Implementing a Cloud-Based Data Warehouse Solution in AWS
AWS QuickSight offers you an opportunity to harness the power of the data. Read this first-hand account to see how one CIO relies on QuickSight to make better use of the data in his organization's data warehouse.
Why choose AWS for a data warehouse?
We chose AWS for our data warehouse due to its scalability, reliability, and the wide range of tools and services it offers. This combination allows us to effectively manage our growing data needs and make data-driven decisions.
How does data extraction and transformation work?
We set up AWS Glue ETL jobs to extract data from DynamoDB and transform it into a structured format suitable for analysis. Using PySpark, we wrote the transformation code to handle data processing before loading it into Athena tables for querying.
How do you visualize data with QuickSight?
We utilized QuickSight to create interactive dashboards and reports. By integrating data from our data warehouse, we generated various visualizations like charts and graphs. To ensure our dashboards reflected the most recent data, we set up automatic refresh schedules and used Glue for incremental updates.
Implementing a Cloud-Based Data Warehouse Solution in AWS
published by VEscape Labs
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