David Data
Welcome to David Data, the ultimate destination for anyone interested in data analytics and analytics engineering!
Whether you're just getting started in the world of data or you're an experienced data professional, my videos have something for everyone. I cover a range of topics, from data modelling and architecture, dbt, and data pipelines. I also explore the latest tools and technologies used in analytics engineering and data engineering.
My goal with this channel is to create a community of data enthusiasts who can learn from each other and share their experiences. I encourage you to leave comments, ask questions, and share your own insights and perspectives.
So, if you're interested in data analytics and data engineering, make sure to hit the subscribe button and turn on notifications so you never miss a video. Thank you for watching, and I look forward to exploring the world of data with you!
David Data
Learn more about it here. https://youtu.be/BeId9HEEs_g
4 months ago (edited) | [YT] | 2
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David Data
Have you tried dbt Microbatch Incremental Strategy? Learn more about it here. https://youtu.be/BeId9HEEs_g
4 months ago | [YT] | 2
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David Data
Happy New Year family.
Cheers to more learning and growth this year.
1 year ago | [YT] | 4
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David Data
Hello everyone,
I will like to say thank you for 2024.
Thank you for the support, the views, the comments, the subscribers, shares and everything else.
Thank you for making 2024 great.
I look forward to seeing you in the new year.
Cheers.
1 year ago | [YT] | 5
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David Data
Merry Christmas🧑🏻🎄
Ho Ho Ho ssana in the Highest.
2 years ago (edited) | [YT] | 2
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David Data
Has this happened to you?😂😂
2 years ago | [YT] | 1
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David Data
Hello data nerds, I would like to know your thoughts and experience level in using dbt (data build too). Please vote with the poll.
3 years ago | [YT] | 0
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David Data
5 Data quality points to consider when analysing your data.
Validity: Data does not conform to your business rules.
Accuracy: Data does not conform to an objective true value.
Completeness: Failure to create, save, or store whole datasets.
Consistency: Computations from data are incorrect.
Uniform: Exploring and presenting data is misleading.
3 years ago | [YT] | 3
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