Apache Spark Vs Apache Flink - Looking Through How Different Companies Approach Spark And Flink

Подписаться 95 тыс.

Просмотров 3,8 тыс.

50% 1

As data increased in volume, velocity, and variety, so, in turn, did the need for tools that could help process and manage those larger data sets coming at us at ever faster speeds.
As a result, frameworks such as Apache Spark and Apache Flink became popular due to their abilities to handle big data processing in a fast, efficient, and scalable manner.
But we often find that sometimes it can be difficult to understand which use cases are best suited for Spark as well as for Flink (or even which might be suited for both).
In this article, we’ll discuss some of those unique benefits for both Spark and Flink and help you understand the difference between the two, and go over real use cases, including ones where the engineers were trying to decide between Spark vs. Flink.
Also, thank you to the sponsor for this video Deltastream, you can try them out for free here - www.deltastream.io/trial/?utm...
If you enjoyed this video, check out some of my other top videos.
Top Courses To Become A Data Engineer In 2022
• Top Courses To Become ...
What Is The Modern Data Stack - Intro To Data Infrastructure Part 1
• What Is The Modern Dat...
If you would like to learn more about data engineering, then check out Googles GCP certificate
bit.ly/3NQVn7V
If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here.
seattledataguy.substack.com/
Or check out my blog
www.theseattledataguy.com/
And if you want to support the channel, then you can become a paid member of my newsletter
seattledataguy.substack.com/s...
Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio
_____________________________________________________________
Subscribe: / @seattledataguy
_____________________________________________________________
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.
*I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

Опубликовано:

6 июн 2024

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист

Посмотреть позже

Комментарии : 20

@danhorus 28 дней назад

13:03 in Spark, we avoid Python UDFs like the plague because they're much slower than native Spark code. I wonder if the same is true for Flink, given that it also runs on JVMs. A quick Google search indicates that vectorized UDFs are a thing in Flink too, so I assume the same limitations apply

@SeattleDataGuy 28 дней назад

Thanks for the added context! It's much appreciated I now am thinking if I have ever had a good experience with a UDF 🤣. I always remember touting them, but even in one case where i do recall trying it out on SQL Server, we found it slow.

@danhorus 28 дней назад

@@SeattleDataGuy With Spark, there are several ways to write transformations. By far, the best option is to use native Spark functions, as they compile to highly optimized and parallelized Java byte code. The second best option is to write UDFs in Scala or Java, as everything still runs in the same JVM. The third best option, in case you want/need to use Python, is to write a vectorized UDF (also known as Pandas UDF), which leverages Apache Arrow to move data between the JVM and the Python interpreter in batches. Finally, as a last resort, you can use regular Python UDFs, however they're a lot slower because they basically compute results row by row rather than in big batches. If you have slow Spark jobs using Python UDFs, refactoring them is usually a good way to gain some performance. About this blog post, I'm not sure the author is aware of this limitation, but if they need this code to run very very fast, they should probably avoid Python UDFs too

@danhorus 28 дней назад

@@SeattleDataGuyI wrote a long comment about the different types of UDFs in Spark, but apparently RU-vid decided to delete it. Maybe you'll find it marked as spam, lol

@SeattleDataGuy 28 дней назад

@@danhorus Did you put a url in it? That seems to be the main reason I have seen youtube define things as spam. I'll look

@danhorus 27 дней назад

Not really, but let's try again, haha. In Spark, there are many ways to apply data transformations. By far the best option is to use native Spark functions, as they compile to highly optimized/parallelized Java byte code. The second best option to maximize performance is to use Scala or Java UDFs, as they run inside the JVM with a minor performance hit. The third option, if you want/need to use Python, is to write a vectorized UDF (also known as Pandas UDF), which leverages Apache Arrow to transfer big batches of records to the Python interpreter and back to the JVM after processing. Finally, the last option you should consider is the regular Python UDF, as it basically transforms row by row and has much worse performance as a result. If you have a slow Spark job, refactoring Python UDFs can make it a lot faster. I'm not sure the authors of the blog post are aware of this, but they can probably make their code faster too

@osoucy 24 дня назад

To me, one of the main benefit of Spark Structured Streaming is that you can easily switch between near real-time (micro batches) and scheduled batch processing without having to re-writing a single line of code. This is a very effective way of scaling up and down and balancing costs vs latency.

@SeattleDataGuy 9 дней назад

that is very useful! when do you think micro-batches make the most sense