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databricks-dataops-course

Course for doing databricks dataops, based on a data mesh monorepo structure

Preparation

  1. All members must get commit access to:
    • the repo from the teacher
    • the databricks training workspace

Purpose of the course

How can we deploy Databricks data pipelines in a way that is:

  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team
    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines

Repository structure

We will do our tasks in the context of the folder representing the revenue data pipeline or flow:

orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/

The structure is a proposal, which might have to be adapted in a real world organization.

The structure is:

  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)
          • flow: revenue

The structure will be applied to:

  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed

The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.

You can explore the structure here in Databricks, or more easily in the repo with a browser.

Longer explanation of the repo structure

A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.

Dataops libs

For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.

Reusing the structure and dataops libs

The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.

Course

  1. Go to course/
    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General
    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks
    • Some sections are just for reading or running, others you need to solve.

About

Course for doing databricks dataops, based on a data mesh monorepo structure

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

6 stars

Watchers

6 watching

Forks

139 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
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databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
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databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
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" + '

Skip to content
BranchesTags
Repository files navigation
databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
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
BranchesTags
Repository files navigation
databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
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
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databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
About
Course for doing databricks dataops, based on a data mesh monorepo structure
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
6 stars
Watchers
6 watching
Forks
139 forks
Report repository
Releases
Packages
Used by
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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databricks-dataops-course
Course for doing databricks dataops, based on a data mesh monorepo structure
Preparation
  1. All members must get commit access to:

    • the repo from the teacher
    • the databricks training workspace
Purpose of the course
How can we deploy Databricks data pipelines in a way that is:
  • (git-)versioned
  • usable
  • ordered and sustainable
  • enabling decentralized domain ownership for each data domain and team

    • this somehow enables data mesh-like principles
  • way of working for exploration, development, staging and production of pipelines
Repository structure
We will do our tasks in the context of the folder representing the revenue data pipeline or flow:
orgs/acme/domains/transport/projects/taxinyc/flows/prep/revenue/
The structure is a proposal, which might have to be adapted in a real world organization.
The structure is:
  • org: acme
    • domain: transport
      • project: taxinyc
        • flowtype: prep (meaning ETL/data engineering, the alternative is ml, for ML work)

          • flow: revenue
The structure will be applied to:
  • Data code, i.e. the pyspark code herein git
  • The database tables produced by that code
  • The data pipelines being deployed
The purpose of this structure is to have sufficient granularity to enable each department/org, team/domain, project and pipeline, to be kept apart.
You can explore the structure here in Databricks, or more easily in the repo with a browser.
Longer explanation of the repo structure
A longer explanation of the ideas behind the repo structure can be found in the article Data Platform Urbanism - Sustainable Plans for your Data Work.
Dataops libs
For the dataops code, we use the brickops package from Pypi, to enable a versioned pipeline deployment and way of working. The main logic is under dataops/deploy.
Reusing the structure and dataops libs
The structure and brickops libs can be used in your own projects, by forking the repo or copying the content and adapting it.
Course
  1. Go to course/

    • DO NOT run anything under course/00-Workshop-Admin-Prep
  2. Got to course/01-Student-Prep/01-General

    • Go through the instructions under that folder
  3. Start with the tasks under 02-DeployTasks

    • Some sections are just for reading or running, others you need to solve.
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Course for doing databricks dataops, based on a data mesh monorepo structure
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