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I Passed the AWS Certified Data Analytics – Specialty (DAS-C01) Exam!

I'm happy to share that I have successfully passed the AWS Certified Data Analytics – Specialty (DAS-C01) exam. It was a challenging journey, and I'd like to share some of the key resources that helped me prepare for this achievement.

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Great, let's create a React application called "Activity Logger" and host it using AWS Amplify with Git integration. Here’s a step-by-step guide:

Step 1: Set Up the React App

1. Install Node.js: Make sure you have Node.js and npm installed. You can download and install it from nodejs.org.

2. Create a New React App: Use Create React App to bootstrap your React project.

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Getting started with aws amplify

Sure, let's use AWS Amplify to set up and host your Live App. AWS Amplify simplifies the process of building, deploying, and hosting web applications. Here's a step-by-step guide to create and deploy a simple HTML-based Quip Live App using AWS Amplify.

Step 1: Set Up Your AWS Amplify Project

1. Install Amplify CLI:
   • Make sure you have Node.js and npm installed.

• Install the Amplify CLI globally.

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Hellow World App

tell application "Finder"
	display dialog "Hello World"
end tell

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Resize keynote to FHD (1920x1080)

tell application "Keynote"
	activate
	try
		set theDoc to front document
	on error
		set theDoc to make new document
	end try

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Here's a streamlined guide with highlighted key points for each major attraction in Mathura, focusing on the essential aspects and must-do activities, particularly from a Hindu cultural perspective:

1. Shri Krishna Janmabhoomi Temple

• Essentials: Birthplace of Lord Krishna, spiritual ceremonies.
• Must-Do: Participate in the evening aarti; it's a profound devotional experience.

2. Dwarkadhish Temple

• Essentials: Rich architecture, vibrant festivities.
• Must-Do: Attend the festival celebrations during Janmashtami for a lively and colorful experience.

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Certainly! Below is a README file you can use to guide yourself or others on setting up a Seafile Docker instance with data storage on an external drive (in this case, a Seagate external drive, which we'll refer to as ExtDrive). This document will include all necessary steps to ensure that Seafile uses this external drive for storing files.

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Setting Up Seafile on Docker with External Drive Storage

This README provides detailed instructions on how to set up Seafile using Docker, specifically configured to store data on an external Seagate drive named ExtDrive. Follow these steps to ensure your data is stored securely and externally from your main system.

Prerequisites

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Techniques to handle late arrival

Watermarks and Allowed Lateness are both vital techniques in managing late data in stream processing systems, but they serve slightly different purposes and are often used in conjunction to maximize data integrity and processing efficiency. Here’s an in-depth look at when and why you might choose to use each technique, or both together, along with real-world industry examples.

Watermarks

Purpose: Watermarks are primarily used to handle out-of-order data. They provide a way to estimate the "completeness" of data up to a certain point in time, based on event timestamps.

When to Use: Use watermarks when:

• You expect data to arrive out of order.
• You need a mechanism to know when to close a window and process its data.

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Here's the table sorted chronologically based on the release date of each Google Cloud service:

Google Cloud Service	Release Date	Based on/Open-source Inspiration	Open-source Start Date	Notes
Google BigQuery	2010	Dremel (Internal Google Tech)	N/A	BigQuery is inspired by Dremel but is not directly based on open-source technology.
Google Cloud Dataflow	2014	Apache Beam	2016 (as Apache Beam)	Initially developed by Google as Google Dataflow, then donated to the Apache Software Foundation as Apache Beam.
Google Cloud Composer	2018	Apache Airflow	2015	Developed by Airbnb and later open-sourced as Apache Airflow, which Google adopted for Cloud Composer.
Google Data Fusion	2019	CDAP (Cask Data Application Platform)	2011

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Data Warehouse Migration Story for FinTrust Bank

Framework Step	Details
Situation	FinTrust Bank, with an annual revenue of $12 billion, was facing a high-stakes challenge when its existing systems couldn't handle over 500 million transactions per day during a critical testing phase with a key e-commerce client. This client was projected to increase annual revenues by 15% ($1.8 billion). Key stakeholders involved were the client's CIO, CTO, and CSO, highlighting the strategic importance of the project.
Task	The urgent task was to stabilize and scale the bank’s data processing capabilities to not only retain the e-commerce client but also to set a foundation for scalable, compliant growth suitable for high-volume transaction environments.