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View Code? Open in Web Editor NEWThe Data Engineering Cookbook
Home Page: https://learndataengineering.com/
License: Apache License 2.0
The Data Engineering Cookbook
Home Page: https://learndataengineering.com/
License: Apache License 2.0
Very minor issue, the word Consumption is incorrectly spelt as Cosumption.
Present in:
Interview Questions -> The Cloud
The Designing Data Intensive Applications book is a fantastic resource on everything related to building, scaling and maintaining data-intensive applications. It covers topics such as: data modeling, serialization formats, database replication and partitioning, batch and stream processing.
Just wanted to say thank you for the time you have put into providing this info.
A quick suggestion. You might want you add some tags to your repo so it shows up in searches better.
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โง https://andreaskretz.com/
In this section : https://github.com/andkret/Cookbook/blob/master/AdvancedSkills.md#what-makes-hadoop-so-popular
I think you mean the BI software Tableau and not Tableu, don't you ?
This version is over simplified for a purpose I get it but if you want to add a more detailed version of the big data landcape you can referance the work of Matt Turck : (2019) http://mattturck.com/wp-content/uploads/2019/07/2019_Matt_Turck_Big_Data_Landscape_Final_Fullsize.png
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Hi And,
I am not able to download the PDF version book from the link you have mentioned in the Github link. Please advise. Not sure this issue is only for me or others as well. Thanks!
Regards,
Srini
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โง https://archive.ics.uci.edu/ml/index.php
Hello,
Thanks for that's interesting content. Beside PDF file, I'd like to suggest to generate ePub
and Mobi files as well.
In the README file it's not clear how the PDF generated, but I'd be happy to add it if PDF generation is added to readme.
Thanks.
Building Cloud Services with the Java Spring Framework (Coursera) Link does not work anymore
Thanks for this great resource!
While it is always a challenge to write in a gender neutral or balanced manner, please consider this in your book. For example, in the conclusion of the introduction both the data engineer and data scientist use the masculine pronoun. This could easily be fixed.
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โง https://andreaskretz.com/
"Because to train a algorithm you need useful data."
a โ an
The Hadoop Application Architectures book is a very good resource on architecture and design of big data systems, the first part teaches a lot of technologies and the second part presents four case studies which try to apply the different concepts, ideas and technologies to a problem domain.
I found this web very useful: https://learngitbranching.js.org/
Maybe could be added to section Get Familiar With Git
Regards!
In the main file when including figures, you use "images/", but the directory is actually called "Images". This prevents compilation on ubuntu (and I assume all *nix systems).
! LaTeX Error: File `Images/Machine-Learning-Pipeline' not found.
See the LaTeX manual or LaTeX Companion for explanation.
Type H <return> for immediate help.
...
l.112 ...twidth]{Images/Machine-Learning-Pipeline}
I'd like to suggest to add the following Python resources, which I've been using myself:
Python Docs: The Python Tutorial
Python Programmer
Udemy - The Complete Python Programmer Bootcamp 2020
Think Python: How to Think Like a Computer Scientist
Real Python
DataCamp: Data Engineer with Python Track
DataQuest: Data Engineer with Python
On the actual page 37, section 12.3.4 Scaling Out, it is talked about a link to a Microsoft MSDN page that is missing
This Link to a Microsoft MSDN page has more options of scaling out an SQL database for you
"They need to know the different big data tools Inside and out."
Inside โ inside
In the README.md the link to the interview questions is https://github.com/andkret/Cookbook/blob/master/InterviewQuesitons.md
which is a misspelling of the actual link to the interview questions which is https://github.com/andkret/Cookbook/blob/master/InterviewQuestions.md
so it appears as if there are no interview questions
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โง http://www.the-numbers.com/
Suggested update to the The 4 Vs of Big Data: add Value to the list and change title to The 5 Vs of Big Data.
Haven't finished the entire specialization myself, but so far it's good!
Link: https://www.coursera.org/specializations/gcp-data-machine-learning
I would highly recommend the heading be Containers and Pods, or something to that effect, and not Docker. Although Docker has been the most easily recognizable containerization technology, many communities want to build containerization technology that works for the opensource community and not the business model of a popular containerization technology. It seems the Kubernetes community has been moving towards a container runtime interface (CRI) that is not locked into depending on Docker. That's where CRI-O has come into play.
It's the concept behind containers and pods that is important to understand and not necessarily the technology used to deliver those concepts. At least IMHO. These types of technologies will always change.
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A lot of the files in the repo are created/modified when you compile the main document. There is no need for them to be to be tracked as either they create very noisy commits, or you have to do additional work to revert these files back to their original state before committing, in which case they no longer match the document in it's latest state.
For example, if I change the title text of the first \part{}
and recompile, I have made changes to 7 files
$ git status
On branch master
Your branch is up to date with 'origin/master'.
Changes not staged for commit:
(use "git add/rm <file>..." to update what will be committed)
(use "git checkout -- <file>..." to discard changes in working directory)
modified: Data Engineering Cookbook.aux
modified: Data Engineering Cookbook.log
modified: Data Engineering Cookbook.out
modified: Data Engineering Cookbook.pdf
deleted: Data Engineering Cookbook.synctex.gz
modified: Data Engineering Cookbook.tex
modified: Data Engineering Cookbook.toc
no changes added to commit (use "git add" and/or "git commit -a")
The important detail could easily be lost here.
Installs of texlive delete the .synctex.gz by default, unless you pass synctex=1
as an option to pdflatex.
I think a .gitignore
with the following content will be a good place to start
# Ignore build artefacts
*.aux
*.log
*.lof
*.lot
*.toc
*.out
*.synctex.gz
This will keep commits, and PRs, clean and easier to follow.
Do you mean Json web token
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flag / GITHUB_TOKEN
env var.)
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Hi, @andkret, great idea about data engineering cookbook, if possible please add some reference about DevOps i.e. pipelines, CI/CD to give a big picture on the toolchain loops for before and after go live that is havig go live from the very first iteration. Books: Kim Gene & Co.
We use BigQuery extensively both for production real-time queries of our data and batch processing that builds those tables in the first place. BigQuery was a major enabling technology for us and remains almost magical in how powerful and affordable it is. We also use BQML extensively as it eliminates much of the work to do basic machine learning. It runs in the same environment as the database queries, not code to write, no data transfer. We also use Matillion for BigQuery for all of our workflows. ELT vs ETL so SQL is an essential skill set but its visual programming environment provides common touch-point between all parties and very productive environment to work in.
First of all, great work with this really nice document and resource!
I have a small comment in the Get Familiar With Github
chapter and it's about the confusion between Github and git
itself. Although Github right now is the biggest hosting service for git, it's very important to make the distinction between git and the service that is Github.
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โง http://visualizingeconomics.com/
qulik sense should be qlik sens
https://github.com/andkret/Cookbook/blob/master/sections/03-AdvancedSkills.md#quliksense
Hallo @andkret, appretiate your work here very much. For completeness i would like to see Graph Databases, especially Neo4J. Saw some very cool stuff from
their Graph Connect with Neo4J and Spark.
By the way, just for improvement, i would suggest to add some descriptive words to the doc, where currently only a podcast link or youtube link is available. Only to avoid the cookbook becoming just a link collection.
BR Moe
Hi Andreas, as you suggested, here are some examples of sample databases:
MySQL: Sakila Sample Database
MySQL: Employees Sample Database
Microsoft SQL Server: Northwind and pubs sample databases
Microsoft SQL Server: AdventureWorks sample databases
Microsoft SQL Server: WideWorldImporters sample database
ChinookDatabase
PostgreSQL: DVD Rental Store
Hello ,I'm a junior data engineer in China and I want to translate this book for learning.
hi,
i was reading project description (README.md) and then i thought about reading your medium publications
I have a Medium publication where you can publish your data engineer articles:
[Medium publication](https://link.medium.com/9oi1VDrhPW)
this redirect to another URL
https://rsci.app.link/9oi1VDrhPW?_p=f054275bf62ada7e63800b60266d75
the Avast antivirus trigger a phishing URL detection I am confused!!!
I feel we could enhance by working on
Data Scientist
Data Engineering
Do let me know if these are good, I can work on submitting a PR
In section 7.3 Switch, Level 3 Switch, one of the differences between a layer 2 and a layer 3 switch is vlan support, so it is most likely ideal to add a vlan bit.
eg, https://documentation.meraki.com/MS/Layer_3_Switching/Layer_3_vs_Layer_2_Switching
Where is the link to download a PDF version of this cookbook?
Below 7.5 Firewalls, in the talk here you mentioned roughly, "I'm not sure where this topic can be learned, maybe Udacity. If anyone knows ...?" (heavy paraphrasing here)
A link to an iptables tutorial, or a talk about it would be a great addition to the firewall section of the guide. :)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.