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Begin by logging into your Klaviyo account. Navigate to the "Lists & Segments" or "Analytics" section, depending on the data you need. Use Klaviyo's export feature to download your data in a CSV or JSON format. This can usually be done directly from the interface by selecting the desired dataset and opting for an export.
Once you have the exported data, inspect it to ensure the format is consistent and correct for your needs. Cleanse the data if necessary, removing any unwanted fields or correcting data formats. Save the cleansed data in a format suitable for AWS services, typically CSV or JSON.
If not already set up, install and configure the AWS Command Line Interface (CLI) on your local machine. This will require you to create AWS access keys if you haven't already. Use the command `aws configure` to input your AWS Access Key ID, Secret Access Key, region, and output format.
Log in to your AWS Management Console and go to the S3 service. Create a new S3 bucket where you will upload the Klaviyo data. Make sure to set appropriate permissions and policies on the bucket to allow for future access and data processing.
Use the AWS CLI to upload your prepared data files to the S3 bucket. This can be done with the command `aws s3 cp /local/path/to/your/data s3://your-s3-bucket-name/` replacing the paths with your actual file location and bucket name.
Set up an AWS Glue Crawler to catalog the data in your S3 bucket. Navigate to AWS Glue in the AWS Management Console and create a new crawler. Point the crawler to your S3 bucket and let it infer the schema of your data. Once the crawler has run, it will create a database and tables in the AWS Glue Data Catalog.
Use Amazon Athena to query your data stored in the S3 bucket and cataloged by AWS Glue. Go to the Athena service in the AWS Management Console, select the database and table created by the crawler, and start writing SQL queries to analyze your data. Athena will directly query the data stored in S3, allowing you to perform complex analyses and gain insights.
By following these steps, you can effectively move your data from Klaviyo to an AWS Data Lake environment without relying on third-party connectors. This process ensures you have full control over your data management and analytics workflows.
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.
Klavivo is a communications platform aimed at helping businesses grow through email and marketing automation. Klavivo does the granular work, from personalized newsletters and thank you’s to automated emails reminding visitors of abandoned carts and order follow-ups—so businesses don’t have to spend time on the little details. An inexpensive solution for businesses to customize email marketings campaigns, it integrates with a customer’s data sources at scale and allows brands to measure their results.
Klaviyo's API provides access to a wide range of data related to email marketing and e-commerce. The following are the categories of data that can be accessed through Klaviyo's API:
1. Profiles: This includes information about individual subscribers, such as their email address, name, location, and other demographic data.
2. Lists: This includes information about the different email lists that are managed within Klaviyo, such as the number of subscribers, the date they were added, and their engagement metrics.
3. Campaigns: This includes information about the different email campaigns that have been sent, such as the subject line, the content, and the performance metrics.
4. Metrics: This includes data related to the performance of email campaigns, such as open rates, click-through rates, and conversion rates.
5. Events: This includes data related to specific actions taken by subscribers, such as making a purchase, abandoning a cart, or signing up for a newsletter.
6. Products: This includes information about the products that are sold through an e-commerce store, such as their name, price, and availability.
7. Orders: This includes information about the orders that have been placed by customers, such as the order number, the date, and the total amount.
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:
