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."
Begin by accessing your Outreach account and identifying the data you need to move. Depending on the data type (e.g., contacts, emails, tasks), use Outreach's built-in export functionality or APIs. Typically, Outreach allows you to export data in CSV or JSON formats. Use the API if you need more control over the data extraction process, possibly requiring coding skills to interface with the Outreach API.
Once you have extracted the data, prepare it for transfer. This involves cleaning the data to ensure consistency and format compatibility with Apache Iceberg. Ensure that the data types are properly aligned and that there are no missing or corrupted data entries. Convert the data into a structured format like CSV or Parquet, which are suitable for Apache Iceberg.
Before transferring data to Apache Iceberg, set up a local or cloud storage environment where the data can be temporarily stored. This could be a local file system or a cloud-based storage service like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Ensure that you have the necessary permissions and configuration to store your data securely.
Install Apache Iceberg on your preferred environment. This could be on a Hadoop cluster, a standalone Spark environment, or any compatible processing framework that supports Iceberg. Configure Iceberg by setting up the necessary catalog configurations (e.g., Hive Metastore, AWS Glue, etc.) and specify the storage location where you will load the data.
Use a data processing engine like Apache Spark to load the data into Apache Iceberg. Write a Spark job that reads the prepared data from your local or cloud storage and writes it into an Iceberg table. Ensure that the schema of your Iceberg table matches the schema of your data. Use Spark�s Iceberg integration to create or append data to the Iceberg table.
After loading data into Apache Iceberg, verify the integrity and accuracy of the data. Run queries to ensure that the data aligns with what was extracted from Outreach. Check for data consistency, completeness, and accuracy. Use SQL queries via Spark or another compatible query engine to validate data counts, schema, and sample entries.
Finally, establish a routine for regularly updating the data in Apache Iceberg. Depending on how frequently the Outreach data changes, you might need to automate the data extraction, preparation, and loading processes using scripts or cron jobs to ensure that the Iceberg data remains current. Document this process and set up monitoring to catch any potential issues quickly.
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.
Outreach is a sales engagement platform that accelerates revenue growth by optimizing every interaction throughout the customer lifecycle. The platform manages all customer interactions across email, voice and social, and leverages machine learning to guide reps to take the right actions.
Outreach's API provides access to a wide range of data related to sales and marketing activities. Here are some of the categories of data that can be accessed through the API:
1. Prospects and leads: Information about potential customers, including their contact details, job titles, and company information.
2. Accounts: Data related to the companies that prospects and leads work for, including company size, industry, and location.
3. Activities: Information about sales and marketing activities, such as emails, calls, and meetings, including details about the participants, duration, and outcomes.
4. Templates and sequences: Data related to email templates and sequences used in outreach campaigns, including open and click-through rates.
5. Analytics: Metrics related to sales and marketing performance, such as conversion rates, pipeline value, and revenue generated.
6. Integrations: Information about third-party tools and services integrated with Outreach, including data related to those integrations.
Overall, Outreach's API provides a wealth of data that can be used to optimize sales and marketing strategies, improve customer engagement, and drive revenue growth.
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:
