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."
Begin by exporting the data you need from Iterable. You can do this by accessing the Iterable dashboard and using its export functionalities. Typically, you will need to run a query for the data set you want to export or use the export feature for user lists or event data. Save the exported data in a CSV or JSON format, as these are widely supported and easy to manipulate.
Once you have the exported data, inspect and clean it if necessary. Ensure the data is consistent and free of errors or duplicates. If your data is in JSON format, consider transforming it into CSV or Parquet if it simplifies the process, as Firebolt supports these formats well. Make sure the schema of your data matches the schema of the Firebolt table where you intend to load the data.
Log into your Firebolt account and ensure you have access to the necessary resources. You will need to have a database and a table set up in Firebolt where the data will be loaded. If not already done, create a database and table following Firebolt's SQL syntax. Make sure the table's schema matches that of your prepared data.
Use Firebolt’s native command-line tools or SQL client to establish a connection to your Firebolt database. You will typically need your Firebolt account credentials and the endpoint URL to connect. This step is crucial as it sets up the environment necessary for data insertion.
With your connection to Firebolt established, upload your data file to a location accessible by Firebolt. You can use the Firebolt CLI or SQL client to execute a `COPY INTO` command. This command will load data from your file into the Firebolt table. Ensure that the file path and data format specified in the command match your prepared data file.
Once the data is loaded into Firebolt, run a series of verification queries to ensure the data has been accurately transferred. Check for the correct number of records, data integrity, and schema conformity. This step helps identify any discrepancies or errors that may have occurred during the data transfer process.
After verifying the data, optimize your Firebolt table by creating necessary indexes to enhance query performance. Use Firebolt’s indexing features to speed up query execution on your data. This step is important to ensure that you can efficiently run analytical queries on your newly imported data.
By following these steps, you can successfully move data from Iterable to Firebolt without relying on third-party connectors or integrations.
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.
Iterable is a marketing platform designed to help businesses grow. Its automated platform enables businesses to measure and optimize customer interactions, with the ability to easily create and execute cross-channel campaigns. Through in-app notifications, email, SMS, web and mobile push, and social media integrations, Iterable powers the entire customer engagement lifecycle, throughout all stages of the customer journey.
Iterable's API provides access to a wide range of data related to customer engagement and marketing campaigns. The following are the categories of data that can be accessed through Iterable's API:
1. User data: This includes information about individual users such as their email address, name, location, and other demographic information.
2. Campaign data: This includes information about marketing campaigns such as email campaigns, push notifications, and SMS campaigns. It includes data on the number of messages sent, open rates, click-through rates, and conversion rates.
3. Event data: This includes data on user behavior such as website visits, product purchases, and other actions taken by users.
4. List data: This includes information about the lists of users that have been created in Iterable, including the number of users in each list and their engagement history.
5. Template data: This includes information about the email templates and other marketing materials used in campaigns, including their design, content, and performance metrics.
6. Analytics data: This includes data on the performance of marketing campaigns, including metrics such as revenue generated, customer lifetime value, and return on investment.
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:
