note to the editor: please stop cutting away from the code so quickly. we're trying to follow along in the code based on what she's saying. at that moment, we don't need to cut back to the shot of her face. we can still hear her voice in the voiceover.

The RDD API is outmoded as of Spark 2.0 and in almost every use case you should be using the Dataset API. You lose out on a lot of improvements and optimizations using RDDs instead of Datasets.

Can you do Apache Kafka next? How do they compare?

I understood some of those words.

ahh.. so refreshing after taking a week break from dev work and staying away from non dev topics. Lol, I love our field. Like music to my ears

pretty sure theres a typo in that code. "splitLines" doesnt exist and is probably supposed to be words.map(...) instead

Is there any meta analysis on the usefulness of bigdata analysis? How often do jobs get run that either produce no meaningful data or don't produce any statistically significant data?

Wow congrats on the content. You were able to explain it in a concise, yet logical and detailed way. nice

These data ones are really good! Keep them coming!

It's so clear and easy after the explanation! I will be waiting for more vids about clustering and distributed computing)

A great example of how programming languages are a reasonably efficient mechanism to communicate sections of program and how natural language really is not.

Thank you for teaching an old man new things.

Please give time measurements comparing single node with multi node execution. What is the overhead?

Really interesting video! I have done some MapReduce before, but I haven’t came across Apache Spark

typo in line 32 for using `splitLines` instead of `word`?

Where are the extra bits?

Great explanations. Of course there are many things going on behind the scenes, but good overview.

More of these, please. More big data.

She refers to an early example. Did I miss that video? Otherwise, nicely done. Love learning about distributed computing.

I wish she also talked a little about Spark's ability to deal with data streams

Brady Please make a video on Kubernetes

Thank you for the great summary.

Thank you so much. This was an incredible explanation

Sorry for redundancy, just verifying my understanding. Do I understand it correctly that (when running this example in a cluster) collect runs the 'reduceByKey' against the results on each node, and then reduces to a final result. Say on Node 1 I have count of word 'something' = 5 , on Node 2 I have count of word 'something' = 3, then collect combines from those two nodes into a count of 'something' = 8, And so on...?

What is the architectural difference between spark and map reduce ?

I really love your videos I would like to know if it is possible to watch them in French or at least with subtitles so that we can follow

really good summary thankyou!

What programming language is she using??

Great video

Do a video explaining AES!

Interesting video!

Good video :)

can anyone please suggest books to learn about distributed systems?

She's damn good at explaining and easy to listen to, any plans of having her host other episodes? (sorry for "her" I don't know her name).

feels like this video is four years too late ... :-/

Good old Scala.

Was so excited to see this posted :) I'm a Cassandra professional.

its bit silly but i cant understand 100% because english isnt my first language , hope someone could add english subs on every this channel videos because i found computerphile videos are easy to understanding because excellent explanation

Computerphile will be excited to learn that tripods exist.

For anyone interested, although the documentation is awful for Apache Flink and it doesn't support Java versions beyond 8, it at least lets you do setup on each node. Spark does not have any functionality for running one-time setup on each node, which makes it infeasible for many use cases. These distributed processing frameworks are quite opinionated and if you're not doing word count or streaming data from one input stream to another with very simple stateless transformations in between you'll find little in the documentation or functionality. They're not really designed for use cases where you have a parallel program with a fixed size data source known in advance and want to scale it up as you would by adding more threads, but more for continuous data processing.

This was very helpful

woohooo rebecca is back

More like this!!!!!!

Thanks, nice vid.

yeah vertical scaling and modular based data handing similar Hadoop ,Hive. framework library.

Apache Flink next please

1:19 Floppy drives? xD LOL

Looks like you could do a search engine in that.

"RDD is basically an array distributed across the cluster" - genius

Would have liked it to be a bit more in-depth and technical, was too high level.

Please show some drawings or animations of data going back and forth between the noded.

The first time I learned about Apache Spark, I was looking up documentation for another framework named Spark.

thanks

Ohhh, she is using VSCode! I love VS Code :D

Hi friends!

I study bioinformatics handling txt files many gigabytes in size and this could be so handy

content is nice, well explained. BUT the camera and editor are so bad. We are not here for a documentary, the computer shot from her shoulder is completely useless and distracting, if you want to use your cuts, use something like the picture in picture but please let us focus on the code!!

@3:16 line 12 is wrong. Great review 👍 otherwise!

First? Does this matter? No. Go build a cluster and be happier!

00000001

21st!!!

First? sorry, I've never watched a video when it said it was posted "25 seconds" ago, and so it would be weird if I were actually first. Good Video, I feel like I stink at data analysis, but I'm more experienced than most in my organization so...

What useless video : - slow down, explain slow , assume audience know not much

first!

First 😂

totally lost me 3 min into this video.

She's mumbling in the beginning... can't really hear her (American-born English speaker)