Shadowing Practice: Learn Databricks for FREE (Step-by-Step Guide) - Learn English Speaking with Video
Ders oluşturuluyor...
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hey friends so now i'm gonna show you exactly how to create a free databricks account
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and after that i'm gonna guide you quickly into the interface of databricks
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because if it is your first time it can feel a bit overwhelming
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so now let's get started i guess it's starting now before
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we start creating any account i would like you to understand something important
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so that you understand what you are actually creating
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so now the thing is databricks is not a tool
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that you're gonna go and download
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and install locally at your pc instead it is a platform
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that you're gonna simply open in your browser and start working with it
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so that means it doesn't matter whether you are a windows user
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or a mac user you can work with databricks
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and of course you don't need any super computer to use databricks
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because we are not running anything locally at your pc now
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of course you're gonna ask okay how we process actually the data we need things like cpu memory storage
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so where we're gonna get those
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if we are not using your local pc now the thing
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is as well in data bricks itself it doesn't go
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and build like data centers
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and servers in order to process the data the idea is very simple it is a platform
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that is built on top of other cloud platforms like azure aws
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and google clouds so
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that means it is not like stand alone platform it will be using other cloud service providers
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so now because of all of
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that Databricks is not like fixed price we pay my friends on the usage
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so the more compute and storage
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that we use from the cloud the more it gonna cost us
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and now of course I might get you scared I'm gonna think okay
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if I need to learn Databricks I have to go
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and pay for all those compute and storage
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and the cloud I have to configure the whole setup well
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I have for you good news only recently Databricks now offers
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a free edition where everything is prepared for you to learn databricks
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so that means you're gonna get an access to databricks the same platform
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that we use in real projects with 90 of all features
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and this is more than enough actually to learn databricks
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and currently behind the scenes it is connected to aws cloud platform
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so everything is prepared with only few clicks you're gonna get the whole setup
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and yes my friends for free databricks is covering all the compute
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and storage that we're going to use in the aws
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but of course there will be some limitations about creating high -end fast clusters
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because they are very expensive and they don't allow you to do
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that so this can be for personal use
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and as well for learning purposes
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but on the other hand we still have the professional setup
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if you are working in a company then it really depends
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on the cloud platform your company is using like here in europe mostly we are using the azure platform
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so you can connect databricks to any of those cloud providers
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and run everything there but of course you have to pay for it
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so let's go and create that
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so let's go to the home page of databricks .com
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and then we have here on the right side try databricks let's go
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and click on it okay so the first thing
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that we have to do
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if you don't have already an account with databricks you have to go
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and sign up so let's go and do that and now after
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that we clearly have two options either we can use it for personal use
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or for work for personal use it's going to be for free
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so if you are just learning databricks i'm gonna say go with this edition but
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if you're gonna use it for work here actually you have two options either you can use the express edition
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so everything is already prepared with the aws you don't have to go
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and set up your own cloud so everything is there
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and by the way they are currently giving free for 14 days
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and some credits or you say you know what i have my own cloud
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so i have already an azure account or aws
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and i would like to go and connect it to databricks
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and with this actually you can unlock the full professional version of databricks the enterprise edition
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that we actually use in companies actually here we have three options the free one the express edition
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and the full enterprise edition pick the one that suits you and go
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and create the accounts all right after creating the accounts finally we're gonna land inside a databricks interface
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and as you can see we are just using the web
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browser without any installations now i know the interface might be overwhelming
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because we have a lot of tools inside databricks we're gonna do it step by step
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so don't worry about it now the first thing
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that we might notice is to go to the right side over here
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so we can see the workspaces now since you are using
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a new free account you might see only one workspace
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and that's okay
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but once you start working in re -projects you might get an access to multiple databricks workspaces
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and in order to switch between them you will be using here the workspace now the next thing
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that we can check is actually the settings so
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if you go to your icon
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and then to the settings you're gonna get here in middle
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a new interface where you're gonna get the usual stuff about
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configuring the profile the appearance for example you can configure the date
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and time formats you can go and manage the access to your workspace
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so you can go and make groups and add users to it
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and many other options about the security the compute for now
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we will not change anything we're gonna leave it as it is the only thing
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that i might say it is interesting for this phase is to go to the linked accounts
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And here what we can do, we can go and connect one of our GitHub repositories the databricks
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so that anything that you do inside databricks will be stored
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and saved inside your github repo and as well you have the version control
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so if you have one already you can go
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and connect it to your databricks account
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so as you can see we don't have any crazy things
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inside the settings it is the classical usual things now we're
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gonna go to something really interesting in the interface by the way this is completely new
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if you go over here to this icon it is the genie code
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so let's go and open it so basically the genie code is the ai assistant
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that is inside databricks that's gonna help you work with data and code
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so here we have a basic interface where you're gonna start writing your prompts like for example what is databricks
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and what is very interesting you can decide between those two
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so either an agent or a shot a shot it's just simple like question
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and answer like we do in chat gpt
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but an agent it is way more powerful it can go
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and handle for you a task so
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that means you can give it a goal and it's gonna go
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and complete it step by step
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and this could be anything like creating pipelines creating for you a script a notebook exploring the data building visualizations
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and charts so this is very powerful things
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and as well very new in data breaks
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so you can write your prompts by asking questions
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or giving it a task as you can see we got an answer for our question
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and later we can start giving it command in order to do things for us in databricks
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so this is where you can interact with the genie code
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now let's go to the landing page of databricks now my
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friends the most important part in this interface is actually the left navigation bar
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because you're gonna see a lot of tools and things in databricks
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and since we have many things databricks is gonna go
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and group them into like sections
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so the first set of tools they are the main section then after
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that we have a lot of tools
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that are dedicated only for the sql a whole section for sql
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and another one for data engineering and the last one for ai
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and machine learning so with that you're now you are getting the feeling
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that i can use this platform actually for many things for
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data engineering data analytics data science machine learning even ai engineering
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now before we start talking about all those tools usually
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if you are learning any new data platforms you have to
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think about three things where is the data where is the compute
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and what tools do i have in order to use my data
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and the compute to do my projects
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so let's start with the most important question where is the data in databricks
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and you can find that in the main section in the catalog
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so this is the most important component in the interface the catalog of databricks now
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if you open it you're gonna have like two sections on
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the left side it's gonna look like a traditional database
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so you have like hierarchy and you can start browsing this catalog by just expanding things
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so at the start we have the my organization
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and then we have different workspaces and
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if you start like expanding you're gonna find inside them schemas
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and inside the schemas guess what we have tables
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so we are just browsing like the structure the hierarchy of the catalog
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and by the way the data that we see underneath samples it is something
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that prepare from databricks but course you can go
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and bring your data to the catalog now
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if you want to see more details about each of those objects you can go
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and click for example on the schema
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and then on the right side you're gonna see more details about it like what are the tables inside it
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and then here more details about the storage the permissions the policies
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so you're gonna have the details on the right side same things
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if you go for example to a table you're gonna see
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more details like the history the lineage the insights quality
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so we are seeing here all the details same thing
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if i go for example to the workspace we're going to
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see as well what is inside the workspaces with more details
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and permissions so that's it this is the catalog where the data lives in
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and of course we have to deep dive into this later
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because this is the fundamentals that we have to learn how to organize
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and structure our data inside Databricks
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so this is about the data now the second most important
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thing to check is where is the compute where are the resources the powers
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that we're use in order to process our data
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and here in databricks we have like let's say two tools
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for it in the interface in the main section you can see here we have compute
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and as well in the sql we have the sql warehouses now let's go inside the warehouses
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so now whether you are using the free edition
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or the express like me you're gonna find that
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that are already created for us like a warehouse
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so this is our compute you can see the status is offline
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so it stops we see the name we see the size it is very small
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and the type the serverless and at the end you can go
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and start the warehouse now my friends the compute is usually the most expensive things
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that we could use in the platform that's why databricks has a lock in you cannot go
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and change the size of your warehouse or you cannot go
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and create other computes
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because then the costs of the free edition is going to be very high
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but now for me since i'm using the express edition i can go
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and create a new warehouse or change the size of it.
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And of course, don't worry about it.
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It is more than enough to practice in Databricks.
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You are not processing terabytes of data you're gonna use some medium data sets in order to explore the platform
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and learn the features inside it so that's why it is for you frozen
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if you are using free edition but for me I can go
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and create here like for example new warehouse now look at
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this this is exactly why those data platforms are speeding up a lot of projects
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so we are just deciding on very simple things like for example the name of the warehouse
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and as well the cluster size that's it everything else is
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going to be done behind the scenes like configuring the servers
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the infrastructure the sizes of the workers the scaling their resource management
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and monitoring everything going to be done behind the scenes
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and we don't have to configure anything only the size of our compute
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and for the free edition you cannot do that of course
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and with this you can have some understanding how easy is things in such platforms
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so i will not go
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and create anything i'm going to stick with the default one
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that's it as you can see the status is already online
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so the server is ready to be used
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and i can use it everywhere i can use it in
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order to execute my notebooks my pipelines my sql queries the dashboards the ai
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so all the tools
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that i have over here on the list side it's going
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to be powered by this sql warehouse of course in the enterprise edition you can go
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and create multiple different types of clusters you can see here we are using the serverless
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but there are other types for different purposes
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but currently this is one of the best types in order to use both sql
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and python by spark on all other tools
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so as you can see things are simple and easy it's not that hard
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but don't forget behind the scenes we still have this big data engine the spark with all data distribution
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and parallel processing and stuff that is running as you start the warehouse okay
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so with this you know where is the data where is the compute
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and of course the question is
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which tools i'm gonna use in order to do my projects
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my job well of course this depends on what you plan to do
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if you want to do data analytics data engineering science ai
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engineering you're gonna have different tools to use now let's say i am data analyst
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and i would like to analyze the data inside the data bricks then of course beside the catalog
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and the warehouse the most important section for me gonna be the sql section
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and everything else is not that important or let's say you can go
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and ignore so what do we have here the first one is the sql editor
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so here again you're gonna see our catalog in order to navigate through your data
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and in the middle you can go and write sql query
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so it looks like any traditional sql databases you will just go
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and write here an sql query from table and then you're gonna go
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and execute it and see the result in the output so
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that you are like exploring and analyzing your data
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that you have inside the catalog
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and i can say this can be your main tool in
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order to interact with the data to do data exploration
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and data analyzes so here it's like a place where you're gonna go
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and store all your sql queries as you can see i just created one
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and we have now a query for
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that it just gives you an overview of all the queries
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that is used inside databricks for now it is empty
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because we didn't do yet anything so i think but not
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that important now we go to something really nice
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that dashboards my friends as a data analyst you can actually go and build dashboards like i'm not
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very strong in order to do data explorations
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and let's say to create the first drafts for the analyzers
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and here we have some examples I'm gonna go
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and just click on one of those samples it might take some time in order to query the data
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and present the result in the dashboard look at this we
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have a dashboard where you have a title for it you can go
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and filter the data you have big number some details dot plot
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and analyzes over the time but again not pixel perfect like power bi
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or tableau but still you can build the first visuals
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and dashboards directly inside the same platform where you are working
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with the data without having you to jump to a new tool
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so this is just a dummy dashboard
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that we get from databricks now let's go to more interesting
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stuff for you we have something called genie now of course at the start everything gonna be empty
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but what you actually do you can go
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and create an ai bi genie by just selecting any of your data
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and now look at this this already looks like shad gpt
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so this is another ai assistance where you go
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and define a subset of your data that you want to analyze
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but this time without writing any code you will just go
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and use the natural language in order to ask any question about your data
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so what is the total number of rows inside my table
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so look at this we have already answered the total number of rows in your table is 750
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and if you go and expand this you can see it as a table
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and as well you can see the code
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that is generated behind the scenes and it is sql
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so the genie gonna generate from our prompts and sql query
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that gonna use our warehouse in order to interact with the data
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and you can see the code is totally correct crazy stuff right
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so now let's keep going the other things is not
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that important so the alerts over here now we don't see anything yet
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but what you can do we can go and create an sql query
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and based on the thresholds it can go and give us notifications
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and alerts so again based on sql query
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and the last one you can see the query history
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so here you can see a full log and protocol of all queries
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that got executed in your databricks it's like we are monitoring let's say what is going on
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so can see the query when it started the duration on
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which data sets what is the resource that is used to compute
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and who did that so again as i said some kind of monitoring it is not thing
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and the last one we have the sql warehouse
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so my friends for you as a data analyst we have three main tools in order to explore
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and analyze our data so either we're going to go
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and use the sql editor in order to write simple queries
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or we can go and build a dashboard in order to analyze our data
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or a third cool option is that you're gonna go
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and use the ai bi genie where you're gonna go
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and connect your data to the ai and start having simple chats
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so that's it those are the tools
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that you have inside data bricks for you as a data analyst in order to work with your data okay
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so now let's go and switch and let's say
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that you are a data engineer
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and you would like to use data bricks in order to build your projects
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so which tools you have to focus on again the catalog is the most important thing
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because your main job is actually to build the catalog for the others
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and the compute we talked about it
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so either you can use the compute here
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or the sql warehouse now the third component
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that is really important for you to understand how to deal with it is the workspace
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so now what is our main job as data engineers we go
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and write codes so we need a place for that
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and this is gonna happen inside the workspace so here we can go
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and create notebooks write codes and organize the whole projects
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so we can go and create folders and put inside it our python
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or notebook codes and by the way all other interactions
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that we are doing like for example writing an SQL query
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building a dashboard creating a new genie you're gonna find all those stuff inside the workspace again
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so that you can go and organize it
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and reuse it inside your projects
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so look at this we have the genie space inside the workspace
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that we just created same thing goes for the dashboards and
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if i do any queries it's going to be landing inside
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this workspace now of course at this level at the home anything
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that i'm creating in notebook code it's going to be stored inside my account in the databricks
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but of course usually we don't do that we usually organize
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and store our code inside github repository and for
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that we have a section over here called workspace and then repos
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so if you go inside it now you can go
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and connect your github repository inside databricks
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and inside it you will be creating your code and project
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and with that you have version control on your code
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and your work will not stay only inside your account in data breaks
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and there are many other like not important things like for example here you can see all the notebooks
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that are shared with you and so on
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so again workspace is the place where you organize your projects
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and to be honest as a data engineer i spend most
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of my time just working in this component inside the workspace now the second thing
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that we usually do is we go and create jobs
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and orchestrate our work so in this place the automation of my work can happen
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so i can go and create a pipeline
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and then schedule it to run automatically instead of me doing everything manually
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and here in the interface like we have different types on
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how to do this the most like important one i'm gonna say the jobs this is the way
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that i use in order to orchestrate the notebooks and codes
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that i'm creating so now like any data pipelining
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and orchestration i'm gonna go and connect all the notebooks
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that have created at the workspace and start connecting them one after one
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and at the ends what we're gonna do we're gonna go
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and add a trigger for it to run automatically like for
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example here we have different options the scheduled one i'm gonna say okay run it once every day
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so i'm gonna say this is very simple for data engineering
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to create an automated pipeline now for you it can be empty
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but previously i have created something in order like just to
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test now we can see like more details about my pipeline
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and in the task you can see here i have created
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like like three notebooks that's gonna build for me a data leak house
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so I'm gonna say this is the easiest thing
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that we do in Databricks
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and data engineers in order to create a visual data pipeline
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now what else we do as data engineers after we write the code
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and build the pipelines we have to monitor every day whether everything is correct
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and for this in Databricks we have a dedicated section in
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that data engineering so here we have something called runs
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and this kind of gives you a nice
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and easy overview of the health of your jobs like for example here i tried to run it twice
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and as you can see the status is failed of course you can see the start time
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which notebook the type who run this and so on
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so again here is the place where we can monitor our jobs
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and do the operations and if something is wrong
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so it's not green you have to go
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and debug where exactly the issue happens
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so actually that's it those are the main components
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that are specially used by data engineers and data bricks
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so the workspace where we go and write our logic our code
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and organize the whole project and then we go
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and connect everything to automate our code by building jobs
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and pipelines and at the end so we go
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and do the operations by monitoring the health of the jobs okay
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so now let's move to the AI
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and machine learning part this is mainly used by data scientists ML engineers
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and AI engineers each role is slightly different
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but they all work on building models
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and intelligent system on top of our data
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so let's go through the components that are relevant for them
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so again they will be using workspaces because they're gonna go
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and write codes so they have to write notebooks in order to explore data train models
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and write code in python and of course they are interacting with data
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so the catalog is again important the compute in order to execute things
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but at the end over here we have a whole dedicated sections for ai
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and machine learning
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so what do you have here the first one is the
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playground this is where you experiment with the ai models especially the language models
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so you can test prompts you can try a few ideas quickly see how the model responds
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so it is mainly used for exploration
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and testing now the next one is actually new one the agents
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so again agent it is an advanced ai system
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that actually gonna go and do something you give it a task
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and it's gonna go
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and complete it step by tip it is more than just conversation with the ai
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and here we can see that databricks is already like suggesting for us ideas
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and some common use cases like the document parsing where you can go
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and give the ai your documents like pdfs and images
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and you can use it in order to start extracting the data from them another one we have information extraction
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so again the same thing you're gonna give it your documents
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and extract some key features and classifications another one
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which is very famous one where you can go
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and build a shot bot based on your documents
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so again you can upload your things
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and start having conversation with the ai about the contents of your files
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and then the last one we already talked about it the ai bi genie we're gonna go
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and convert your text to sql and many other things
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so i'm gonna say
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if you want to work with ai in databricks this component is gonna be the most interesting one
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so what else do we have we have the ai gateway way.
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It's all about managing the AI models that are used in your projects.
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It's like the other monitoring tools that we saw inside Databricks but this time for the AI models.
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Now let's go to the next one.
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The experiments.
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This is mainly used by data scientists in order to track the training of the models.
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Go and compare the different trends
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and parameters of the models in order to see at the end which model performs the best.
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Of course we don't see anything yet because we didn't train any model
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so we cannot see the content now let's go to the features
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so this is something classical for machine learning engineers where you're gonna go
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and store the features that you extracted in order to reuse it later
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so that you don't have to go and rebuild them every time
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and the next one we have the models
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so again it's all about the ai models
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so here you're gonna see where the models are stored
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and versions you can go and manage the different versions
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that are used inside your projects track the changes and reuse models when needed
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and finally we have the serving this is where you deploy your model once the model is ready you go
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and expose it as an api so the applications can go
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and use it in real time
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so as you can see the full section is all about ai
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and machine learning it is the full life cycle from experimenting to building managing
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and finally deploying the ai and machine learning solutions and
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if you are not data scientist
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or ai engineer i can tell you the most interesting one gonna be the agents all right my friends
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so that was a quick tour into the data breaks interface as you can see there are many things
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that we can do inside this platform
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and i totally understand at the beginning it might looks a lot
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and you might get overwhelmed with all those things
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but now you have at least a clear idea on what each component is doing
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and who uses what and now my friends there is something
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that i really wanted you to understand that we don't just go
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and learn everything that we see inside data bricks we focus on the parts
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and the components that actually match our role
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so now what i want you to do next based on your goal
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and your role whether data analyst engineering scientist ai engineering go
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and explore the platform and the interface on your own
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so i want you to take few minutes by clicking around
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and trying all those components
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that i just showed you in order to get comfortable with the interface
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and don't worry about it you will not break anything at the end
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if you are using the free edition you are not paying for anything
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so this is what you're gonna do next but before
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that there is something important if you enjoy this type of content
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and you would like to support my work then subscribe like
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and comment this really helps in order to reach nice people like you thank you
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so much for watching and i will see you in the next video bye
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Bu dersin kelimeleri ve konuşma notları
Bu C1 seviyesindeki konuşma dersi “Learn Databricks for FREE” videosuna dayanıyor. En çok tekrarlanan kelimeler: databricks, inside, sql. Bu videoda gölgeleme çalışması için 449 cümle ve 5213 kelime var. Konuşma bölümü 24:48 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 210 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %87’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodaki en ileri düzey 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| workspace isim | /ˈwɝkˌspeɪs/ | çalışma alanı |
| compute fiil | /kəmˈpjuːt/ | hesaplamak |
| query isim | /ˈkwɪɹ.i/ | sorgulama |
| warehouse isim | /ˈwɛə(ɹ)haʊs/ | antrepo, depo |
| catalogue isim | /ˈkæt.əˌlɔɡ/ | katalog |
| notebook isim | /ˈnoʊtˌbʊk/ | not defteri, defter |
| dashboard isim | /ˈdæʃˌboɹd/ | kadran, gösterge panosu |
| genie isim | /ˈd͡ʒiː.ni/ | cin, çor |
| pipeline isim | /ˈpaɪpˌlaɪn/ | boru hattı |
| configure fiil | /kənˈfɪɡə(ɹ)/ | yapılandırmak |
| execute fiil | /ˈɛksɪˌkjuːt/ | idam etmek |
| azure isim | /ˈæʒɚ/ | gökşin, gökçe |
| schema isim | /ˈskiːmə/ | şema |
| cluster isim | /ˈklʌstɚ/ | salkım |
| extract isim | /ˈɛkstɹækt/ | ekstre |
Duyacağınız deyimsel fiiller
| Kelime | Telaffuz | Anlam |
|---|---|---|
| speed up fiil | /spiːdˈʌp/ | hızlanmak, hızlandırmak |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | is built · is prepared · is connected |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | have already answered · have created |
Dikkat edilecek telaffuzlar
Konuşmacı gonna, you're, I'm gibi kısaltılmış biçimleri 142 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: threshold /ˈθɹɛʃ(h)oʊld/, underneath /ˌʌndɚˈniθ/
- “sh” ve “zh” sesleri: dashboard /ˈdæʃˌboɹd/, azure /ˈæʒɚ/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, limitation /lɪmɪˈteɪʃən/, extraction /ɪkˈstɹækʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/, analytics /ˌæn.əˈlɪt.ɪks/, hierarchy /ˈhaɪ.ə.ɹɑː(ɹ).ki/, analyzer /ˈæn.əˌlaɪ.zɚ/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; workspace, compute, query gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.