├── .gitignore
├── 0_CLOUD-PATTERNS
├── 0_Starting-Points
│ ├── 3-STEPS-TO-START.md
│ ├── README.md
│ └── VENDORS.md
├── 1_Viz-Systems
│ ├── README.md
│ ├── VIZ-examples.md
│ ├── VIZ-four-evolve.md
│ └── Viz-Cloud-Genomics
│ │ └── README.md
├── 2_DevOps
│ ├── README.md
│ └── Terraform
│ │ ├── README.md
│ │ ├── main-aws.tf
│ │ ├── main-azure.tf
│ │ └── main-gcp.tf
├── 3_Machine-Learning
│ ├── 2_Matrices_for_data_scientists.ipynb
│ └── README.md
├── 4_Prompt-Katas
│ ├── Learn-More.md
│ ├── Prompt-tips.md
│ ├── Prompts.md
│ ├── README.md
│ └── gemini-for-google-workspace-prompting-guide-101.pdf
├── README.md
└── images
│ ├── RYG-progress.png
│ ├── aws-genomics-arch.png
│ ├── data-lake.png
│ ├── deployment-types-color.png
│ ├── deployment-types.png
│ ├── deploys.png
│ ├── genomic-pipeline.png
│ ├── lake-sketch.png
│ ├── modern-cloud-arch.png
│ ├── new-main.png
│ ├── research-pipelines.png
│ └── scale-jobs-arch.png
├── AWS
├── AWS-CLOUDLAKES.md
├── README.md
├── aws-courses.png
└── aws.png
├── AlibabaCloud
├── README.md
└── alibaba-cloud.png
├── Azure
├── Azure-CLOUDLAKES.md
├── README.md
└── azure.png
├── GCP
├── GCP-CLOUDLAKES.md
├── README.md
└── gcp.png
├── IBM
└── README.md
├── LICENSE
├── NVIDIA
└── README.md
├── README.md
├── images
├── 0-main.png
├── 1-main-gpt.png
├── 2a-ex-BQ.png
├── 2b-ex-Azure.png
├── 2c-ex-serverless-aws.png
├── LAMP-1.png
├── LAMP-2.png
├── LAMP-3.png
├── LAMP-4.png
├── alibaba-locations.png
├── aws-ci-cd.png
├── aws-locations.png
├── azure-locations.png
├── data-lakes
│ ├── CLOUDLAKES.md
│ ├── aws-cromwell.png
│ ├── aws-idseq.png
│ ├── aws-k8.png
│ ├── aws-nextflow.png
│ ├── aws-variantspark.png
│ ├── azure-cromwell.png
│ ├── cloud-k8.png
│ ├── excalidraw-files
│ │ ├── GCP-PAPI-k8.excalidraw
│ │ ├── aws-k8.excalidraw
│ │ └── cromwell.excalidraw
│ ├── gcp-cromwell.png
│ ├── gcp-deepvariant.png
│ ├── gcp-k8.png
│ ├── gcp-nextflow.png
│ └── raw-k8.png
├── databricks-icon.png
├── gcp-locations.png
├── gcp-networks.png
├── ibm-cloud.png
├── learning-cloud.png
├── logos.png
├── prompt-kata-group.png
├── terraform-arch.png
├── trends.png
└── viz-tools.png
└── z_utilities
├── .devcontainer
├── DOCKERFILE
└── devcontainer.json
├── .metals
└── metals.h2.db
├── .tours
├── aws-tour.tour
├── cloud-tour.tour
└── gcp-tour.tour
└── .vscode
└── settings.json
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 | *.db
131 |
--------------------------------------------------------------------------------
/0_CLOUD-PATTERNS/0_Starting-Points/3-STEPS-TO-START.md:
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1 | # 3 Steps To Get Started in Cloud--->
2 |
3 |
4 | 1. 🤔 **READ this Repo** and FAQ
5 | - this repo is a companion to my **'Cloud Careers and Certifications`** - [link](https://www.linkedin.com/learning/cloud-computing-careers-and-certifications-first-steps-14851328) course on LinkedIn Learning or Lynda.com course. It is designed to be a first course for those who are new working with public cloud services
6 | - ❓ FAQ - here are the top 10 questions students ask about `learning-cloud` - [link](https://github.com/lynnlangit/learning-cloud/tree/master/CLOUD-PATTERNS/0_Starting-Points)
7 | 2. 🤔 **SELECT a cloud vendor** (AWS, GCP...), set up a free trial (test) account for learning
8 | - See the main file in each vendor folders for links
9 | - for **AWS** go to `\AWS` folder -> `README.md`
10 | - for **GCP** go to `\GCP` folder -> `README.md`, etc...
11 | - Set up **MFA (multi-factor authentication)** on the root user account for your trial cloud account to protect against account misuse by others
12 | - setup up MFA on AWS root user account - [link](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_mfa_enable_virtual.html)
13 | - setup MFA on GCP user accounts - [link](https://www.trendmicro.com/cloudoneconformity/knowledge-base/gcp/CloudIAM/enable-mfa-for-user-accounts.html)
14 | - Learn more about **cloud vendor differences** - [link](https://github.com/lynnlangit/learning-cloud/blob/master/CLOUD-PATTERNS/0_Starting-Points/VENDORS.md)
15 | 3. 💸 **SECURE and SETUP a budget alert** on your test account - I suggest alerting on spend of over $ 50 USD per day
16 | - follow **best practices** for security a demo account (AWS example) - [link](https://dev.to/aws-heroes/stop-aws-account-hacks-1bim)
17 | - pick a **scenario** or service to start learning
18 | - build your project on the cloud and Learn...repeat!
19 |
20 | # --->THEN GO BUILD!
21 |
22 |
23 |
24 |
--------------------------------------------------------------------------------
/0_CLOUD-PATTERNS/0_Starting-Points/README.md:
--------------------------------------------------------------------------------
1 | # Learning Cloud FAQ - Top 10
2 |
3 | - Q: **Which cloud?**
4 | - A: Most overall jobs - AWS
5 | - A: Cheapest, most performant to learn/experiment - GCP
6 | - A: Fastest enterprise growth - Azure
7 | - A: Quantum - IBM
8 |
9 | ---
10 |
11 | - Q: **Which languages?**
12 | - A: Python (application development)
13 | - A: Terraform (infrastructure)
14 | ---
15 |
16 | - Q: **Where to start?**
17 | - A: Pick a vendor, setup a free trial account
18 | - A: Set up multi-factor authentication for your login (email)
19 | - A: Set up a billing alert, send to your email
20 | ---
21 |
22 | - Q: **Which service(s)?**
23 | - A: Core services are VMs, file storage (buckets) and security (AWS: EC2, S3, IAM....)
24 | ---
25 |
26 | - Q: **How to get a job?**
27 | - A: Build something, get certified, talk (post) about it - in this order
28 | ---
29 |
30 | - Q: **How do I avoid a surprise, big cloud bill when learning?**
31 | - A: Set a budget alert and notification
32 | - A: Turn off VMs when not using
33 | - A: Use smallest sized VMs/GPUs as is practical for testing
34 | ---
35 |
36 | - Q: **What are some 'overlooked' areas?**
37 | - A: Security, cost control - more jobs than qualified people
38 | ---
39 |
40 | - Q: **Can I do Gen AI / ML on the cloud?**
41 | - A: Most complete offering - GCP VertexAI
42 | ---
43 |
44 | - Q: **Which certifications get me the highest paying jobs?**
45 | - A: GCP
46 | ---
47 |
48 | - Q: **Whare are the most interesting/useful cloud data services by vendor?**
49 | - A: Azure - CoPilot(s), CosmosDB
50 | - A: AWS - S3, EC2, AWS Braket (Quantum)
51 | - A: GCP - BigQuery, VertexAI, DataProc (managed Apache Spark), Dataplex
52 | - A: IBM - Qiskit (Quantum)
53 |
--------------------------------------------------------------------------------
/0_CLOUD-PATTERNS/0_Starting-Points/VENDORS.md:
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1 | # How to Select a Cloud Vendor
2 |
3 | Selecting one or more cloud vendors is a key choice when adopting public cloud services. I have done most of my production work with AWS or GCP. However, for some scenarios, Azure or Alibaba Cloud are 'best fit' choices as well.
4 |
5 | The global market trends (shown below) can be seen as a starting point for you.
6 |
7 | 
8 |
9 | It is important to further refine your selection based on the following:
10 |
11 | - **services needed**
12 | - does a potential vendor offer your desired services in their cloud?
13 | - comparing service x vendor, which fits best?
14 | - example: does `AWS IoT` or `Azure IoT` better meet your needs?
15 | - **services location(s)**
16 | - does a potential vendor have your desired services in your preferred location(s)?
17 | - where are their data center(s)?
18 | - **community cloud skills**
19 | - what is the size of the total technical community for a potential vendor?
20 | - what is the size of the local technical community in your geography for a potential vendor?
21 |
22 | ---
23 |
24 | ## Vendor Characteristics
25 |
26 | ### GCP - DEVELOPER & DATA SCIENCE CLOUD
27 | - Positive - easy to set up for dev/experiements, cheap/fast & scalable, innovative ML services
28 | - Negative - Enterprise setup/support complex, idiosyncratic (i.e. 'Googly') service implementation
29 |
30 | ### AWS - DEVOPS CLOUD
31 | - Positive - market share/ partner ecosystm, huge variety of services
32 | - Negative - dated, security/cost management is arduous
33 |
34 | ### Azure - ENTERPRISE CLOUD
35 | - Positive - Active Directory integration simplifies security, core services are solid performers
36 | - Negative - expensive, must test scalability by service
37 |
38 | ### Alibaba Cloud - APAC CLOUD
39 | - Positive - good variety of services, well-priced
40 | - Negative - derivative of AWS, based in China (privacy concerns), requires US passport (or other ID) to setup account
41 |
42 | ### IBM Cloud - QUANTUM CLOUD
43 | - Positive - innovation in quantum computing - great tools, useful Quantum libraries and tools
44 | - Negative - expensive for general cloud service
45 |
46 |
47 |
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/0_CLOUD-PATTERNS/1_Viz-Systems/README.md:
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1 | # Cloud Systems Visualization
2 |
3 | Effectively visualizing cloud systems is key to not only building, but also growing and maintaining sytems. There are a number of tools and techniques to use.
4 | These include diagramming, drawing and connecting tools to existing (deployed) systems. I did a series of keynotes about this topic as well.
5 |
6 | ## Talks and Decks
7 |
8 | ### Viz Systems
9 | - 📺 Keynote: 2019 / 'Viz Cloud Systems' / GOTO: Berlin - [keynote](https://www.youtube.com/watch?v=HHitdmje1ok)
10 | - 📣 Slide deck: 2019 'Visualizing Cloud Systems' - [link](https://slides.com/lynnlangit/goto-viz-cloud-systems)
11 | - 📺 Talk: 2020 / 'Viz Cloud Systems' / GOTO: Chicago - [talk](https://www.youtube.com/watch?v=htmEA-dpX_4)
12 | - 📣 Slide deck: 2020 'Visualizing Cloud Systems' - [link](https://slides.com/lynnlangit/goto-visualizing-cloud-systems)
13 | - 📺 Talk: 2019 / 'Viz Cloud Systems' / Kandddinsky: Berlin - [talk](https://www.youtube.com/watch?v=DJdydx4g0v4)
14 | - 📣 Slide deck: 2020 'Cloud for Machine Learning' - [link](https://slides.com/lynnlangit/cloud-for-machine-learning)
15 |
16 | ### Serverless
17 | - 📺 Keynote: 2019 / 'Viz Serverless Systems' / ServerlessConf: London - [keynote](https://www.youtube.com/watch?v=eNmGQOAedQ4)
18 | - 📣 Slide deck: 2019 'Visualizing Serverless Systems' - [link](https://slides.com/lynnlangit/viz-cloud-systems-18)
19 | - 📺 Talk: 2019 / 'Serverless Architectures' / NDC: Minnesota - [talk](https://www.youtube.com/watch?v=od4mrgJ9wW8)
20 | - 📺 Talk: 2017 / 'Serverless Reality' / NDC: Oslo - [talk](https://www.youtube.com/watch?v=PgZ2dxnj734)
21 | - 📣 Slide deck: 2018 'Serverless SQL' - [link](https://slides.com/lynnlangit/serverless-sql-queries-10)
22 |
23 | ## Tools
24 | - 🖌️ Excalidraw: Hand-drawn looking diagrams - [link](https://excalidraw.com/)
25 | - 📣 Beautiful.ai: Stylized slides with diagrams - [link](https://www.beautiful.ai/)
26 | - 🛠️ Lucidcharts: general purpose diagramming tool, some AWS integration - [link](https://lucid.app/)
27 | - 🛠️ Mindmup (Mindmaps): maps and decision trees - [link](https://www.mindmup.com/)
28 | - 🛠️ Hava.io: live cloud diagrams, multiple system perspectives - [link](https://app.hava.io/)
29 | - 🛠️ Diagrams.com: cloud diagrams as code - [link](https://diagrams.mingrammer.com/docs/getting-started/examples)
30 | - 🛠️ Structurizr: Diagram code and systems - [link](https://structurizr.com/)
31 |
32 | ## My Techniques and Articles
33 | - 📐 **Sketching as a starting point** - example (GCP CI/CD pipeline) -- [link](https://acloudguru.com/blog/engineering/cloud-based-ci-cd-on-gcp)
34 | - 📐 **User Perspectives (Researcher, Lab Lead, ...)** - example (Terra.bio on GCP WDL Workflows) -- [link](https://lynnlangit.medium.com/cloud-native-hello-world-for-bioinformatics-3beb1ab820a)
35 | - 📐 **Just-enough / evolving diagrams** - example (Azure Genomics Pipelines) --[link](https://lynnlangit.medium.com/azure-for-genomic-scale-workloads-ad3c989a3d0b)
36 | - 📐 **Right-sizing** - example (VariantSpark on AWS pipeline -- [link](https://lynnlangit.medium.com/scaling-custom-machine-learning-on-aws-part-3-kubernetes-5427d96f825b)
37 | - 📐 **Connected / live diagrams** - see link to Hava.io
38 |
39 | ### Other Resources
40 | - 📖 Article: Technical Perspectives (Dev, DevOps, Security) - "The 5 Types of Architecture Diagrams" --[link](https://www.readysetcloud.io/blog/allen.helton/the-5-types-of-architecture-diagrams/)
41 | - 📖 Article: How to review a Software Architecture Diagram (from Simon Brown) -- [link](https://dev.to/simonbrown/how-to-review-a-software-architecture-diagram-6p0)
42 | - 📖 Post: Best Practices for Architecture Diagrams -- [link](https://sportebois.medium.com/better-architecture-diagrams-for-agile-teams-actionable-tips-and-lessons-e76627dc4315)
43 | - ⚙️ Standards: Fundamental Modeling Concepts (FMC), visualization guidelines -[link](http://www.fmc-modeling.org/visualization_guidelines)
44 |
45 | ---
46 |
47 | ## Example Visualizations
48 |
49 | See this repo page for cloud system visualization examples --> [link](https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/1_Viz-Systems/VIZ-examples.md)
50 |
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/0_CLOUD-PATTERNS/1_Viz-Systems/VIZ-examples.md:
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1 | # Cloud Systems Visualization Examples
2 |
3 | ## Talks and Decks
4 |
5 | See this page for a list with links --> [link](https://github.com/lynnlangit/learning-cloud/tree/master/CLOUD-PATTERNS/1_Viz-Systems)
6 |
7 | ---
8 |
9 | ## Example Visualizations
10 | The following examples illustrate various concepts in cloud systems visualization. Description plus tool used listed for each example.
11 |
12 | ### 1. Conceptual system for Transactional Website (Excalidraw)
13 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/modern-cloud-arch.png" width=800>
14 |
15 | ### 2. Conceptual design for Data Lake (Beautiful.ai)
16 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/data-lake.png" width=800>
17 |
18 | ### 3. Conceptual design for Genomic Data Pipeline (Beautiful.ai)
19 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/genomic-pipeline.png" width=800>
20 |
21 | ### 4. Cloud Pipeline implementation genomics choices (MindMup)
22 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/research-pipelines.png" width=1000>
23 |
24 | ### 5. Sketch of architectural pattern for Cloud Lake (Excalidraw)
25 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/lake-sketch.png" width=800>
26 |
27 | ### 6. Red-Yellow-Green DevOps progress (Lucidcharts)
28 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/RYG-progress.png" width=1000>
29 |
30 | ### 7. AWS Genomics Pipeline (Lucidcharts)
31 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/aws-genomics-arch.png" width=1000>
32 |
33 | ### 8. AWS CI-CD pipeline (Lucidcharts)
34 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/aws-ci-cd.png" width=1000>
35 |
36 | ### 9. GCP Bioinformatics Processes including DataMesh (Excalidraw)
37 | <img src="https://github.com/lynnlangit/gcp-for-bioinformatics/blob/master/images/batch-pipelines.png" width=1000>
38 |
39 | ### 10. GCP Scaling Jobs by Type (Lucidcharts)
40 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/scale-jobs-arch.png" width=1000>
41 |
42 |
43 |
44 | ---
45 |
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/0_CLOUD-PATTERNS/1_Viz-Systems/VIZ-four-evolve.md:
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1 | # Cloud System Visualization Evolving Example
2 |
3 | ## Concepts and Examples
4 |
5 | - See this page for a list with links --> https://github.com/lynnlangit/learning-cloud/blob/master/CLOUD-PATTERNS/VIZ-concepts.md
6 | - See this page for a list with examples --> https://github.com/lynnlangit/learning-cloud/blob/master/CLOUD-PATTERNS/VIZ-concepts.md
7 |
8 | ---
9 |
10 | ## Four Evolving Example Visualizations
11 | The following examples illustrate evolving a cloud architecture via visualization. Architecture moves from client/server to microservices.
12 |
13 | ### 1. AWS Client/Server
14 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/LAMP-1.png" width=800>
15 |
16 | ### 2. AWS Client/Server w/HA
17 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/LAMP-2.png" width=800>
18 |
19 | ### 3. AWS Serverless
20 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/LAMP-3.png" width=800>
21 |
22 | ### 4. AWS Serverless w/ CI-CD
23 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/LAMP-4.png" width=1000>
24 |
25 |
26 | ---
27 |
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/0_CLOUD-PATTERNS/1_Viz-Systems/Viz-Cloud-Genomics/README.md:
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1 | # Vizualize Cloud Genomic Pipelines
2 |
3 | Examples used in understand building Bioinformatics data analysis pipelines on the public cloud.
4 |
5 | ## Overview
6 |
7 | <img src="https://github.com/lynnlangit/TeamTeri/raw/master/Images/NGS-Workflow.png" width=600 align="right">
8 |
9 | Sequencing top level processes types are listed below and shown in the conceptual diagram:
10 | - Wet lab / sequencer - prepped samples are sequenced on a machine
11 | - **Primary analysis** pipeline of sequencer data - sequencer machine output files are analyzed
12 | - **Secondary analysis** pipeline of primary analysis data - primary analysis output files are analyzed
13 | - **Teritary analysis** pipeline of secondary analysis data - secondary analysis output files are analyzed
14 | - Data storage and access - this includes genomic reference datasets, along with storage of genomic files all along these processes
15 |
16 | High level processes diagram shown to the right:
17 |
18 |
19 |
20 | ---
21 |
22 | ## Bio Tools Cloud Options
23 |
24 | Shown below are 5 different patterns (on GCP) for deploying the COMPUTE layer to support scalable bioinformatics tools. Simplified data lake (bucket) pattern is shown for the DATA layer.
25 |
26 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/viz-tools.png" width=1000>
27 |
28 | ### GCP Bioinfomatics
29 |
30 | Info about genomic pipeline architectures - [link](https://github.com/lynnlangit/gcp-for-bioinformatics/blob/master/6_ARCHITECTURE.md) and shown below. Example shows 'vision-on-a-page' for primary, secondary and tertiary cloud-based bioinformatics data analysis. Example uses simplified representation of DATA layer and focuses on COMPUTE layer concepts for this reference architecture.
31 |
32 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/new-main.png" width=1000>
33 |
34 |
35 |
36 |
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/0_CLOUD-PATTERNS/2_DevOps/README.md:
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1 | # DevOps Patterns
2 |
3 | Cloud DevOps Patterns center around cloud service infrastructure deployment tools, types and patterns.
4 |
5 | There are many choices.
6 | The diagram below summarizes different ways I work with clients to deploy cloud solutions.
7 |
8 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/deploys.png" width=800>
9 |
10 |
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/0_CLOUD-PATTERNS/2_DevOps/Terraform/README.md:
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1 | # Terraform Basics
2 |
3 | (From the Terraform docs...)
4 | - *"Terraform is an **infrastructure as code tool** that lets you define and manage infrastructure resources through human-readable configuration files.*
5 | - *It allows you to use a consistent workflow over your infrastructure lifecycle, regardless of the resource provider. (supports AWS, Azure, GCP and more)*
6 | - *Infrastructure as code workflows let you declaratively manage a variety of services and automate your changes to them, reducing the risk of human error through manual operations."*
7 |
8 | - Concise article "What is Terraform?" --> https://medium.com/nerd-for-tech/nfrastructure-as-code-using-terraform-cb383017cbeb
9 |
10 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/terraform-arch.png" width=800>
11 |
12 | ## Using Terraform
13 |
14 | Basic commands are as follows:
15 | - create a `main.nf` file in the current directory (example shown in this directory)
16 | - run these commands
17 | - `terraform init` to initialize the project
18 | - `terraform plan` to plan the changes
19 | - `terraform apply` to apply the changes
20 | - `terraform destroy` to destroy the project
21 |
22 | ## Terraform Concepts
23 |
24 | There are a number of patters/concepts to consider/include:
25 | - Terraform Modules
26 | - Terraform Workspaces
27 | - Variable Files (tfvars)
28 | - Secrets storage / maangement (integrate w/provider, i.e. AWS Secrets Manager, etc...)
29 | - Testing - Terratest libraries
30 | - Terraform CDK (use programming languages to generate TF files)
31 | - CI/CD env - Terraform Cloud or provider (i.e. AWS Code Deploy, Code Build, Code Commit, etc...)
32 |
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/0_CLOUD-PATTERNS/2_DevOps/Terraform/main-aws.tf:
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1 | provider "aws" {
2 | region = "us-east-1"
3 | # access_key = "xxx"
4 | # secret_key = "yyg"
5 | }
6 |
7 | resource "aws_bucket" "my_first_bucket" {
8 | bucket = "my-first-bucket"
9 | acl = "private"
10 | versioning {
11 | enabled = true
12 | }
13 | }
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/0_CLOUD-PATTERNS/2_DevOps/Terraform/main-azure.tf:
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1 | //create a terraform main file that creates a virtual machine in azure
2 | provider "azurerm" {
3 | version = "=2.0.0"
4 | features {}
5 | }
6 | resource "azurerm_resource_group" "my_resource_group" {
7 | name = "my-resource-group"
8 | location = "West Europe"
9 | }
10 | resource "azurerm_virtual_network" "my_virtual_network" {
11 | name = "my-virtual-network"
12 | address_space = ["address_space"]
13 | location = azurerm_resource_group.my_resource_group.location
14 | resource_group_name = azurerm_resource_group.my_resource_group.name
15 | }
16 | resource "azurerm_subnet" "my_subnet" {
17 | name = "my-subnet"
18 | resource_group_name = azurerm_resource_group.my_resource_group.name
19 | virtual_network_name = azurerm_virtual_network.my_virtual_network.name
20 | address_prefixes = ["address_prefixes"]
21 | }
22 | resource "azurerm_network_interface" "my_network_interface" {
23 | name = "my-network-interface"
24 | location = azurerm_resource_group.my_resource_group.location
25 | resource_group_name = azurerm_resource_group.my_resource_group.name
26 | ip_configuration {
27 | name = "my-ip-configuration"
28 | subnet_id = azurerm_subnet.my_subnet.id
29 | private_ip_address_allocation = "Dynamic"
30 | }
31 | }
32 | resource "azurerm_public_ip" "my_public_ip" {
33 | name = "my-public-ip"
34 | location = azurerm_resource_group.my_resource_group.location
35 | resource_group_name = azurerm_resource_group.my_resource_group.name
36 | allocation_method = "Dynamic"
37 | }
38 | resource "azurerm_network_interface_backend_address_pool_association" "my_network_interface_backend_address_pool_association" {
39 | network_interface_id = azurerm_network_interface.my_network_interface.id
40 | ip_configuration_name = azurerm_network_interface.my_network_interface.ip_configuration[0].name
41 | backend_address_pool_id = azurerm_lb_backend_address_pool.my_backend_address_pool.id
42 | }
43 | resource "azurerm_lb" "my_lb" {
44 | name = "my-lb"
45 | location = azurerm_resource_group.my_resource_group.location
46 | resource_group_name = azurerm_resource_group.my_resource_group.name
47 | frontend_ip_configuration {
48 | name = "my-frontend-ip-configuration"
49 | public_ip_address_id = azurerm_public_ip.my_public_ip.id
50 | }
51 | }
52 | resource "azurerm_lb_backend_address_pool" "my_backend_address_pool" {
53 | name = "my-backend-address-pool"
54 | resource_group_name = azurerm_resource_group.my_resource_group.name
55 | loadbalancer_id = azurerm_lb.my_lb.id
56 | }
57 | resource "azurerm_lb_nat_pool" "my_lb_nat_pool" {
58 | name = "my-lb-nat-pool"
59 | resource_group_name = azurerm_resource_group.my_resource_group.name
60 | location = azurerm_resource_group.my_resource_group.location
61 | loadbalancer_id = azurerm_lb.my_lb.id
62 | protocol = "Tcp"
63 | frontend_port_start = 50000
64 | frontend_port_end = 50119
65 | backend_port = 22
66 | }
67 | resource "azurerm_virtual_machine" "my_virtual_machine" {
68 | name = "my-virtual-machine"
69 | location = azurerm_resource_group.my_resource_group.location
70 | resource_group_name = azurerm_resource_group.my_resource_group.name
71 | network_interface_ids = [azurerm_network_interface.my_network_interface.id]
72 | vm_size = "Standard_B1ls"
73 | delete_os_disk_on_termination = true
74 | delete_data_disks_on_termination = true
75 | storage_image_reference {
76 | publisher = "Canonical"
77 | offer = "UbuntuServer"
78 | sku = "18.04-LTS"
79 | version = "latest"
80 | }
81 | storage_os_disk {
82 | name = "my-os-disk"
83 | caching = "ReadWrite"
84 | create_option = "FromImage"
85 | managed_disk_type = "Standard_LRS"
86 | }
87 | os_profile {
88 | computer_name = "my-computer-name"
89 | admin_username = "my-admin-username"
90 | admin_password = "my-admin-password"
91 | }
92 | os_profile_linux_config {
93 | disable_password_authentication = false
94 | }
95 | boot_diagnostics {
96 | storage_account_uri = azurerm_storage_account.my_storage_account.primary_blob_endpoint
97 | }
98 | }
99 | resource "azurerm_storage_account" "my_storage_account" {
100 | name = "my-storage-account"
101 | resource_group_name = azurerm_resource_group.my_resource_group.name
102 | location = azurerm_resource_group.my_resource_group.location
103 | account_tier = "Standard"
104 | account_replication_type = "LRS"
105 | enable_https_traffic_only = true
106 | }
107 | resource "azurerm_storage_container" "my_storage_container" {
108 | name = "my-storage-container"
109 | storage_account_name = azurerm_storage_account.my_storage_account.name
110 | container_access_type = "private"
111 | }
112 | resource "azurerm_storage_share" "my_storage_share" {
113 | name = "my-storage-share"
114 | storage_account_name = azurerm_storage_account.my_storage_account.name
115 | quota = 50
116 | }
117 | resource "azurerm_storage_share_directory" "my_storage_share_directory" {
118 | name = "my-storage-share-directory"
119 | storage_share_name = azurerm_storage_share.my_storage_share.name
120 | storage_account_name = azurerm_storage_account.my_storage_account.name
121 | }
122 | resource "azurerm_storage_share_file" "my_storage_share_file" {
123 | name = "my-storage-share-file"
124 | storage_share_name = azurerm_storage_share.my_storage_share.name
125 | storage_account_name = azurerm_storage_account.my_storage_account.name
126 | source = "source"
127 | }
128 | resource "azurerm_storage_blob" "my_storage_blob" {
129 | name = "my-storage-blob"
130 | storage_account_name = azurerm_storage_account.my_storage_account.name
131 | storage_container_name = azurerm_storage_container.my_storage_container.name
132 | type = "Block"
133 | source = "source"
134 | }
135 | resource "azurerm_storage_queue" "my_storage_queue" {
136 | name = "my-storage-queue"
137 | storage_account_name = azurerm_storage_account.my_storage_account.name
138 | }
139 | resource "azurerm_storage_table" "my_storage_table" {
140 | name = "my-storage-table"
141 | storage_account_name = azurerm_storage_account.my_storage_account.name
142 | }
143 |
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/0_CLOUD-PATTERNS/2_DevOps/Terraform/main-gcp.tf:
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1 | provider "gcp" {
2 | region = "us-central1"
3 | project = "my-test-project"
4 | }
5 |
6 | resource "google_compute_instance" "my_test_instance" {
7 | name = "my-test-instance"
8 | machine_type = "f1-micro"
9 | zone = "us-central1-a"
10 |
11 | boot_disk {
12 | initialize_params {
13 | image = "debian-cloud/debian-9"
14 | }
15 | }
16 |
17 | network_interface {
18 | network = "default"
19 |
20 | access_config {
21 | // Include this section to give the instance a public IP address
22 | public_ip = "true"
23 | }
24 | }
25 | }
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/0_CLOUD-PATTERNS/3_Machine-Learning/README.md:
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1 | # ML Patterns
2 |
3 | ML (Machine Learning), also called AI (Artificial Intelligence) is a set of services and tools. Each cloud vendor offers a mixture of these items.
4 |
5 | ## ML Basics
6 |
7 | If you are new to ML, core libraries and ML algorithm visualization sites can speed up learning.
8 | - Interactive Visualizations at 'MLU-Explain' - https://mlu-explain.github.io/
9 | - Core Python learning ML library of 'sci-kit learn' - https://scikit-learn.org/
10 | - Google AI, ML education site - https://ai.google/education/
11 |
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/0_CLOUD-PATTERNS/4_Prompt-Katas/Learn-More.md:
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1 | # Learn More about Cloud (and Code) Prompting
2 |
3 | - "The Illustrated Transformer" --> https://jalammar.github.io/illustrated-transformer/
4 | - Fun dots Gemini Demo --> https://huggingface.co/spaces/Trudy/gemini-realtime-dots
5 | - Live Coding with Gemini --> https://huggingface.co/spaces/Trudy/gemini-live-p5
6 |
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/0_CLOUD-PATTERNS/4_Prompt-Katas/Prompt-tips.md:
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1 | # Prompt Tips
2 |
3 | ## Follow the standard format
4 | - As a 'who the LLM is'
5 | - Using 'provide context'
6 | - Generate 'describe output in detail' as 'describe output format` (i.e. list, table, etc...)
7 | - Limit output values, i,e `Answer with <valueA> or <valueB> only`
8 | - For a 'who is the output audience'
9 |
10 | ## Improvements
11 | - Work on one task at a time
12 | - Do not skip steps
13 | - Show your reasoning
14 | - Cite sources including URLs
15 | - If you can not generate cited information do not guess, say 'I can not find relavent information'
16 | - Provide examples of desired output
17 |
18 | ## Tone
19 | - Be specific
20 | - Use active voice
21 | - Use 'West Coast' US English
22 | - Does politeness matter? i.e. 'please, thanks, good job...' etc
23 |
24 | ## Pre-RAG Augmentation
25 | - 'Trial' RAG via huge context windows (Gemini 2M tokens)
26 | - Provide URLs -or- directly upload PDFs
27 |
28 | # For Specific LLMs
29 |
30 | ## Gemini for Google Cloud
31 | - Prompt Guidance --> https://cloud.google.com/gemini/docs/discover/write-prompts
32 |
33 | ## Google Gemini API
34 | - Prompt Guidance --> https://ai.google.dev/gemini-api/docs/prompting-strategies
35 |
36 | ## Google Imagen
37 | - Prompt Guidance --> https://ai.google.dev/gemini-api/docs/imagen-prompt-guide
38 |
39 |
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/0_CLOUD-PATTERNS/4_Prompt-Katas/Prompts.md:
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1 | # List of Prompts
2 |
3 | Problems/ tools / prompts
4 |
5 | ## Date
6 |
7 | ### March 25, 2025
8 | - Problem / Task: **Test the impact of politeness**
9 | - Test prompt: "Prompt: You are an expert in creating effective prompts for <LLM>. Do these tasks one at a time.
10 | Do not skip steps. The first task is to explain the impact of including 'polite words' such as 'please' and 'thank you' in prompts. Return the results in a list.
11 | The second task is to provide an two contrastive examples of a complex prompt and generated results: one includes "polite words" and the other does not. Return the results in a list as well.
12 | - Tested with Google Gemini, ChatGPT and Claude.ai - most significant impact noted in Claude.ai output.
13 |
14 | ### For March 20, 2025
15 | - Problem/Task(s): **Slurm-to-Batch/Singularity(Apptainer) to Docker**
16 | 1. Backgrounder: *You are an expert in application container architecture and implementation. Generate 5 key steps to translate existing Singularity container to Docker containers to be run on Google Cloud using the Google Batch service. Produce the output in a table.*
17 | 2. Add context: *You are a Cloud Architect who specializes in designing and building proof of concept architectures which include best practices for Google Cloud.*
18 | 3. Add transparency: <Add to prompt> *Work step-by-step and show your work. Cite URL references. Rank output steps in priority order.*
19 | 4. Try different models: Try with experiemental and thinking models
20 | 5. Try different model params: reduce Temperature, add grounding with Google search
21 | 6. Try different tools within Google ecosystem, try Claude, try Perplexity
22 | 7. If time, switch back to my account and show GitHub Copilot Workspaces
23 | 8. RESEARCH prompt: *Show me the 5 most important papers that contributed to application container technology standards, i.e. singularity, docker. Provide citations*
24 |
25 | ## Backlog / Ideas
26 |
27 | ### Cloud Tasks
28 |
29 | ### Architecture
30 | - Update diagram per client requirments
31 | - Build deployment script for cloud x
32 |
33 | #### Docker Containers
34 | - Describe required Docker dev environment and provide setup instructions
35 | - Create a Dockerfile
36 | - Improve a Dockerfile
37 | - Build/deploy/test container image locally
38 | - Suggest security remidiation methods for container image
39 | - List build options for a cloud
40 | - Create deployment script
41 | - Translate a Singularity (or Apptainer) container to a Docker container
42 |
43 | ### Human Health Tasks
44 |
45 | #### Bioinformatics
46 | - Summarize published research on topic x - DeepResearch
47 | - Segment image (cellular / nuclear)
48 | - Predict protein structure - AlphaFold
49 |
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/0_CLOUD-PATTERNS/4_Prompt-Katas/README.md:
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1 | # Cloud Prompt Katas
2 |
3 | Concept: Use cloud tasks as a basis for testing quality of output of GenAI tool prompts.
4 |
5 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/prompt-kata-group.png" width=800>
6 |
7 | ## Concepts / Test Kata Problems
8 | - derive from tool testing
9 | - derive from problem/tasks
10 | - derive from 'found' prompts
11 |
12 | ### GenAI / LLM Exploration Concepts
13 | Katas for practice/play/learning w/Prompts as 'code'
14 | - Iteration on exact prompt langauge as 'params'
15 | - Attention to tool-based prompt guidance/automated prompt re-writing
16 | - Big (general purpose) / small (domain-specific) LLM comparisons
17 | - Tool (GUI) vs API calls
18 | - Improvements via examining output from 'thinking' LLM models
19 |
20 | ### Validation
21 | - HITL - domain expertise as validation
22 | - Advsersarial
23 | - different models settings within tool
24 | - different models within tool
25 | - different tools
26 | - Automated
27 | - methods of automated, quantified output quality validation
28 |
29 | ## List of Candidate GenAI Tools
30 |
31 | ### General Purpose
32 | - ChatGPT / OpenAI
33 | - Google Gemini
34 | - Claude / Anthropic
35 |
36 | ### Domain-specific
37 | - Code: GitHub CoPilot & Copilot Repositories
38 | - Code: Repl.it
39 | - Cloud: GCP Gemini
40 | - Cloud: Amazon Q
41 | - Images: Google Imagen3
42 |
43 | - Medical Images: BioMedCLIP (open source foundational LLM)
44 | - Video: Google Veo2
45 | - Data: Databricks Genie, DBRX
46 |
47 | ### New Search Engines
48 | - Perplexity (multiple models)
49 |
50 | ### Ethical 'no's
51 | - DeepSeek
52 | - Llama
53 |
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/0_CLOUD-PATTERNS/README.md:
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1 | # Patterns
2 |
3 | ### Core Cloud
4 |
5 | <kbd><img src="https://miro.medium.com/max/1100/1*qXdSJxH7DtetjDlGQRyFgA.png" width=800></kbd>
6 |
7 | Core Cloud Patterns and design considerations, summarized below with a checklist, article and talk.
8 |
9 | - ✅ My Checklist: **"What is the Current State of Your Cloud Deployments?"** - do a checkup of your cloud env to get a baseline - [link](https://lynnlangit.medium.com/10-legacy-cloud-considerations-44b2a5073706?sk=75a729b527de05fa13103a913c9a45db)
10 | - :book: My Article: **"6 Cloud Adoption Patterns"** - particularly applies to enterprise-sized companies - [link](https://lynnlangit.medium.com/cloud-adoption-patterns-d47ffc5789fe)
11 | - :tv: My Talk: **"Cloud Adoption Patterns"** - expanded talk-version of article above - [link](https://www.youtube.com/watch?v=_su5lPuENNo)
12 |
13 | ### 4 Steps to Your First Cloud Native Applicattion
14 | 1. Your_Code ➡️ Your_App ? (add DevOps or 'infrastructure as code')
15 | 2. Your_Methods ➡️ Your_App ? (use Microservices / Functions or 'serverless')
16 | 3. Your_Data as Streams | CRUD | Batches ➡️ Data Methods? (select data store or SQL | NoSQL | DataLake [buckets])
17 | 4. CODE + DATA + OTHER_SERVICES ➡️ Cloud Native App Services? (implement 'other cloud service' or 'security [IAM]...')
18 |
19 | ### What are *Other* Cloud Service Types?
20 |
21 | - 🔐 Users | Accounts - Roles & Permissions - IAM comparison blog post - [link](https://ermetic.com/blog/cloud/aws-azure-and-gcp-the-ultimate-iam-comparison/)
22 | - 💳 Costs - Billing & Control
23 | - 📁 App Organization - Folders & Projects
24 | - 🔥 Up Time - Monitoring & Alerts
25 | - 🏗️ Updating - CI/CD
26 | - 📚 Data - Data Mesh - [link](https://www.datamesh-architecture.com/)
27 | - ✨ Advanced Processing - ML / AI
28 |
29 | ---
30 |
31 | ## End-to-end Example
32 |
33 | Shown below are the building blocks of a modern cloud architecture
34 |
35 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/0_CLOUD-PATTERNS/images/modern-cloud-arch.png" width=800>
36 |
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/AWS/AWS-CLOUDLAKES.md:
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1 | # Patterns for AWS Cloud Data Lakes
2 |
3 | ## Raw Kubernetes
4 |
5 | The reference pattern...
6 |
7 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/cloud-k8.png">
8 |
9 | ## Managed Kubernetes / EKS
10 |
11 | AWS includes the [`Elastic Kubernetes Service`](https://aws.amazon.com/eks/) as a container cluster controller. EKS can be configured to use an auto-scaling service. Also `AWS Batch` (see below) can be used.
12 |
13 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/aws-k8.png">
14 |
15 |
16 | ---
17 | ## AWS Genomics
18 |
19 | ### GATK, CROMWELL & BATCH
20 |
21 | AWS includes the [AWS Batch service](https://aws.amazon.com/batch/) for burstable genomic-scale data pipelines. Pattern is shown below for the GATK analysis toolkit.
22 |
23 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/aws-cromwell.png">
24 |
25 | ### IDSeq & Batch
26 |
27 | AWS includes the [AWS Batch service](https://aws.amazon.com/batch/) for burstable genomic-scale data pipelines. Pattern is shown below for the [IDSeq](https://www.discoveridseq.com/) toolkit.
28 |
29 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/aws-idseq.png">
30 |
31 |
32 | ### Nextflow & Batch
33 |
34 | AWS includes the [AWS Batch service](https://aws.amazon.com/batch/) for burstable genomic-scale data pipelines. Pattern is shown below for a Nextflow analysis toolkit.
35 |
36 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/aws-nextflow.png">
37 |
38 | ### VariantSpark & Batch
39 |
40 | AWS includes the [AWS Batch service](https://aws.amazon.com/batch/) for burstable genomic-scale data pipelines. Pattern is shown below for the VariantSpark toolkit.
41 |
42 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/aws-variantspark.png">
43 |
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1 | # AWS Resources
2 |
3 | ## My Courses on LinkedIn Learning
4 |
5 | 📺 My AWS courses on LinkedIn Learning - [link](https://www.linkedin.com/learning/search?entityType=COURSE&keywords=aws%20%2B%20lynn%20langit) - **18 courses!**:
6 | - **AWS for DevOps for Beginners** - best practices, core concepts by example - [link](https://www.linkedin.com/learning/aws-devops-best-practices-for-beginners)
7 | - **AWS for Data Series**: Machine Learning, Data Analytics, Data Services, Data Security
8 | - **AWS for DevOps Series**: AWS Controlling Cost- [link](https://www.linkedin.com/learning/amazon-web-services-controlling-cost) , Monitoring, Metrics & Logging, High Availability & Elasticity, Continuous Delivery & Process, Security, Governance & Validation, Amazon Workspaces
9 | - **AWS Architects Series**: Advanced Security, High Availability & Continuous Delivery, Network & Storage Design; Design & Implement Systems
10 | - **Big Data courses**: Learning Hadoop and Spark - [link](https://github.com/lynnlangit/learning-hadoop-and-spark), Learning NoSQL - [link](https://github.com/lynnlangit/learning-nosql) - includes AWS content
11 | - **End-user Computing course**: Learning Amazon Workspaces - [link](https://www.linkedin.com/learning/amazon-workspaces-deploy-virtual-desktops-14472889)
12 |
13 | ## My Other AWS Resources
14 |
15 | My AWS notes/links/sample code in GitHub Repos and other writings and screencasts (on YouTube)
16 |
17 | - :star: updating in 2024 - open source course **"aws-for-bioinformatics"** on YouTube/GitHub - [link](https://github.com/lynnlangit/aws-for-bioinformatics)
18 | - :octocat: My example AWS GitHub Repos
19 | - **'hello-aws-data-services'** - [link](https://github.com/lynnlangit/Hello-AWS-Data-Services)
20 | - **'aws-cost-control'** - [link](https://github.com/lynnlangit/aws-cost-control)
21 | - **'learning-hadoop-and-spark'** - [link](https://github.com/lynnlangit/learning-hadoop-and-spark)
22 | - **'learning-nosql'** - [link](https://github.com/lynnlangit/learning-nosql)
23 | - **'learning-amazon-workspaces'** - [link](https://github.com/lynnlangit/learning-amazon-workspaces)
24 | - 📚 My Medium AWS Articles - [link](https://medium.com/search?q=aws%20langit)
25 | - 📺 My YouTube AWS Serverless Playlist for **serverless AWS** - [link](https://www.youtube.com/playlist?list=PL4Q4HssKcxYsa2A2D2_Zln2tkL4v4-ymO)
26 | - 📺 My YouTube general AWS Playlist for **general AWS** - [link](https://www.youtube.com/playlist?list=PL93B06369FAD34284)
27 | - 🏆 AWS Community Hero (for Data) - [link](https://aws.amazon.com/developer/community/heroes/lynn-langit/?did=dh_card&trk=dh_card)
28 |
29 | ---
30 |
31 | ## Top Open Source AWS Resources
32 |
33 | - :octocat: Open Guide for AWS on GitHub - [link](https://github.com/open-guides/og-aws)
34 | - 📚 short article: 'how to get started learning AWS - [link](https://dev.to/loujaybee/where-and-how-to-start-learning-aws-as-a-beginner-27ab)
35 | - :octocat: How they AWS - [link](https://github.com/upgundecha/howtheyaws)
36 | - :octocat: Awesome AWS Security - [link](https://github.com/jassics/awesome-aws-security)
37 | - 📚 AWS Architecture Center, includes reference architectures - [link](https://aws.amazon.com/architecture)
38 |
39 | ### AWS Certification Information
40 |
41 | - List of certifications for AWS:
42 | - Main certification site - [link](https://aws.amazon.com/certification/)
43 | - Cert Prep site - [link]( https://aws.amazon.com/certification/certification-prep/)
44 |
45 | 
46 |
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1 | # Alibaba Cloud Resources
2 |
3 | 
4 |
5 |
6 | - 📺 My course **'Learning Alibaba Cloud'** (2 hours) - [link](https://www.linkedin.com/learning/learning-alibaba-cloud). Learning objectives:
7 | - Setting up an Alibaba Cloud account
8 | - Creating and configuring VMs
9 | - Creating and configuring container clusters
10 | - Creating functions
11 | - Creating storage buckets and databases
12 | - Managing resources with Cloud Shell
13 | - Accessing the Marketplace
14 | - Using the API Explorer console and templates
15 | - :octocat: My companion Repo
16 | - **'Learning Alibaba Cloud'** - [link](https://github.com/lynnlangit/learning-alibaba-cloud)
17 | - Alibaba Cloud Community Builders - [link](https://www.alibabacloud.com/campaign/communitybuilder)
18 |
19 | ---
20 |
21 | ## Other Resources & Certification Info
22 |
23 | - List of certifications for Alibaba Cloud - main [link](https://edu.alibabacloud.com/certification)
24 |
25 | 
26 |
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1 | # Patterns for Azure Cloud Data Lakes
2 |
3 | ## Raw Kubernetes
4 |
5 | The raw pattern...
6 |
7 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/cloud-k8.png">
8 |
9 |
10 | ---
11 | ## Azure Genomics
12 |
13 | Azure includes the [`Azure Kubernetes Service`](https://azure.microsoft.com/en-us/services/kubernetes-service) as a container cluster controller. Azure also includes the [`Azure Batch Service`](https://docs.microsoft.com/en-us/azure/batch/batch-technical-overview).
14 |
15 | See our article on Medium ['Azure Genomics w/cromwell'](https://lynnlangit.medium.com/azure-for-genomic-scale-workloads-ad3c989a3d0b). Note current implementation uses the [TES-Azure API](https://github.com/microsoft/tes-azure) to 'connect' cromwell with Azure Batch service.
16 |
17 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/azure-cromwell.png">
18 |
19 |
20 |
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/Azure/README.md:
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1 | # Azure Resources
2 |
3 | - 📺 My course **'Azure Databricks'** (3 hours) - [link](https://www.linkedin.com/learning/azure-spark-databricks-essential-training). Learning Objectives:
4 | - Business scenarios for Apache Spark
5 | - Setting up a cluster
6 | - Using Python, R, and Scala notebooks
7 | - Scaling Azure Databricks workflows
8 | - Data pipelines with Azure Databricks
9 | - Machine learning architectures
10 | - Using Azure Databricks for data warehousing
11 | - :octocat: Link to example Azure Databricks notebooks - [link](https://github.com/lynnlangit/learning-hadoop-and-spark/tree/master/5-Use-Spark/Jupyter-Notebooks/azure_databricks_notebooks)
12 | - My example GitHub Repos
13 | - :octocat: **'learning-hadoop-and-spark'** - [link](https://github.com/lynnlangit/learning-hadoop-and-spark)
14 | - :octocat: **'Learning Cromwell on Azure'** [link](https://github.com/lynnlangit/learning-cromwell-on-azure)
15 | - :octocat: **'learning Github Codespaces'** - [link](https://github.com/lynnlangit/learning-codespaces)
16 | - :octocat: **'learning-quantum'** - [link](https://github.com/lynnlangit/learning-quantum/tree/main/2_cloud-vendors/azure-quantum) learning Q# and more
17 | - 📖 My Medium Articles - [link](https://medium.com/search?q=azure%20langit)
18 | - 🏆 My Microsoft Regional Director award - [link](https://rd.microsoft.com/en-us/lynn-langit)
19 |
20 | ---
21 |
22 | ## Other Resources & Certification Info
23 |
24 | - Azure Cheat Sheet - list of all Azure services on one page - [link](https://github.com/milanm/azure-cheat-sheet)
25 | - Azure Architecture center, includes reference architectures - [link](https://docs.microsoft.com/en-us/azure/architecture/)
26 | - Online poster with all certification types (see Azure section) - [link](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE2PjDI)
27 | - Shown below Microsoft certifications by job role
28 | 
29 |
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/GCP/GCP-CLOUDLAKES.md:
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1 | # Patterns for GCP Cloud Data Lakes
2 |
3 | ## Raw Kubernetes
4 |
5 | The reference pattern...
6 |
7 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/cloud-k8.png">
8 |
9 | ## Managed Kubernetes / GKE
10 |
11 | GCP includes the [`Google Kubernetes Engine`](https://cloud.google.com/kubernetes-engine) as a container cluster controller. GKE can be configured to use an auto-scaling service. Also PAPI, which is now called `Google Life Sciences API` (see below) can be used.
12 |
13 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/gcp-k8.png">
14 |
15 |
16 | ---
17 | ## GCP Genomics
18 |
19 | ### GATK & PAPI
20 |
21 | GCP includes the [Google Cloud Life Sciences API](https://cloud.google.com/life-sciences/docs/reference/rest) (was called the Pipelines API) for burstable genomic-scale data pipelines. Pattern is shown below for the GATK analysis toolkit.
22 |
23 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/gcp-cromwell.png">
24 |
25 | ### DeepVariant & PAPI
26 |
27 | GCP includes the [Google Cloud Life Sciences API](https://cloud.google.com/life-sciences/docs/reference/rest) (was called the Pipelines API) for burstable genomic-scale data pipelines. Pattern is shown below for the DeepVariant analysis toolkit.
28 |
29 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/gcp-deepvariant.png">
30 |
31 |
32 | ### Nextflow & PAPI
33 |
34 | GCP includes the [Google Cloud Life Sciences API](https://cloud.google.com/life-sciences/docs/reference/rest) (was called the Pipelines API) for burstable genomic-scale data pipelines. Pattern is shown below for a Nextflow analysis toolkit.
35 |
36 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/gcp-nextflow.png">
37 |
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/GCP/README.md:
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1 | # GCP Resources
2 |
3 | I have created a number of courses for GCP. Many are on Linked In Learning with associated Repos on GitHub.
4 | NOTE: A couple of these courses are fully open source (on GitHub and YouTube).
5 |
6 | ## Courses
7 |
8 | ### GCP Courses
9 |
10 | - My GCP Courses on LinkedIn Learning - [link](https://www.linkedin.com/learning/search?entityType=COURSE&keywords=gcp%20langit) to all GCP courses
11 | - 📺 **'GCP Essentials'** - this is the first course to take in this series - [link](https://www.linkedin.com/learning/google-cloud-platform-essential-training-for-administrators-22141609)
12 | - 📺 **'GCP Enterprise Essentials'** - take this course if you are an enterprise cloud professionals - [link](https://www.linkedin.com/learning/google-cloud-platform-for-enterprise-essential-training-22140980)
13 | - 📺 **'GCP Tools'** course on [LI_L](https://www.linkedin.com/learning/learning-google-cloud-developer-and-devops-tools) & associated Repo folder in `tools` section at - [link](https://github.com/lynnlangit/gcp-essentials/tree/master/0_setup_and_iam_and_costs/tools)
14 | - 📺 **'GCP Cost Control'** course on [LI_L](https://www.linkedin.com/learning/google-cloud-controlling-cost), and associated Repo page at - [link](https://github.com/lynnlangit/gcp-essentials/tree/master/0_setup_and_iam_and_costs/0c_cost_control)
15 | - 📺 **'Learning Hadoop'** - this is the first course to take in this series
16 | - 📺 **'Cloud Hadoop: Scaling Apache Spark'** - uses GCP Dataproc - take this course to learn to use/scale Spark on GCP
17 | - 📺 **'Cloud NoSQL for SQL Pros'** - this course covers both GCP and AWS NoSQL data services
18 |
19 | ### Google Gemini & Machine Lerning Courses
20 | - 📺 **'Google Gemini for Developers'** - this course introduces the Google Gemini for developers at [link](https://www.linkedin.com/learning/google-gemini-for-developers-24018542)
21 | - 📺 **'Advanced Google Gemini'** - this course explores buiding LLM apps using the Google Gemini for developers at [link](https://www.linkedin.com/learning/advanced-gemini-for-developers)
22 | - 📺 **'GCP Machine Learning'** - this course covers machine learning on GCP at [link](https://www.linkedin.com/learning/google-cloud-platform-for-machine-learning-essential-training-23457382)
23 | - 📺 **'Spark and NoSQL'** courses on LinkedIn Learning (includes lots of GCP info)- [link](https://www.linkedin.com/learning/search?entityType=COURSE&keywords=hadoop%20spark%20langit)
24 |
25 | ## Repos
26 |
27 | - My example GCP Repos and associated Courses
28 | - :octocat: **'gcp-essentials'** - [link](https://github.com/lynnlangit/gcp-essentials) - companion to my LI_L 'GCP Essentials' course
29 | - :octocat: **'gcp-ml'** - [link](https://github.com/lynnlangit/gcp-essentials/tree/master/6_AI-ML) - compantion to my my LI_L 'GCP ML' and my 'Google Gemini' courses
30 | - :octocat: **'learning-hadoop-and-spark'** - [link](https://github.com/lynnlangit/learning-hadoop-and-spark) - companion to my LI_L 'Learning Hadoop' course
31 | - :octocat: **'learning-nosql'** - [link](https://github.com/lynnlangit/learning-nosql) - compantion to my LI_L 'NoSQL' course
32 | - :octocat: **'gcp-for-bioinformatics'** - [link](https://github.com/lynnlangit/gcp-for-bioinformatics) - includes YouTube screencast demo playlist - FREE COURSE!
33 |
34 | ## Articles, Screencasts and more
35 |
36 | - 📚 My GCP Medium Articles - [link](https://medium.com/search?q=gcp%20langit)
37 | - 📺 My YouTube bioinformatics GCP playlist - [link](https://www.youtube.com/playlist?list=PL4Q4HssKcxYtE5Tae3epNab3mK9iP1iWX)
38 | - 📺 My YouTube general GCP playlist - [link](https://www.youtube.com/playlist?list=PL6971A0258365F21E)
39 | - 🏆 My GDE (Google Developer Expert for Cloud) award - [link](https://developers.google.com/community/experts/directory/profile/profile-lynn_langit)
40 |
41 | ---
42 |
43 | ## Other Resources & Certification Info
44 |
45 | - GCP Community Resources, including tutorials - [link](https://cloud.google.com/community/)
46 | - GCP code samples - [link](https://cloud.google.com/docs/samples)
47 | - GCP Services, defined concisely on one page - [link](https://github.com/gregsramblings/google-cloud-4-words)
48 | - GCP Services compared to AWS and Azure services - [link](https://cloud.google.com/docs/compare/aws?hl=en_US)
49 | - List of certifications for Google Cloud Platform - [Link](https://cloud.google.com/certification) and shown below
50 |
51 | 
52 |
53 | ----
54 |
55 |
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/IBM/README.md:
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1 | # IBM Cloud Resources
2 |
3 | I am currently exploring **quantum computing** using IBM cloud resources. IBM Cloud includes a number of types of services, summarized and shown below.
4 |
5 |
6 | - :octocat: My Repo 'learning-quantum' has links and other resources - [link](https://github.com/lynnlangit/learning-quantum)
7 | - 🗞️ IBM Qiskit Cloud Quantum Developer certification - [link](https://www.ibm.com/training/certification/ibm-certified-associate-developer-quantum-computation-using-qiskit-v02x-C0010300)
8 |
9 | <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/ibm-cloud.png" width=600>
10 | ---
11 |
12 |
13 |
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169 | License. However, in accepting such obligations, You may act only
170 | on Your own behalf and on Your sole responsibility, not on behalf
171 | of any other Contributor, and only if You agree to indemnify,
172 | defend, and hold each Contributor harmless for any liability
173 | incurred by, or claims asserted against, such Contributor by reason
174 | of your accepting any such warranty or additional liability.
175 |
176 | END OF TERMS AND CONDITIONS
177 |
178 | Copyright 2020-2023 Lynn Langit
179 |
180 | Licensed under the Apache License, Version 2.0 (the "License");
181 | you may not use this file except in compliance with the License.
182 | You may obtain a copy of the License at
183 |
184 | http://www.apache.org/licenses/LICENSE-2.0
185 |
186 | Unless required by applicable law or agreed to in writing, software
187 | distributed under the License is distributed on an "AS IS" BASIS,
188 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
189 | See the License for the specific language governing permissions and
190 | limitations under the License.
191 |
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/NVIDIA/README.md:
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1 | # NVIDIA Cloud Resources
2 |
3 | I am currently exploring **quantum computing** using NVIDIA cloud resources. NVIDIA Cloud includes a number of types of services, summarized and shown below. Also see [my other GitHub repo](https://github.com/lynnlangit/learning-quantum/tree/main/2_cloud-vendors/NVIDIA%20CUDA-Q).
4 |
5 | NVIDIA recently started offering GenAI (and other) types of technical certifications --> [link](https://www.nvidia.com/en-us/learn/certification/)
6 |
7 | ## About Nvidia CUDA-Q
8 |
9 | From Nvidia's site --> https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html
10 |
11 | *"CUDA-Q streamlines hybrid application development and promotes productivity and scalability in quantum computing. It offers a unified programming model designed for a hybrid setting—that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for programming in Python and in C++. Learn more about the key benefits of CUDA-Q."*
12 |
13 | *"This Quick Start page guides you through installing CUDA-Q and running your first program. If you have already installed and configured CUDA-Q, or if you are using our Docker image, you can move directly to our Basics Section. More information about working with containers and Docker alternatives can be found in our complete Installation Guide."*
14 |
15 | ### Building
16 |
17 | *"We can define our quantum kernel as a typical Python function, with the additional use of the `@cudaq.kernel` decorator. Let’s begin with a simple GHZ-state example, producing a state of maximal entanglement amongst an allocated set of qubits."*
18 |
19 | ```
20 | import cudaq
21 |
22 | @cudaq.kernel
23 | def kernel(qubit_count: int):
24 | qvector = cudaq.qvector(qubit_count)
25 |
26 | # Place the first qubit in the superposition state.
27 | h(qvector[0])
28 |
29 | # Loop through the allocated qubits and apply controlled-X,
30 | # or CNOT, operations between them.
31 | for qubit in range(qubit_count - 1):
32 | x.ctrl(qvector[qubit], qvector[qubit + 1])
33 |
34 | # Measure the qubits.
35 | mz(qvector)
36 | ```
37 |
38 | ### Running
39 |
40 | See this page --> https://nvidia.github.io/cuda-quantum/latest/using/basics/run_kernel.html#sample
41 |
42 |
43 |
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1 | # Learning Cloud
2 |
3 | ## What's in this Repo
4 |
5 | This Repo contains links to **100+ resources** I've created for you to learn to work on the public cloud.
6 | Links, examples, short explanations and architectures are included for AWS, Azure, or GCP & more.
7 |
8 | ## What's NEW
9 |
10 | <details><summary>In 2025 / LinkedIn Learning Courses</summary><br>
11 |
12 | I have published more than 30 courses on AI, Cloud and Data topics on LinkedIn Learning - [here](https://www.linkedin.com/learning/instructors/lynn-langit)
13 |
14 | - ⭐ PUBLISHED - 100% update - both courses [**'Google Gemini v 2.x for Devs (Beg & Adv)'**](https://www.linkedin.com/learning/google-gemini-for-developers-25832309)
15 | - ⭐ PUBLISHED - Updated with GenAI content [**'Cloud Careers and Certifications'**](https://www.linkedin.com/learning/cloud-computing-careers-and-certifications)
16 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/gcp.png" width=25> Recorded - New course **'Google Agentspaces'**
17 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/gcp.png" width=25> Scheduled - New course **'Google VEO3'**
18 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/aws.png" width=25> Scheduled - New course **'AWS GenAI DevOps'**
19 | </details>
20 |
21 | ### MOST VIEWED CONTENT
22 |
23 | <details><summary>Most viewed Cloud & GenAI Content</summary>
24 |
25 | #### GenAI Courses on Linked In Learning
26 |
27 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/gcp.png" width=25> **`Google Gemini for Developers`** - [link to repo](https://github.com/lynnlangit/gcp-essentials/tree/master/6_AI-ML/2_gemini_LLM) and [course](https://www.linkedin.com/learning/google-gemini-for-developers-25832309)
28 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/gcp.png" width=25> **`Advanced Google Gemini for Developers`** - [link to repo](https://github.com/lynnlangit/gcp-essentials/blob/master/6_AI-ML/2_gemini_LLM/ADV-LLM-Dev.md) and [course](https://www.linkedin.com/learning/advanced-gemini-for-developers)
29 | - <img src="https://github.com/lynnlangit/learning-cloud/blob/master/images/databricks-icon.png" width=20> **`Learn Databricks Gen AI`** - [link to course](https://www.linkedin.com/learning/learn-databricks-genai)
30 | - <img src="https://github.com/lynnlangit/sample-data/blob/master/1_sample_data/emoji-icons/chat-gpt.png" width=20> **`5+ min of my ChatGPT`** - screencast series (on YouTube as playlist)--[link to playlist](https://www.youtube.com/playlist?list=PL4Q4HssKcxYuwbVAgVqwM5od3yLtg9NM0)
31 |
32 |
33 | #### GCP Topics
34 | - 🧬 📺 :octocat: **`GCP-for-Bioinformatics`** [FREE course on GitHub](https://github.com/lynnlangit/gcp-for-bioinformatics)
35 | - 📺 :octocat: **`Serverless Architecture`** course - [link](https://www.linkedin.com/learning/serverless-architecture-19870153) & [repo](https://github.com/lynnlangit/serverless-architecture)
36 | - 📺 :octocat: **`GCP Essentials`** and **`GCP Enterprise`** courses on LI_L - see repo for updates - [link](https://github.com/lynnlangit/gcp-essentials)
37 | - 📺 :octocat: **`GCP Tools`** [course on LI_L](https://www.linkedin.com/learning/learning-google-cloud-developer-and-devops-tools) & associated repo examples in `tools` folder at [link](https://github.com/lynnlangit/gcp-essentials/blob/master/1_storage/tools/README.md)
38 | - 📺 :octocat: **`GCP Cost Control`** [course on LI_L](https://www.linkedin.com/learning/google-cloud-controlling-cost), see repo [link](https://github.com/lynnlangit/gcp-essentials/tree/master/0_setup_and_iam_and_costs/0c_cost_control) too
39 |
40 |
41 | #### Data, Machine Learning and More
42 | - 📺 :octocat:**`Learning SnowflakeDB`** [course on LI_L](https://www.linkedin.com/learning/learning-snowflakedb) & associated repo at [link](https://github.com/lynnlangit/learn-snowflakedb)
43 | - 📺 :octocat: **`Cloud Quantum Computing`** [course on LI_L](https://www.linkedin.com/learning/cloud-quantum-computing-essentials) & associated working repo at [link](https://github.com/lynnlangit/learning-quantum/tree/main/2_cloud-vendors)
44 | - :octocat: Studies on **`Learning Ethical AI`** , my resources repo at [link](https://github.com/lynnlangit/learning-ethical-ai)
45 | - 🧬 :octocat: In preview - **`aws-for-bioinformatics`** a FREE and open source course on GitHub and YouTube - [link](https://github.com/lynnlangit/aws-for-bioinformatics)
46 | - 📚 :octocat: 📺 **`Learning Data Mesh`** [repo + book club](https://github.com/lynnlangit/learning-data-mesh)
47 |
48 | </details>
49 |
50 | ---
51 |
52 | ### ALL CONTENT: 100+ Cloud Courses, Articles
53 |
54 | <details><summary>All Cloud Content</summary>
55 |
56 | #### All Cloud Courses
57 | - 📚 my **cloud courses** on LinkedIn Learning (30) - [link](https://www.linkedin.com/learning/instructors/lynn-langit)
58 | - :octocat: my **example code** in Github repos (10+) - [link](https://github.com/lynnlangit)
59 | - 📖 my **system visualization** tools, talks and examples (list) - [link](https://github.com/lynnlangit/learning-cloud/tree/master/0_CLOUD-PATTERNS/1_Viz-Systems)
60 | - 🧬 :octocat: my **course on bioinformatics for cloud** on GitHub (`TeamTeri`) - [link](https://github.com/lynnlangit/TeamTeri)
61 |
62 | #### Cloud Architectures, Patterns and Articles
63 | - :octocat: My `CLOUD-PATTERNS` section to share best practice patterns and tools for cloud workloads - [link](https://github.com/lynnlangit/learning-cloud/tree/master/0_CLOUD-PATTERNS)
64 | - 📺 :octocat: My `Serverless Architecture` companion repo to my course on LI_L - [link](https://www.linkedin.com/learning/serverless-architecture-19870153)
65 | - 📚 **`Lynn Langit's Cloud World`** [on Substack](https://lynnlangit.substack.com/)
66 | - 📖 my **technical articles** on Medium (40) - cloud topics - [link](https://medium.com/search?q=langit%20cloud)
67 | - 📖 my **micro-blogging** on Dev.to (many...) - [link](https://dev.to/lynnlangit)
68 |
69 | #### All Cloud Screencasts, Sample Data and Slide Decks
70 | - 🗣️ my **screencasts/talks** on YouTube (50+) - cloud topics and more - [link](https://www.youtube.com/c/LynnLangit/playlists)
71 | - 🗄️ my **sample data** in GitHub repo (10+) kinds of sample data - [link](https://github.com/lynnlangit/sample-data)
72 | - 🗣️ my **slide decks** on Slides.com (many...) - [link](https://slides.com/lynnlangit)
73 |
74 | </details>
75 |
76 | ---
77 |
78 | ### Tl;dr: "How do I get started in cloud?"
79 |
80 | If you are completely **new to cloud**, you might want to go here first --> [link](https://github.com/lynnlangit/learning-cloud/tree/master/0_CLOUD-PATTERNS/0_Starting-Points)
81 | Also see my ⭐⭐⭐ - **FAQ course** - [Insights on Cloud Computing](https://www.linkedin.com/learning/insights-on-cloud-computing-with-lynn-langit) ⭐⭐⭐
82 |
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1 | # Patterns for Cloud Data Lakes
2 | ## Raw Kubernetes
3 |
4 | This is the general pattern. See specific vendor `CLOUDLAKE.md` files in each folder for examples using that cloud vendor's data lake servics.
5 |
6 | <img src="https://github.com/lynnlangit/learning-cloud/blob/38919ae405d672286aec0a33ebe01e1b42c3d096/images/data-lakes/cloud-k8.png">
7 |
8 |
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/z_utilities/.devcontainer/DOCKERFILE:
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1 | FROM mcr.microsoft.com/vscode/devcontainers/base:debian-10
2 |
3 | RUN apt-get update \
4 | && export DEBIAN_FRONTEND=noninteractive \
5 | && apt-get -y install --no-install-recommends graphviz
6 |
7 |
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/z_utilities/.devcontainer/devcontainer.json:
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1 | {
2 | "name": "Markdown Editing",
3 | "dockerFile": "DOCKERFILE",
4 |
5 | "settings": {
6 | "terminal.integrated.shell.linux": "/bin/bash"
7 | },
8 |
9 | "extensions": [
10 | "yzhang.markdown-all-in-one",
11 | "streetsidesoftware.code-spell-checker",
12 | "DavidAnson.vscode-markdownlint",
13 | "shd101wyy.markdown-preview-enhanced",
14 | "bierner.github-markdown-preview"
15 | ]
16 |
17 | }
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/z_utilities/.tours/aws-tour.tour:
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1 | {
2 | "title": "AWS-tour",
3 | "steps": [
4 | {
5 | "file": "AWS/README.md",
6 | "line": 7,
7 | "description": "These courses are for specific topics in AWS",
8 | "title": "AWS for Data Science"
9 | },
10 | {
11 | "file": "AWS/README.md",
12 | "line": 8,
13 | "description": "These courses are for AWS DevOps topics",
14 | "title": "AWS for DevOps"
15 | },
16 | {
17 | "file": "AWS/README.md",
18 | "line": 9,
19 | "description": "This course series is for Cloud Architects",
20 | "title": "AWS for Architects"
21 | },
22 | {
23 | "file": "AWS/README.md",
24 | "line": 13,
25 | "description": "Example GitHub Repos include scripts, demos and sample datasets",
26 | "title": "Example GitHub Repos"
27 | },
28 | {
29 | "file": "AWS/README.md",
30 | "line": 24,
31 | "description": "The open guide to AWS is a useful resource, written by the user community\n[Open Website in browser](command:vscode.open?[\"https://github.com/open-guides/og-aws\"])",
32 | "title": "The Open Guide to AWS on GitHub"
33 | }
34 | ],
35 | "ref": "master"
36 | }
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/z_utilities/.tours/cloud-tour.tour:
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1 | {
2 | "title": "Cloud-tour",
3 | "steps": [
4 | {
5 | "file": "README.md",
6 | "line": 9,
7 | "description": "This link goes to my LinkedIn Learning Cloud courses",
8 | "title": "LinkedIn Learning Courses"
9 | },
10 | {
11 | "file": "README.md",
12 | "line": 12,
13 | "description": "This link goes to my YouTube Channel. I have playlists by topic there too.",
14 | "title": "YouTube Screencasts"
15 | },
16 | {
17 | "file": "README.md",
18 | "line": 22,
19 | "description": "I have one page per cloud in this repo. Go to the vendor folder, then the `README.md` file in that folder.\n\nFor example for AWS, [Open AWS CodeTour](command:codetour.startTourByTitle?[\"AWS-tour\"])",
20 | "title": "GOTO AWS CodeTour"
21 | },
22 | {
23 | "file": "README.md",
24 | "line": 23,
25 | "description": "I have one page per cloud in this repo. Go to the vendor folder, then the `README.md` file in that folder.\n\nFor example for AWS, [Open GCP CodeTour](command:codetour.startTourByTitle?[\"GCP-tour\"])"
26 | }
27 | ],
28 | "ref": "master"
29 | }
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/z_utilities/.tours/gcp-tour.tour:
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1 | {
2 | "title": "GCP-tour",
3 | "steps": [
4 | {
5 | "file": "GCP/README.md",
6 | "line": 7,
7 | "description": "The 'missing' GCP course - designed for beginners to the GCP cloud"
8 | },
9 | {
10 | "file": "GCP/README.md",
11 | "line": 11,
12 | "description": "Uses GCP Dataproc - managed Hadoop and Spark"
13 | },
14 | {
15 | "file": "GCP/README.md",
16 | "line": 19,
17 | "description": "Fully open source and free course for GCP - all examples use genomic datasets"
18 | }
19 | ],
20 | "ref": "master"
21 | }
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/z_utilities/.vscode/settings.json:
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1 | {
2 | "cSpell.words": [
3 | "bioinformatics",
4 | "langit",
5 | "screencasts"
6 | ],
7 | "extensions.ignoreRecommendations": true,
8 | "files.autoSave": "afterDelay",
9 | "typescript.tsc.autoDetect": "off",
10 | "npm.autoDetect": "off",
11 | "debug.allowBreakpointsEverywhere": true,
12 | "html.autoClosingTags": false,
13 | "files.watcherExclude": {
14 | "**/target": true
15 | }
16 | }
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