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Start by logging into your Vitally account. Navigate to the data export section, which is typically found in the account settings or under data management. Choose the data you wish to export, such as customer information, engagement metrics, or usage statistics. Export the data in a CSV or JSON format, as these are commonly supported formats for data manipulation and transfer.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will store the exported files from Vitally. Make sure to configure the bucket with appropriate permissions and settings, such as enabling versioning and setting up access policies that define who can read or write to the bucket.
On your local machine, ensure you have the AWS CLI (Command Line Interface) installed and configured. You can download it from the AWS website. Once installed, configure the CLI with your AWS credentials and default region using the `aws configure` command. This will allow you to interact with your AWS resources from the command line.
Use the AWS CLI to upload the data exported from Vitally to your S3 bucket. Navigate to the directory where your exported CSV or JSON files are located. Use the command `aws s3 cp s3:///` to upload each file to the S3 bucket. Ensure that the files are correctly uploaded by listing the contents of the bucket using `aws s3 ls s3:///`.
Navigate to the AWS Glue service in your AWS Management Console. Create a new Glue Crawler that will scan the data in your S3 bucket and create a data catalog. Configure the crawler with the appropriate IAM role that has access to your S3 bucket and define the data store as your S3 bucket. Run the crawler to populate the AWS Glue Data Catalog with the schema of your data.
Once your data is cataloged, create an AWS Glue ETL (Extract, Transform, Load) job to transform the data according to your requirements. This may involve cleaning the data, reformatting it, or integrating it with other datasets. Define the source and target locations, and specify the transformations you need to perform within the Glue job script.
After running your Glue ETL job, the transformed data should be ready to load into your AWS Datalake. If your Datalake is structured in an S3-based architecture, confirm that the ETL job output is stored in the correct S3 location. Ensure data integrity and correctness by verifying the output files. You may also set up continuous Glue jobs to handle regular data updates if needed.
By following these steps, you can successfully transfer data from Vitally to an AWS Datalake using native tools and services provided by AWS, 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.
Vitally is a customer engagement platform for B2B SaaS companies to drive a world-class customer experience and eliminate churn. Our easy-to-use platform integrates all your customer data and provides a 360 degree view into the metrics that matter most to you, allows you to set up health scores and notifications, and create powerful automationplaybooks.
Vitally's API provides access to a wide range of data related to customer success and engagement. The following are the categories of data that can be accessed through Vitally's API:
1. Account Data: This includes information about the customer's account, such as account name, account ID, and account status.
2. User Data: This includes information about the users associated with the account, such as user name, user ID, and user role.
3. Activity Data: This includes information about the activities performed by the users, such as login activity, feature usage, and engagement metrics.
4. Support Data: This includes information about the customer support interactions, such as support tickets, chat logs, and email conversations.
5. Health Data: This includes information about the health of the customer account, such as usage trends, churn risk, and renewal probability.
6. Feedback Data: This includes information about the customer feedback, such as survey responses, NPS scores, and customer reviews.
Overall, Vitally's API provides a comprehensive set of data that can be used to gain insights into customer behavior, engagement, and satisfaction, and to optimize customer success 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?
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