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."
Review the SalesLoft API documentation to understand the available endpoints for retrieving the data you need. Familiarize yourself with authentication methods, data formats, rate limits, and any necessary parameters for API requests.
Log in to your SalesLoft account and navigate to the API settings. Generate an API key that you will use to authenticate your requests. Ensure you have the appropriate permissions to access the data you intend to retrieve.
Write a script (using a language like Python, JavaScript, or any that supports HTTP requests) to connect to the SalesLoft API. Use the API key to authenticate and make requests to the relevant endpoints to extract the data you need. Save this data in a suitable format, such as CSV or JSON files, on your local system.
Ensure that your MSSQL database is ready to receive the incoming data. Create tables that match the structure of the data you extracted from SalesLoft. Define the appropriate data types and constraints for each column to match the data format.
Process the extracted data to ensure it fits the schema of your MSSQL database. This may involve data cleaning, such as handling null values, converting data types, and formatting dates. You can use scripts or tools like Python pandas for this step.
Use a programming language or scripting tool that allows for database connectivity (such as Python with pyodbc, or a simple SQL script) to insert the transformed data into your MSSQL database. Establish a connection to your database, and execute SQL INSERT statements or use bulk insert methods to load the data efficiently.
After loading the data, run queries in your MSSQL database to verify the integrity and completeness of the data. Check for discrepancies, ensure that all records are imported, and validate that data types and constraints are maintained. If issues are found, troubleshoot and correct them before proceeding with further analysis or use of the data.
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.
Salesloft is a comprehensive sales engagement platform designed to help sales teams streamline their prospecting, communication, and pipeline management processes. It provides a centralized hub for sales professionals to execute targeted outreach campaigns, track email opens and clicks, schedule meetings, and manage their sales cadences. One of its key strengths is its ability to integrate with various other tools, amplifying its capabilities. Salesloft can connect with popular CRM systems like Salesforce, HubSpot, and Microsoft Dynamics, enabling seamless data synchronization and centralized contact management.
SalesLoft's API provides access to a wide range of data related to sales and marketing activities. The following are the categories of data that can be accessed through SalesLoft's API:
1. People: This category includes data related to individuals such as their name, email address, phone number, job title, and company.
2. Accounts: This category includes data related to companies such as their name, industry, location, and size.
3. Activities: This category includes data related to sales and marketing activities such as emails, calls, meetings, and tasks.
4. Cadences: This category includes data related to sales cadences such as the name, duration, and steps of a cadence.
5. Templates: This category includes data related to email templates such as the name, subject line, and body of a template.
6. Analytics: This category includes data related to sales and marketing performance such as open rates, response rates, and conversion rates.
7. Integrations: This category includes data related to third-party integrations such as the name, status, and configuration of an integration.
Overall, SalesLoft's API provides a comprehensive set of data that can be used to improve sales and marketing performance.
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:
