Data Engineering Vocabulary - Becoming A Data Engineer

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.
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[@SeattleDataGuy]

If you guys want to learn more about data engineering, then sign up for my newsletter here seattledataguy.substack.com/ or join the discord here discord.gg/2yRJq7Eg3k

[@fox_trot]

Main of the vocabularies mentioned in this video. 1. CDC: capture data change 2. Normlization/denormalization 3. Pubisher/Subscriber AKA Producer/Comsumer 4. Execution plan 5. Data lineage 6. Data governance

[@portabledata]

Seattle Data Guy - Love the quick summaries of key concepts here! The pictures of the different types of normal forms are a great way to communicate the concepts

[@SeattleDataGuy]

Glad it was helpful!

[@TrijitSaha]

Crazy that you've uploaded this today having watched your previous video on this yesterday and binging your other data engineering videos in the last week. Love the content!

[@SeattleDataGuy]

Thanks for all the support! Trying to keep putting stuff out! Actually at a conference right now so I am juggling a few things 😂

[@txdba]

Thanks. Those were great topics to be prepped for when interviewed.

[@idkwhy77]

thanks! Was interested in the field, still not in college so looking forward to it. Good to start early & prep for future :)

[@ankursingh730]

Love the content! Explanations are pretty simple which has really helped me understand few things. Thanks !

[@SeattleDataGuy]

Great to hear!

[@ashraf7172]

How you are so great with your concepts. 😅 Inspiring us to work hard

[@larryfisherman25]

I asked myself the same thing 😅

[@boukraailyesali9131]

+9999999999999999

[@SeattleDataGuy]

Thank you so much 😀 happy to help!

[@WarbossPepe]

Looking buff, boi!

[@SeattleDataGuy]

hahaha thanks...

[@Raviraj-1514]

Debezium is also a good example for CDC

[@thedailyepochs338]

Love the Ken Jee Merch

[@josipsadek4425]

Hey, tnx for the video, love your content. You mentioned some links regarding the "Execution plan", but I can't find them in your description. Also, I would like to learn more about data governance, I am most interested in ensuring data we store is high-quality data. So any links are very appreciated. :) At my work, I learned that if a mobile app has any kind of bug and therefore analytics data is not collected properly, that incomplete data stays forever in our warehouse. So I would like to introduce some sort of system that makes sure that we store complete and high-quality data. Also, I would like to if the data is not complete that someone gets an email with details.