What I'm missing are query runtimes for all but the first two types. For example, what happens to a query if a graph database has 10,000 nodes and 500,000 edges?
Great comparison of relational vs columnar and I really like document and key-value store parts too, the only thing is 18min is a bit long, otherwise perfect!
Hi, Anton! Can you please share some resources on how to scale ML workloads in GKE? Some tricks that you may know, time sharing vs multi-instance GPU? In other words, how do we get most out of the compute without running into OOM issues Thank you!
Well you put a lot of time for SQL and MongoDb.. and practically brefly mention others.. Interesting will be open topics for Graph and vector and so on.
It's about fact finding per person the validity to establish. correlation in universities of universes? How about the universe continually moves and dark matter escapes at the edge a data and informational fact in science and matters though in strands waves forms or could carry some mass or form mass and enlarge as it attracts, the orientations for dimension have layers and it waves or electromagnetically affected by outside science influence and could change overtime, sensor for changes needed, then person identity validation if not metamorphosized yet for observational fix per universe of identity signatures but as you've said what if awol or dropped out then reconsidered in other connected schools for example in different timeframe was the previous info still valid since discontinued, if pieces by jigzsaw with comparison was comparison fixed connected or not for every hour there's some change, skin cells, protein factor increase or not, lipid content changes, mindset rewire if rewire, bone cells technologically change for example, etc can minimal difference be considered or be open minded that universally speaking variables of species, familiarity? More studies needed
The column oriented explanation is wrong. Cassandra does not store dsta in colums. It still stores in rows, but how does it read data row by row, but each row has key value pairs whcih are colums. Where it van jump to a required columns. Cassandra is column family DB, not a column oriented DB.
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One of the best video to understand Types of Databases. Excellent work 👍
thank you!!
Your clarity is exceptional
Its a very good summary of all features, adv and disadv of databases, and their use cases too. A good one mate!
thank you!!
Absolute perfect level of detail that I was looking for. Love this video, thank you so much!
thanks!
Super good for understanding the differnces, crystal-clear, thank you!
thanks!
7 minutes of video do way more than a whole month of classes. What an awesome video
❤️
what did you use for slides/presentation? This is top notch.
crystal clear illustration, structured story telling, subscribed!
thank you!
I'm doing a talk tomorrow and this was helpful in reminding me of good info to mention.
Damn, good job and thank you. (it would be great if you had a visual remainder to distingush sections but everything else is just perfect.)
thanks!
Super nice summary for beginners. Im just learning about noSQL and this is a great start to the idea of it!
thank you!!
Its a GREAT video that explains all clearly, simple and with examples!
thank you!
This is so a well thought out lesson. Thank you so much.
Thank you! :)
The most important video i have seen about this topic 🎉❤
thanks!
This is very good video. Thanks for creating this content for us.
Amazing video on the topic! Very clear and concise
thank you!
Thank you for your detailed explanation. Hats off!
thank you!
Very nice summary. Thanks, Anton!
thanks, Ashok!
Thank you for sharing this great video!
fantastic perspectives
thank you!
such clarity ❤
Thank you Anton for the great video!
Is it worth to mention Redis and Memcached as examples of key value databases?
Redis tries to be everything nowadays, lol
Thanks for sharing, was very helpful 💖💖
thanks!!
Well done !! Very clear
Thank you!
Really great video 👏
You are amazing man thank you very much, with this I did my homework hahahaj
thank you! :)
Great content, Thanks!
thank you!
What I'm missing are query runtimes for all but the first two types. For example, what happens to a query if a graph database has 10,000 nodes and 500,000 edges?
regular Relational DB: array of structs
columnar DB: struct of arrays
HI Anton, congratulations for the video. I am also interested in the software used for the animated infographics. Thanks in advance.
thanks, adobe suite
Very Very Well done Thanks
thank you!
Thank you very much
my pleasure!
THANK YOU for a great video!
❤️
Great comparison of relational vs columnar and I really like document and key-value store parts too, the only thing is 18min is a bit long, otherwise perfect!
thanks! noted!
Thank you for this amazing content.
Great video, for a tinder like application. What database would you choose and why?
If it's a personal project, start simple and use MongoDB or Postgres! You can scale and redesign later.
What about *Redis* , does it fall under Time-series database or is it something else entirely?
it is primarily a key-value store
impressive work
What a great video!
thanks!
Thanks!
love it 👌
high quality
thank you!
Great video indeed. May be you could also add a section with multi-purpose database(s) like SurrealDB ;-)
thanks :)
This is gold
thanks!!
Hi, Anton! Can you please share some resources on how to scale ML workloads in GKE? Some tricks that you may know, time sharing vs multi-instance GPU?
In other words, how do we get most out of the compute without running into OOM issues
Thank you!
It really depends on the framework you're using to run ML, such as Spark, Airflow, etc. I don't really have general advice on this topic.
Amazing
thanks you!
nice video, but 6:25 Cassandra is not columnar db isn't it? it a wide-column store.
well it's a mix i know
@@AntonPutra your videos awesome bro. I love your videos a lot. Thank you very much.
@@premraj.m thanks :)
Well you put a lot of time for SQL and MongoDb.. and practically brefly mention others.. Interesting will be open topics for Graph and vector and so on.
thanks for the feedback, i have one for graph db - ua-cam.com/video/-6Xc2_IOh-0/v-deo.html
It's about fact finding per person the validity to establish. correlation in universities of universes? How about the universe continually moves and dark matter escapes at the edge a data and informational fact in science and matters though in strands waves forms or could carry some mass or form mass and enlarge as it attracts, the orientations for dimension have layers and it waves or electromagnetically affected by outside science influence and could change overtime, sensor for changes needed, then person identity validation if not metamorphosized yet for observational fix per universe of identity signatures but as you've said what if awol or dropped out then reconsidered in other connected schools for example in different timeframe was the previous info still valid since discontinued, if pieces by jigzsaw with comparison was comparison fixed connected or not for every hour there's some change, skin cells, protein factor increase or not, lipid content changes, mindset rewire if rewire, bone cells technologically change for example, etc can minimal difference be considered or be open minded that universally speaking variables of species, familiarity? More studies needed
The column oriented explanation is wrong. Cassandra does not store dsta in colums. It still stores in rows, but how does it read data row by row, but each row has key value pairs whcih are colums. Where it van jump to a required columns.
Cassandra is column family DB, not a column oriented DB.
I'll soon make a video specifically about columnar databases, including a hands-on tutorial.
Could you please make a video on Wide column vs column family vs columnar vs column oriented DBs. Please include some examples/usecases/scenarios also
can you give me some examples of databases?
You copied the entire columnar database chapter from this video watch?v=8KGVFB3kVHQ&ab_channel=ness-intricity101
крутяк но сделай пж по русски а то не все понял