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GCP Data Engineering

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

Example

The GCP Professional Data Engineer exam assesses your ability to:

  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality

Introduction

In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification. Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.

Theory

  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components

The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here

Practice

Tips for the exam & usefull links

References

About

Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...

Topics

clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform

Resources

Readme
Activity

Stars

1 star

Watchers

0 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Aug 26, 2024. It is now read-only.

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166 Commits
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NameName
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Repository files navigation
GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Aug 26, 2024. It is now read-only.

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Latest commit
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166 Commits
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NameName
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Theory
README.md
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Repository files navigation
GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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This repository was archived by the owner on Aug 26, 2024. It is now read-only.

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Latest commit
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166 Commits
Folders and files
NameName
Last commit message
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Cheatsheets
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Practice
Practice
Theory
Theory
README.md
README.md
Repository files navigation
GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Aug 26, 2024. It is now read-only.

BranchesTags
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166 Commits
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NameName
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Cheatsheets
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Other_ressources
Practice
Practice
Theory
Theory
README.md
README.md
Repository files navigation
GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Aug 26, 2024. It is now read-only.

BranchesTags
Latest commit
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166 Commits
Folders and files
NameName
Last commit message
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Cheatsheets
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Other_ressources
Practice
Practice
Theory
Theory
README.md
README.md
Repository files navigation
GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
Topics
clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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GCP Data Engineering
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Example
The GCP Professional Data Engineer exam assesses your ability to:
  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality
Introduction
In the Theory chapter are presented my personnal notes that summerize everything one need to know in order to get prepared for this exam/certification.
Then in the Practice section, you'll find all the capstone projects i've accomplished to master the different GCP components.
Theory
  1. All Services
  2. Selection of the appropriate Storage Technology
  3. Build a Storage System & Operations
  4. Design Data Pipelines
  5. Data Processing SolutionsTO BE CONTINUED
  6. Build an Instrastructure & Operations
  7. Security & ComplianceTO BE CONTINUED
  8. DB - Reliability, Scalability & Availability
  9. Flexibility & Portability
  10. ML Pipelines
  11. Choosing the Appropriate Insfrastructure
  12. Measure, Monitore & Troubleshoot ML
  13. Prebuilt ML Models as a Service
  14. Hadoop & Differences with GCP components
The M.L part is not developped here since i'm already familliar with all the Data Science concepts. Anyway you can refer to the Machine Learning cheatsheets for Stanford's CS 229 and a local backup here
Practice
Tips for the exam & usefull links
References
About
Notes aggregated from various sources in order to prepare the Google Cloud Platform Profesionnal Data Engineer Certification + Labs & Practice, tips, references...
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clouddata-engineergcp-componentsgoogle-cloudgoogle-cloud-platform
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