data. Choose the type of data you need (e.g., call logs, contacts) and export it in a CSV or JSON format. This file will be used for importing into Typesense.
After exporting the data, examine the CSV or JSON file to ensure it is correctly formatted for Typesense. Typesense requires data to be in a specific structure, typically a JSON array of objects. Convert your CSV data to JSON if necessary, ensuring each object contains the fields you want to index.
Install and set up a Typesense server on your local machine or a cloud server. You can do this by downloading the Typesense binary from the official website and running it. Ensure the server is properly configured and running. You might need to configure firewall settings to allow access to the server.
Using the Typesense client (available in various languages like JavaScript, Python, etc.), create a new collection in Typesense. Define the schema for this collection according to the structure of your data. This includes specifying the fields, their types, and which fields should be indexed or searchable.
Write a script in a programming language of your choice that reads the JSON file prepared in step 2 and sends the data to your Typesense collection. Use Typesense's API to perform this operation. The script should iterate through each record in the JSON file and use the Typesense client to index the records.
After importing the data, verify that it has been correctly indexed in Typesense. Use the Typesense client to perform search queries on your collection and ensure that the data is searchable and returns expected results. Validate the schema, data integrity, and search functionalities.
Finally, monitor the performance of your Typesense instance. Check the server logs for errors and optimize the search performance by adjusting index settings if necessary. Consider factors like search speed and relevancy of results, adjusting the schema and indexing strategy as needed to enhance performance.
By following these steps, you'll successfully move data from Freshcaller to Typesense without relying on third-party tools, ensuring complete control over the data migration 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.
Setup a connection to your Freshcaller site in minutes, and select the Freshcaller collections you want to replicate.
Freshcaller's API provides access to a wide range of data related to call center operations. The following are the categories of data that can be accessed through Freshcaller's API:
1. Call data: This includes information about incoming and outgoing calls, call duration, call recordings, and call transcripts.
2. Agent data: This includes information about agents, such as their availability, status, and performance metrics.
3. Queue data: This includes information about call queues, such as the number of calls waiting, the average wait time, and the number of agents available.
4. IVR data: This includes information about Interactive Voice Response (IVR) systems, such as the number of calls handled by the IVR, the number of calls transferred to agents, and the success rate of the IVR.
5. Ticket data: This includes information about tickets created from calls, such as the status of the ticket, the agent assigned to the ticket, and the resolution time.
6. Analytics data: This includes information about call center performance metrics, such as call volume, call abandonment rate, and average handle time.
Overall, Freshcaller's API provides a comprehensive set of data that can be used to monitor and optimize call center operations.
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:
