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Navigating AWS FinOps: Harnessing Cloud Intelligence Dashboards for Strategic Cost Optimization

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
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Exploring Time Travel Queries in Apache Hudi

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

Unlocking Business Potential with Data Engineering Services

  In today’s digital era, data is the key driver behind business growth and innovation. Data engineering services enable companies to handle vast amounts of raw data efficiently, transforming it into actionable insights. These services play a critical role in optimizing data pipelines, improving decision-making, and fostering innovation across industries. The Foundation of Data Engineering Data engineering is the process of designing, building, and managing the infrastructure and architecture that collects, stores, and analyzes data. It ensures that data flows seamlessly from its source to business applications, maintaining accuracy, reliability, and accessibility. With the explosion of data from diverse sources like IoT devices, cloud systems, social media, and customer interactions, businesses need well-structured data pipelines. These pipelines are essential for extracting, transforming, and loading ( ETL ) data into systems where it can be analyzed and utilized. The success of data

Getting Started with StreamLit: Build Interactive Data Apps in Python

Streamlit is an open-source Python library that simplifies the creation of interactive web apps for data science and machine learning projects. It is highly user-friendly, with minimal coding required to turn Python scripts into shareable web apps. It allows developers and data scientists to create interactive, visually appealing applications with minimal effort by focusing on writing Python code rather than dealing with front-end development.   KEY FEATURES Simplicity : You can build apps using just Python. There’s no need for HTML, CSS, or JavaScript.   Fast Development : With a few lines of code, you can create dashboards or web apps that automatically update as the Python script changes.   Interactive Widgets : Streamlit provides a range of widgets (e.g., sliders, buttons, textboxes) that make it easy to add interactivity to your app.   Data Visualizations : It integrates seamlessly with popular data visualization libraries like Matplotlib, Plotly, and Seaborn, allowing you to disp