Clouds, Security and Enterprises

Amazon QuickSight is a commonly used business intelligence tool set that helps wide range of users to comprehend data by visualisation.  Log data from many sources, both from AWS services and non AWS services, can be streamed to Amazon QuickSight for data visualization, analytics, and for leveraging generative AI. This is a fairly typical use case of QuickSight.  But the quality of the Amazon QuickSight outputs for log data is influenced, or decided, before QuickSight even receives the data. This is because, often, log data has so much dimensions in it, or there are just simply so many fields, which significance is not equal. Insignificant data fields will significantly reduce the quality of the QuickSight visualisation and its readability. Some times, you also want to strip sensitive data before sending the data set for visualisation.  There are creative ways to improve the log data quality when streaming it to Amazon QuickSight using filtering and/or redacting.  Be...

Amazon S3 buckets can be used for storing large amount of data. AWS’ comprehensive tools on data analytics can then be leveraged for processing the data. Often, the post-analytics (processed) data is stored in another S3 bucket. Users can then access the processed data by fetching it from that S3 bucket.  This piece discusses a problem that was recently experienced on accessing the S3 buckets behind a Multi-Region Access Points setup and the problem solving experience.  The Setup A large amount of data is deposit into Amazon S3 service for analytics processing. The processed data is then stored in a different S3 bucket. A user base of around 10,000 accesses the processed data actively using a proprietary application. This application is latency sensitive. Most users are located in three geographical regions: Australia and New Zealand, North America and South America.  Due to the latency sensitive requirement, Amazon S3 Multi-Region Access Point setup has been used.  ...