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Begin by exporting the data you need from Iterable. You can use Iterable's API to extract data by making HTTP requests to the relevant endpoints. For example, use the `/api/export/data` endpoint to pull data, setting the necessary parameters to filter the data as needed. Ensure you have your API key ready and that you handle pagination if the data set is large.
Once you have retrieved the data from Iterable, transform it into CSV format. CSV is a universal format that Snowflake can easily ingest. Use a script in a language like Python or JavaScript to parse the JSON response from Iterable and write these records into CSV files, ensuring that the data is correctly formatted with the necessary headers.
After formatting the data into CSV files, transfer these files to a secure location where they can be accessed by Snowflake for loading. This could be an AWS S3 bucket, Google Cloud Storage, or an Azure Blob Storage. Ensure that your files are securely stored and that you have the necessary permissions to access them from Snowflake.
In Snowflake, create an external stage pointing to the location where your CSV files are stored. Use the `CREATE STAGE` SQL command, specifying the storage location details and authentication credentials. For instance, if using AWS S3, provide the S3 bucket name, path, and access credentials.
Before loading the data, create a table in Snowflake that matches the structure of your CSV files. Use the `CREATE TABLE` command to define the table schema, ensuring that the columns and data types correspond to those in your CSV files. Consider any necessary transformations that may need to be applied to the data.
With the stage and table prepared, load your data from the CSV files into the Snowflake table. Use the `COPY INTO` command to read from the stage and insert the data into your table. Ensure to handle any potential errors or data conversions during this process, and verify that all data has been imported correctly.
After loading the data into Snowflake, perform a series of checks to ensure that the data has been loaded accurately and completely. Run queries to compare record counts with the source data, check for data integrity, and validate that all fields have been populated as expected. Make any necessary adjustments or rerun the load if discrepancies are found.
By following these steps, you can manually move data from Iterable to Snowflake without relying on third-party connectors or integrations, ensuring a custom and secure data transfer process.
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:
