Warehouses and Lakes
Marketing Analytics

How to load data from Iterable to AWS Datalake

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

TL;DR

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 as a source connector (using Auth, or usually an API key)
  2. set up AWS Datalake as a destination connector
  3. 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 AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Iterable with AWS Datalake in minutes

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Prerequisites

  1. A Iterable account to transfer your customer data automatically from.
  2. A AWS Datalake account.
  3. 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 AWS Datalake, for seamless data migration.

When using Airbyte to move data from Iterable to AWS Datalake, it extracts data from Iterable using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their Iterable data for advanced analytics and insights within AWS Datalake, 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 AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

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

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

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Iterable from the dropdown list of your configured sources.
  3. Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
  4. 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.
  5. Select the data to sync: Choose the specific Iterable objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
  6. 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.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Iterable to AWS Datalake according to your settings.

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

Use Cases to transfer your Iterable data to AWS Datalake

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

  1. Advanced Analytics: AWS Datalake’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.
  2. Data Consolidation: If you're using multiple other sources along with Iterable, syncing to AWS Datalake 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.
  3. Historical Data Analysis: Iterable has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Iterable data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Iterable data.
  6. Data Science and Machine Learning: By having Iterable data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Iterable provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Iterable table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Iterable account as an Airbyte data source connector.
  2. Configure AWS Datalake as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Iterable to AWS Datalake 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!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
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Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
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Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
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Connectors Used

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Frequently Asked Questions

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.

What data can you transfer to AWS Datalake?

You can transfer a wide variety of data to AWS Datalake. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Iterable to AWS Datalake?

The most prominent ETL tools to transfer data from Iterable to AWS Datalake include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Iterable and various sources (APIs, databases, and more), transforming it efficiently, and loading it into AWS Datalake and other databases, data warehouses and data lakes, enhancing data management capabilities.