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Begin by exporting the data you want to transfer from MailerLite. Log into your MailerLite account, navigate to the "Subscribers" section, and select the "Export" option. Choose the format you prefer, typically CSV, as it's widely compatible and easy to manage. Save the exported files to your local system.
Set up your AWS environment by creating an S3 bucket where your data will be stored. Log into the AWS Management Console, navigate to the S3 service, and create a new bucket. Ensure you configure appropriate permissions and settings to secure your data, such as enabling versioning and server-side encryption.
Download and install the AWS Command Line Interface (CLI) on your local machine. This tool will allow you to interact with AWS services directly from your command line. Follow the official AWS CLI installation guide for your operating system and configure it using your AWS credentials with `aws configure`.
Use the AWS CLI to upload the exported MailerLite data to your S3 bucket. Open your command line interface and execute the `aws s3 cp` command to transfer the files. For example: `aws s3 cp /path/to/exported_file.csv s3://your-bucket-name/`. Verify that your data has been correctly uploaded by checking the S3 console.
AWS Glue is a service that prepares your data for analytics. Set up an AWS Glue Data Catalog to organize your data. Create a new Glue database and a crawler that points to your S3 bucket. The crawler will scan your data, infer its schema, and populate the Glue Data Catalog.
Once your crawler has finished, create an AWS Glue ETL (Extract, Transform, Load) job to transform your data if necessary. This might include cleansing data, changing its format, or performing transformations. Write your transformation script in Python or Scala, then execute the job to output the processed data back into your S3 bucket.
Use Amazon Athena to query your data directly from the S3 bucket. Athena is a serverless query service that allows you to use SQL to analyze your data. Go to the Athena console, set up a query using the tables created in the Glue Data Catalog, and execute your SQL queries to gain insights from your data.
By following these steps, you can efficiently move data from MailerLite to an AWS Data Lake, leveraging AWS's powerful suite of tools for storage, transformation, and analysis 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.
MailerLite is an intuitive email marketing solution for people of all skill levels. Simplicity is the core principle behind our solutions. We provide drag-and-drop content editors, simplified subscriber management, and advanced automation that are easy to set up. MailerLite is a distributed team of over 130 people living and working in 40 countries. Our international team enables us to better serve our customers around the world.
MailerLite's API provides access to a wide range of data related to email marketing campaigns. The following are the categories of data that can be accessed through MailerLite's API:
1. Subscribers: This category includes data related to subscribers such as their email address, name, location, and subscription status.
2. Campaigns: This category includes data related to email campaigns such as the subject line, content, delivery time, and open and click-through rates.
3. Lists: This category includes data related to email lists such as the name of the list, the number of subscribers, and the date the list was created.
4. Segments: This category includes data related to segments such as the name of the segment, the criteria used to create the segment, and the number of subscribers in the segment.
5. Automation: This category includes data related to automated email campaigns such as the trigger, content, and delivery time.
6. Forms: This category includes data related to forms such as the name of the form, the number of submissions, and the date the form was created.
7. Reports: This category includes data related to email campaign reports such as the number of opens, clicks, bounces, and unsubscribes.
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:
