How to load data from Iterable to BigQuery

Learn how to use Airbyte to synchronize your Iterable data into BigQuery within minutes.

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Set up a Iterable connector in Airbyte

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

Set up BigQuery for your extracted Iterable data

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

Configure the Iterable to BigQuery 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.

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TL;DR

set up Iterable as a source connector (using Auth, or usually an API key)
set up BigQuery as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Iterable

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.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

Integrate Iterable with BigQuery in minutes

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Prerequisites

A Iterable account to transfer your customer data automatically from.
A BigQuery account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Iterable and BigQuery, for seamless data migration.

When using Airbyte to move data from Iterable to BigQuery, it extracts data from Iterable using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Iterable data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Iterable as a source connector

1. First, navigate to the Airbyte dashboard and click on "Sources" in the left-hand menu.

2. Click on the "Create New Source" button and select "Iterable" from the list of available connectors.

3. Enter a name for your Iterable source and click "Next".

4. Enter your Iterable API key in the "API Key" field. You can find your API key in your Iterable account under "API Keys" in the "Integrations" tab.

5. Select the data you want to sync from Iterable by checking the boxes next to the relevant objects (e.g. users, campaigns, events).

6. Choose how often you want your data to sync by selecting a sync frequency from the dropdown menu.

7. Click "Test" to ensure that your credentials are correct and that Airbyte can connect to your Iterable account.

8. If the test is successful, click "Create Source" to save your Iterable source and start syncing your data.

9. You can monitor the progress of your sync in the Airbyte dashboard under "Jobs".

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Step 3: Set up a connection to sync your Iterable data to BigQuery

Once you've successfully connected Iterable as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Iterable from the dropdown list of your configured sources.
Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Iterable objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Iterable to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Iterable data.

Use Cases to transfer your Iterable data to BigQuery

Integrating data from Iterable to BigQuery provides several benefits. Here are a few use cases:

Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Iterable data, extracting insights that wouldn't be possible within Iterable alone.
Data Consolidation: If you're using multiple other sources along with Iterable, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Iterable has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: BigQuery provides robust data security features. Syncing Iterable data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Iterable data.
Data Science and Machine Learning: By having Iterable data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Iterable provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Iterable table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Iterable account as an Airbyte data source connector.
Configure BigQuery as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Iterable to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Iterable to BigQuery Manually

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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.

What is Iterable?

What data can you extract from Iterable?

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.

How do I transfer data from Iterable?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Iterable to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Iterable to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

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Warehouses and Lakes

Marketing Analytics

How to load data from Iterable to BigQuery

Learn how to use Airbyte to synchronize your Iterable data into BigQuery within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 1: Setup Iterable

Step 2: Setup BigQuery

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Iterable and BigQuery

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Iterable as a source connector (using Auth, or usually an API key)
set up BigQuery as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Iterable

What is BigQuery

Integrate Iterable with BigQuery in minutes

Try for free now

Prerequisites

A Iterable account to transfer your customer data automatically from.
A BigQuery account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.