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Kubernetes from Basics to Guru

Kubernetes: From Basics to Guru

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1. Data processing frameworks
2. Batch and real-time streaming analytics
3. SQL versus NoSQL use cases and use case patterns
4. Enterprise data governance and metadata management

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Comprehensive Overview of Hadoop Ecosystem Components with Cloud Service Equivalents

Here's a concise table summarizing the key Hadoop ecosystem components along with their cloud service equivalents:

Component	Purpose	Created by	Language Support	Limitations	Alternatives	Fit	GCP Service	AWS Service	Azure Service
Apache Hive	SQL-like data querying in Hadoop.	Facebook	HiveQL	High latency for some queries.	P

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Apache Spark Learning Journey

This document outlines the structured content of my learning journey through Apache Spark, covering various topics from installation to advanced data processing techniques.

Course name: Spark Programming in Python for Beginners with Apache Spark 3

• Chapter 1: Apache Spark Introduction
• Chapter 2: Installing and Using Apache Spark
• Chapter 3: Spark Execution Model and Architecture

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• GCP PDF 1: Big Data and Machine Learning on Google Cloud

• GCP PDF 2: Data Engineering with Streaming Data

• Apache Data Processing Frameworks

• [Apache Spark Notes](https:

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Here's an updated and detailed table that categorizes the open-source projects related to Google's technologies, including those inspired by, created by, or driven by Google. I've added a category column to better classify each project based on its primary function or architecture:

Open Source Project	Description	Inspired by Google Technology	Category
Apache Hadoop	A framework for distributed storage and processing of large data sets on computer clusters using simple programming models.	Google's MapReduce and GFS (Google File System).	Distributed Processing
Apache Cassandra	A distributed NoSQL database designed to handle large amounts of data across many commodity servers.	Google's Bigtable, a distributed storage system for managing structured data.	NoSQL Database
Apache Beam	A unified programming model for defining and executing data processing pipelines, including ETL, batch, and s

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1.5 to 6.8

flowchart TD
    ES1_5("1. Elasticsearch 1.5\nUse migration plugin and snapshot") -->|Snapshot & restore| ES2_3("2. Elasticsearch 2.3\nRestore snapshot from 1.5\nReview

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Migration Assistant for Amazon OpenSearch Service Workshop

Prerequisite

• aws cli installed
• aws credentials configured
• Installed session manager plugin

brew install --cask session-manager-plugin

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Building Lightweight Web Apps with Flask, HTMX, and Alpine.js

Creating fast, mobile-friendly web applications is crucial in today's fast-paced digital world. Flask, a Python web framework, offers a minimalistic yet powerful platform for developers looking to build efficient applications. For the frontend, HTMX and Alpine.js provide modern, lightweight solutions for dynamic interfaces without the overhead of heavier frameworks. HTMX enhances HTML to support AJAX and other dynamic behaviors seamlessly, while Alpine.js brings the reactivity of Vue or React in a much smaller package. Both are designed to ensure your applications are responsive and perform well on mobile devices. Dive into their ecosystems with the provided tutorials and get your application running in no time.

• Flask:

  • Tutorial & Installation: Explore the official Flask Quickstart guide, which includes installation instructions and a basic application setup.

• HTMX:

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If you're getting a "command not found" error for Minikube, it means that Minikube is either not installed on your system or it's not added to your system's PATH. Here are the steps to install Minikube and start a local Kubernetes cluster:

Installing Minikube:

1. Download Minikube:

   On macOS, you can use Homebrew:

brew install minikube