How to load data from Insightly to Kafka

Learn how to use Airbyte to synchronize your Insightly data into Kafka within minutes.

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.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Insightly connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Kafka for your extracted Insightly data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Insightly to Kafka in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

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

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more
Chase Zieman headshot

Chase Zieman

Chief Data Officer

“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.”

Learn more

Rupak Patel

Operational Intelligence Manager

"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."

Learn more

How to Sync to Manually

Step 1: Understand Insightly API

Before extracting data, familiarize yourself with the Insightly REST API. Review the API documentation to understand how to authenticate, construct queries, and retrieve the data you need. This will typically involve using API endpoints to fetch data like contacts, leads, and other entities.

To access Insightly data, you need to authenticate API requests. Use API keys provided by Insightly for authentication. Write a script in a language like Python or JavaScript to handle HTTP requests, including setting headers for authentication.

Develop a script to send HTTP GET requests to the relevant Insightly API endpoints. Parse the JSON response to extract the data you are interested in. You might need to handle pagination if you are dealing with large datasets.

Once data is extracted, transform it into a format suitable for Kafka. Kafka typically works well with JSON, Avro, or Protobuf formats. Ensure that the transformed data has all necessary fields and is structured correctly to be consumed by Kafka.

Set up a Kafka producer using a library in your preferred programming language, such as the `kafka-python` library for Python or the `kafka-clients` for Java. Configure this producer to connect to your Kafka cluster, specifying the broker addresses and any required authentication.

With the Kafka producer set up, write the transformed data to specific Kafka topics. Determine the appropriate topic names based on the data types (e.g., "insightly_contacts", "insightly_leads") and send the data using the `send` method of your Kafka producer.

Ensure that your pipeline includes robust error handling and logging. Implement try-except blocks (or equivalent) to catch any exceptions during API calls or data transmission. Use logging to record successful data transfers and errors, which will aid in monitoring and troubleshooting the process.

By following these steps, you will have a custom solution to move data from Insightly to Kafka without relying on third-party connectors, giving you full control over the data transfer process.