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Begin by accessing your Retently account and navigating to the section where you can export your data. Retently typically allows data export in formats like CSV or Excel. Choose the appropriate format and download the data to your local machine.
Once the data is exported, inspect the files to ensure that the data is correctly formatted and clean. Remove any unnecessary columns, check for data consistency, and ensure that the file names are descriptive. Save the prepared files in a directory on your local system.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will store your Retently data. Make sure to choose a unique bucket name and configure the bucket settings, including setting appropriate permissions and enabling versioning if needed.
Using the AWS Management Console, upload the prepared data files from your local machine to the newly created S3 bucket. You can simply drag and drop the files into the bucket through the S3 interface or use the AWS CLI for bulk uploads. Ensure that the uploaded files have the correct permissions and storage class settings.
Organize the data within your S3 bucket by creating folders or prefixes that categorize the data logically. This can be based on time periods, data types, or any other relevant categorizations. This organization will help in efficiently managing and querying the data later.
Go to the AWS Glue service in the AWS Management Console. Create a new Glue Crawler that will scan the data in your S3 bucket and create a metadata catalog. Configure the crawler to run periodically if you plan to update the data regularly. Define the data source as your S3 bucket and specify the output database in the Glue Data Catalog.
After the Glue Crawler has successfully cataloged your data, navigate to AWS Athena. Use Athena to write SQL queries to analyze and extract insights from your data. Ensure that the Glue Data Catalog database is selected in Athena, and construct queries based on your data analysis needs. You can also visualize the results directly in Athena or export them for further analysis.
This step-by-step guide allows you to move data from Retently to AWS Data Lake using AWS's suite of tools 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.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
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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