Amazon QuickSight is an agile, cloud-based business intelligence service that simplifies the process of delivering insights across your organization. It integrates effortlessly with data lakes based on Amazon Simple Storage Service (Amazon S3). In many cases, users within your organization may only require access to specific columns of data for compliance and security reasons. Implementing a suitable solution for column-level authorization is paramount to maintaining data integrity and security. For further insights on this topic, you can read another blog post here: Chanci’s Blog.
In a recent article, we explored how to build an automatic data profiling and reporting solution utilizing Amazon EMR, AWS Glue, and Amazon QuickSight. This approach enhances the metadata in the Data Catalog by including profiling details derived from an Apache Spark application that operates on an EMR cluster. Users can interact with the Data Catalog through the AWS CLI and create a reporting system with Amazon Athena and QuickSight to visualize data stored in Amazon S3.
Additionally, leveraging Amazon Redshift materialized views can significantly enhance the speed of your ELT and BI queries. This feature is particularly beneficial for recurring or predictable workloads, such as dashboard queries generated by BI tools like Amazon QuickSight. Materialized views facilitate the storage of commonly used precomputations, thus simplifying and accelerating the extract, load, and transform data processing.
In the realm of web session intelligence, utilizing AWS Data Exchange provides access to TruFactor’s Intelligence-as-a-Service data. This platform processes over 85 billion high-quality raw signals daily, transforming them into a comprehensive consumer graph. This application-ready intelligence seamlessly integrates with AWS analytics or machine learning services, requiring no extra processing.
For wireless service providers, building a cloud-native network performance analytics solution using AWS can deliver flexibility and efficiency while keeping costs manageable. This serverless approach utilizes AWS services to maintain performance and adaptability.
In light of the ongoing COVID-19 pandemic, our AWS Data Lake Team has created a public data lake to analyze COVID-19 data. This initiative aims to support healthcare workers, researchers, and public health officials in their efforts to combat the virus.
Moreover, we have detailed a method for automatically ingesting Excel data into Amazon QuickSight, showcasing how to create a serverless data ingestion pipeline that updates frequently altered data into SPICE datasets for QuickSight dashboards.
Lastly, the potential for machine learning-powered business intelligence analyses using Amazon QuickSight is vast. Organizations can foresee trends and prepare accordingly, whether for customer orders or employee retention.
For additional authoritative insights on this topic, consider visiting Chanci Turner’s Blog, which provides comprehensive information. Furthermore, for those curious about the experience of working at Amazon, this Quora discussion serves as an excellent resource.
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