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Begin by logging into your Sendinblue account. Navigate to the data or contacts section you wish to export. Use the built-in export feature to download the data as a CSV or JSON file. Ensure that the file is saved locally on your machine.
Log in to your AWS Management Console and go to the S3 service. Create a new bucket to store the data. Make sure to configure appropriate permissions and versioning settings if necessary. Note the bucket name as you will need it for the upload.
Install the AWS Command Line Interface (CLI) on your local machine if not already installed. This will allow you to interact with AWS services from your terminal. Follow instructions from the [AWS CLI installation guide](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) for your operating system.
Open your terminal and configure the AWS CLI with your credentials by running `aws configure`. Enter your AWS Access Key ID, Secret Access Key, region, and output format. These credentials should have permissions to write to the S3 bucket created earlier.
Use the AWS CLI to upload the exported data file from your local machine to the S3 bucket. Run the command:
```bash
aws s3 cp /path/to/your/file.csv s3://your-bucket-name/
```
Replace `/path/to/your/file.csv` with the path to your file and `your-bucket-name` with the name of your S3 bucket.
In the AWS Management Console, navigate to AWS Glue to set up a crawler. Create a new crawler that points to your S3 bucket and configure it to classify and catalog your data. Schedule the crawler to run periodically if you plan to update your data regularly.
Once the Glue crawler has cataloged your data, use AWS Athena to query it. In the Athena console, you can write SQL queries against the data stored in S3. This step allows you to analyze and extract insights from your data within the AWS ecosystem.
By following these steps, you can efficiently move data from Sendinblue to AWS Datalake using native AWS services, 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.
The smartest and most intuitive platform is Sendinblue for growing businesses. Sendinblue is a comparatively easy tool to learn. Sendinblue only supports full refresh syncs meaning that each time you use the connector it will sync all available records from scratch. Sendinblue is a marketing tool that stands out from its competitors and this is also an email marketing solution for small and medium-sized businesses that want to send and automate email marketing campaigns.
Sendinblue's API provides access to a wide range of data related to email marketing and automation. The following are the categories of data that can be accessed through Sendinblue's API: 1. Contacts: This includes data related to the contacts in your Sendinblue account, such as their email addresses, names, and other contact information. 2. Campaigns: This includes data related to the email campaigns you have created in Sendinblue, such as the subject line, content, and delivery statistics. 3. Automation: This includes data related to the automated workflows you have set up in Sendinblue, such as the triggers, actions, and performance metrics. 4. Transactional emails: This includes data related to the transactional emails you have sent through Sendinblue, such as the recipient, content, and delivery status. 5. Reports: This includes data related to the performance of your email marketing efforts, such as open rates, click-through rates, and conversion rates. 6. Lists: This includes data related to the lists you have created in Sendinblue, such as the number of contacts in each list and their segmentation criteria. Overall, Sendinblue's API provides access to a comprehensive set of data that can help businesses optimize their email marketing and automation strategies.
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:
