Prática de Shadowing: Databricks Interview Questions Part 1 - Aprenda a falar inglês com vídeo

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hello and welcome to everybody on cloud fitness

so this video is a very on demand video where i

am starting a play series on the data factory interview questions

so a lot of you have sent in emails to me right

you know to make videos on azure data factory

and databricks interview questions so

that is why i have already created a video of a playlist for azure data factory

and this one is for the databricks so let's move ahead

and see what are the questions

that you know we we can start off from this particular video right

so the very first basic question that I have is what is Databricks

so this question might be asked in the interviews just to understand how much you know Databricks

so you should be able to answer it really well right

so when we talk about is your Databricks it is an Apache Spark based environment

that is what the like the very first thing is

that you should know that it is an Apache Spark based environment it is a collaborative environment where when I say collaborative,

it essentially means that you have your data engineers, you have your data analysts,

you have your data science people coming in all together at the one place and working on the data, right?

It is an Apache Spark based big data analytics service.

When I say

Apache Spark, whatever code that you write in Databricks, that is basically run on Spark, right?

It is a Spark code that you write.

Now, apart from that, it is a big data analytics service it's not just the structured data

that you can process it's you can process semi -structured

and even unstructured data as well in the databricks now the second thing is um you know that

it supports multiple language

so it is not very much language bound you can work on scala

you can work on r you can work on python you can work on sql as well

so these are the things

that you should definitely know about databricks as well as it

can you know easily integrate with your other Azure services like

your you know your active directory your keywords your storage accounts

if you have data in your storage accounts you can directly go ahead link it

and use that data right so it is a very essential

and useful SaaS offering so in fact let us go to the second question

that is also very commonly asked because

when you are writing Databricks in your resume what usually happens

is a person who has worked on Databricks essentially would know about Data Factory as well

So this is a very common question that what is the difference between Databricks and DataFactory?

Because whatever code you write in Databricks, let's say you're trying to do a filter,

group buy or anything, that can essentially be done in DataFactory, right?

Or vice versa.

So why do you need Databricks when it can be done in DataFactory?

So you should be very clear.

It is essentially a very, you know, both are actually different offerings.

The very first difference is the purpose, right? the data bricks has a different purpose

and data factory has a different purpose data factory is essentially

for orchestration for the data flow for the data transformation for the code free data transformation

and orchestration but

when you talk about data bricks i have already explained

it is an collaborative platform where multiple people can come in together data science team can come

and they can train their models they can train their models in data bricks

when you talk about data engineers they can write a code solution

so code solution is actually not bad

because you have a lot of flexibility uh you know with

when you write your own code which you are

which you don't get with the data factory right so

that is essentially one of the reason that most of the organization

when there is any kind of you know data engineering task

or data engineering transformations

that have to happen on the code

they prefer using data bricks uh rather than data factory data factory is mostly

you know most of the organization they use for uh data movement

and orchestration purpose so this is the very key and major difference

when you talk about data breaks and the data factory

and of course

when you talk about data processing as well right in data uh breaks

you can process not just your structured semi -structured

or even your you know your batch data you can easily uh

you know use your streaming data as well though in data factory also

you have an option uh to use streaming data

but live streaming is possible only in databricks as of now right

so even when you talk about data processing databricks has an advantage

but yeah these are the two uh major things apart from

that there are cosmetic things that you can talk about like you can say

that data factory uh you know is a code free etl solution

and those kind of things but these two are the major major differences

that you should definitely cover

and then a very you know common question is describe spark architecture

so if you're working on databricks you should know spark architecture right

so i have already in fact explained about the spark architecture in one of my videos let me check that out

So if you see this runtime architecture of Spark Databricks, I have already explained, you know, in detail regarding the architecture of Spark.

If you want to know details on this, you can go ahead and you can, you know, just watch out this video as well.

In this, I have, you know, explained everything about the runtime architecture of Spark, how everything works and how you're.

In fact, I created this diagram to show, you know, the flow of the basically flow of,

you know, whenever you write a query, what is the flow that happens in your data breaks?

So going back to the PPT, if you see over here, right, when I talk about a Spark architecture,

what essentially happens is you write a code.

I'm actually trying to explain this in very short

if you want to have a very detailed explanation you can watch

that video so

when you talk about spark architecture you write your code whenever

you write your code you write it through spark context you uh you know you write it on spark context

and with the help of driver program driver program actually

or uh you know you you write it through spark context

you know whenever you must be writing code in data breaks you you would write

that spark .rd right sc .parallize so SE is basically spark context right

so you create a driver program

and then behind the scenes what happens is spark driver submits the job submits your code

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you know submits your code to dark scheduler so

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when I say dark scheduler dark scheduler is nothing

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but directed acyclic graph scheduler

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so what it what it does is it essentially takes your code what you have written

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and it tries to create a logical and physical plan out of it

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when i say logical and physical plan out of it it tries to optimize your code

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and tries to create a plan out of it on how it will execute your code

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now once it has created your physical and the logical plan then what happens it it says the

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you know it breaks your code in form of tasks

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and your stages right it breaks your code in form of stages

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and tasks and these tasks are actually sent to your executors

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or to your worker nodes for the execution purpose right

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and then from there executors actually write your data to whichever location you have specified

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and in between cluster manager actually manages your you know all the worker nodes

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and the executors right

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so this is a you know very uh short introduction of how spark architectures behave

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you have your code you write it through you know spark context

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you have your driver program you send your code uh

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you know then you have your cluster manager cluster manager tries to you know make sure

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that you have worker nodes ready

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and like it manages your worker nodes basically worker nodes are nothing

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but a set of machines where your actual code will execute

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now your code is there with the help of spark driver it is sent to

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that scheduler which creates logical

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and physical plan out of it once it is done

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it actually you know segregates your code in form of stages and tasks

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which are supposed to be executed on the executors

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then all those tasks through the with the help of task

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scheduler are sent to your executor nodes for the execution purpose

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so this is basically a very short introduction of spark architecture

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and then coming on to the next question is what is the maximum size of worker node that you have used

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Now, this is a very, very, very important question.

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In fact, the most important one, I would say.

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Now, this essentially shows everything, you know, about how much you have worked on Databricks and how much you know about Spark.

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Essentially, this question answers everything.

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So, when I ask or when anybody asks you what is the maximum size of worker node you have used,

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it shows how much data you have been using it shows

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you know how you how good you are in configuring the cluster

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it shows how much you understand the spark architecture right so whenever this question is asked you should not

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exceed the worker node configuration more than 64 gb that is something

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that you should keep in your mind what is the maximum size of worker node

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you have used essentially it depends on your project as well

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but most of the project they do not need more than 64 gb

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or worker nodes right because what happens is

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if you increase the size of your worker nodes more than 64 gb there is a garbage garbage collection overhead

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which reduces your performance and mostly you will not need

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that right you will not need more than 64 gb after

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this you can you know go ahead increase the number of worker nodes

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or you know you can increase the number of cores you have

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and there are a lot other optimizations to be done

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but 64 is a cross checkpoint like more than 64 gb is actually not required

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and in case if you want to know more more about this why in more than 64

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and all for that also i've created you know, one video on how to select worker and driver type in Databricks.

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So if you go through this particular video, you will understand end -to -end how to choose a cluster, basically.

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How you can go ahead, choose a cluster, and what cluster configuration best suits for each workload.

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But yeah, a straightaway answer is 64 GB.

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If you want answer, detailed answer of why, you can go ahead and check the video out.

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And then you have the next question is, how do you choose cluster configuration?

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So when I say how do you choose cluster configuration, again, this is a very important question and it has,

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you know, you can speak about it for almost half an hour.

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But yeah, again, for choosing the cluster configuration,

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you should understand that whenever you are trying to do any normal ETL or ELD work,

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in that case, you should go for memory optimized clusters right after

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that whenever you have some development tasks that you need to do

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and quickly check it out in

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that case always go for general purpose clusters then the third thing is

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if you have a strong ETL task or ELT task

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which requires a lot of shuffle it has a lot of joins in

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that case always go for storage optimized cluster

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so these things you should always remember these are the key points actually in fact i can actually go

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and show you so whenever you are in the databricks portal

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and you try to create a cluster

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so let me try to create a cluster and in fact show you

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if you go to the worker type

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or the driver type you can actually see there is general purpose

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there is general purpose hdd hdd you should never choose never choose an hdd option

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then you have this memory optimize over here and then after you have storage optimized right

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So these things you should always keep in mind that before attending any interview for data breaks.

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So this really makes sense that for any normal EGL workload, you should always use memory optimized,

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storage optimized for a shuffle intensive workload because it has a disk attached to it.

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So it can easily speed up your process of any shuffle that is required.

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That is why you should go for a storage optimized once in case of shuffle intensive work.

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So, this is pretty much that I wanted to cover in this particular video.

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And thank you so much for being till here.

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Do let me know in the comment section if you have any issues, any doubts.

185

And do remember to like, subscribe and share this particular channel.

186

Thank you so much.

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Vocabulário e dicas de fala para esta lição

Esta aula de conversação usa o vídeo “Databricks Interview Questions Part 1”. As palavras que mais se repetem: spark, code, databricks, cluster, write. Este vídeo tem 186 frases e 2275 palavras para praticar shadowing. A fala dura 12:27. O falante fala rápido, cerca de 183 palavras por minuto, então espere sons ligados e reduzidos. 84% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.

Vocabulário principal deste vídeo

15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:

Palavra	Pronúncia	Significado
spark substantivo	/ˈspɑɹk/	faísca, fagulha
cluster substantivo	/ˈklʌstɚ/	aglomeração, aglomerado
worker substantivo	/ˈwɝ.kɚ/	trabalhador
architecture substantivo	/ˈɑː.kɪˌtɛk.t͡ʃə/	arquitetura
essentially advérbio	/ɪˈsɛnʃəli/	em essência, essencialmente
configuration substantivo	/kənˌfɪɡ.əˈɹeɪ̯.ʃən/	configuração
brick substantivo	/ˈbɹɪk/	tijolo
logical adjetivo	/ˈlɑd͡ʒɪkəl/	lógico
submit verbo	/səbˈmɪt/	ceder, entregar-se
transformation substantivo	/ˌtɹæns.fɔɹˈmeɪ.ʃən/	transformação
execute verbo	/ˈɛksɪˌkjuːt/	executar
collaborative adjetivo	/kəˈlæb.ə.ɹə.tɪv/	colaborativo
execution substantivo	/ˌɛk.sɪˈkjuː.ʃən/	execução
garbage substantivo	/ˈɡɑɹ.bɪd͡ʒ/	lixo
analytics substantivo	/ˌæn.əˈlɪt.ɪks/	análise

Phrasal verbs que você vai ouvir

Palavra	Pronúncia	Significado
go ahead verbo	/ˌɡoʊ əˈhɛd/	vai em frente
come on to verbo		dar em cima
watch out		cuidado!

Gramática neste vídeo

As estruturas que o falante mais usa, com as palavras exatas do vídeo:

Estrutura	No vídeo
Present perfect have/has + particípio passado — uma ação passada que ainda importa agora	have sent · have already created · has worked
Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz	might be asked · be done · can be done

Pronúncia para ficar de olho

O falante usa 6 contrações e formas reduzidas, como you're, don't, I'm. Diga-as na forma curta, do jeito que você ouve.

Os sons de “sh” e “zh”: essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, execution /ˌɛk.sɪˈkjuː.ʃən/, explanation /ˌɛkspləˈneɪʃən/
Palavras longas — acerte a sílaba tônica: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, collaborative /kəˈlæb.ə.ɹə.tɪv/

Sons difíceis para falantes de português:

Consoante final — sem acrescentar um “i” depois: spark /ˈspɑɹk/, node /noʊd/, brick /ˈbɹɪk/, submit /səbˈmɪt/, execute /ˈɛksɪˌkjuːt/
/l/ final — a língua toca o céu da boca, não vira “u”: logical /ˈlɑd͡ʒɪkəl/, shuffle /ˈʃʌfəl/
/h/ — um sopro suave, diferente do “r”: behave /bɪˈheɪv/, overhead /ˈəʊvəˌhɛd/

Como praticar com este vídeo

Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
Grave a sua voz e compare com o original, prestando atenção a palavras como spark, cluster, worker.

O que é a Técnica de Shadowing?

Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.

Técnica de shadowing: leia o guia completo passo a passo →

Prática de Shadowing: Databricks Interview Questions Part 1 - Aprenda a falar inglês com vídeo

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