The Cloud Intelligence Dashboards represent an open-source framework crafted and nurtured by a dedicated community of AWS enthusiasts. These dashboards are designed to deliver actionable insights and scalability for organizations, with a focus on customer satisfaction. The functionalities of these dashboards extend to fostering financial accountability, optimizing costs, monitoring usage goals, implementing governance best practices, and attaining operational excellence across all Well-Architected pillars. It includes multiple dashboards: CUDOS Dashboard Cost Intelligence Dashboard KPI Dashboard TAO Dashboard Compute Optimizer Dashboard Cost Anomaly Dashboard CUDOS Dashboard The CUDOS Dashboard offers comprehensive overviews and operational insights, allowing users to delve into resource-specific details. Users can discover automatically generated recommendations for cost optimization and actionable insights within the CUDOS Dashboard. These insights readily apply to FinOps practitione
Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an advanced data management framework designed to efficiently handle large-scale datasets. One of its standout features is time travel, which allows users to query historical versions of their data. This feature is essential for scenarios where you need to audit changes, recover from data issues, or simply analyze how data has evolved over time. In this blog post, we’ll walk through the process of setting up Hudi for time travel queries, using AWS Glue and PySpark for a hands-on example. 1. Getting Started: Importing Libraries and Creating Spark Context First, ensure you have all the necessary libraries in place. In this example, we’ll be using PySpark along with Hudi on AWS Glue notebook to manage data and run our queries. Make sure to import the relevant libraries and establish a Spark and Glue context before proceeding 2. Setting Up Your Hudi Table Before we can explore time travel queries, you need to set up a Hudi table whe