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First, log into your Iterable account and navigate to the ‘Export Data’ section. Select the data you want to export, such as user lists, event data, or campaign results. Choose the CSV format for your export, as this is a widely compatible format. Make sure to include all necessary fields to avoid missing critical data.
Open the exported CSV files in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are included and data types are consistent. Clean up any discrepancies or unnecessary columns that are not needed in Convex. Make sure the data is formatted correctly for easy import into Convex.
Before importing, set up the necessary database tables and fields within Convex to accommodate the data structure from Iterable. Log in to your Convex account and create tables that correspond to the data you exported, ensuring field names and data types match those in your CSVs.
If there are any fields in your CSV files that do not match the data types or structures supported by Convex, you will need to transform these. Use your spreadsheet application to adjust these fields, such as changing date formats, normalizing text fields, or splitting combined fields into separate columns.
Utilize Convex’s import functionality to bring your prepared CSV files into the database. Navigate to the data import section of Convex, select the prepared CSV files, and follow the prompts to map CSV columns to database fields. Ensure that the import settings match the data structure you set up in Convex.
After importing, review the data in Convex to ensure that all records have been accurately transferred. Check for discrepancies, such as missing records or incorrect data types. Use queries and reports within Convex to validate that the data is complete and functional as intended.
Finally, update any workflows or processes that depend on this data within Convex. This could include setting up triggers, notifications, or analytics dashboards that utilize the newly imported data. Ensure that the data is being utilized effectively to achieve your objectives within the Convex platform.
By following these steps, you can successfully move data from Iterable to Convex 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?
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