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Sync with Airbyte
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".
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.
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.
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.
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.
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".
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.
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:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
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 should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: