Shadowing Practice: Learn Databricks for FREE (Step-by-Step Guide) - Learn English Speaking with Video

hey friends so now i'm gonna show you exactly how to create a free databricks account

and after that i'm gonna guide you quickly into the interface of databricks

because if it is your first time it can feel a bit overwhelming

so now let's get started i guess it's starting now before

we start creating any account i would like you to understand something important

so that you understand what you are actually creating

so now the thing is databricks is not a tool

that you're gonna go and download

and install locally at your pc instead it is a platform

that you're gonna simply open in your browser and start working with it

so that means it doesn't matter whether you are a windows user

or a mac user you can work with databricks

and of course you don't need any super computer to use databricks

because we are not running anything locally at your pc now

of course you're gonna ask okay how we process actually the data we need things like cpu memory storage

so where we're gonna get those

if we are not using your local pc now the thing

is as well in data bricks itself it doesn't go

and build like data centers

and servers in order to process the data the idea is very simple it is a platform

that is built on top of other cloud platforms like azure aws

and google clouds so

that means it is not like stand alone platform it will be using other cloud service providers

so now because of all of

that Databricks is not like fixed price we pay my friends on the usage

so the more compute and storage

that we use from the cloud the more it gonna cost us

and now of course I might get you scared I'm gonna think okay

if I need to learn Databricks I have to go

and pay for all those compute and storage

and the cloud I have to configure the whole setup well

I have for you good news only recently Databricks now offers

a free edition where everything is prepared for you to learn databricks

so that means you're gonna get an access to databricks the same platform

that we use in real projects with 90 of all features

and this is more than enough actually to learn databricks

and currently behind the scenes it is connected to aws cloud platform

so everything is prepared with only few clicks you're gonna get the whole setup

and yes my friends for free databricks is covering all the compute

and storage that we're going to use in the aws

but of course there will be some limitations about creating high -end fast clusters

because they are very expensive and they don't allow you to do

that so this can be for personal use

and as well for learning purposes

but on the other hand we still have the professional setup

if you are working in a company then it really depends

on the cloud platform your company is using like here in europe mostly we are using the azure platform

so you can connect databricks to any of those cloud providers

and run everything there but of course you have to pay for it

so let's go and create that

so let's go to the home page of databricks .com

and then we have here on the right side try databricks let's go

and click on it okay so the first thing

that we have to do

if you don't have already an account with databricks you have to go

and sign up so let's go and do that and now after

that we clearly have two options either we can use it for personal use

or for work for personal use it's going to be for free

so if you are just learning databricks i'm gonna say go with this edition but

if you're gonna use it for work here actually you have two options either you can use the express edition

so everything is already prepared with the aws you don't have to go

and set up your own cloud so everything is there

and by the way they are currently giving free for 14 days

and some credits or you say you know what i have my own cloud

so i have already an azure account or aws

and i would like to go and connect it to databricks

and with this actually you can unlock the full professional version of databricks the enterprise edition

that we actually use in companies actually here we have three options the free one the express edition

and the full enterprise edition pick the one that suits you and go

and create the accounts all right after creating the accounts finally we're gonna land inside a databricks interface

and as you can see we are just using the web

browser without any installations now i know the interface might be overwhelming

because we have a lot of tools inside databricks we're gonna do it step by step

so don't worry about it now the first thing

that we might notice is to go to the right side over here

so we can see the workspaces now since you are using

a new free account you might see only one workspace

and that's okay

but once you start working in re -projects you might get an access to multiple databricks workspaces

and in order to switch between them you will be using here the workspace now the next thing

that we can check is actually the settings so

if you go to your icon

and then to the settings you're gonna get here in middle

a new interface where you're gonna get the usual stuff about

configuring the profile the appearance for example you can configure the date

and time formats you can go and manage the access to your workspace

so you can go and make groups and add users to it

and many other options about the security the compute for now

we will not change anything we're gonna leave it as it is the only thing

that i might say it is interesting for this phase is to go to the linked accounts

And here what we can do, we can go and connect one of our GitHub repositories the databricks

so that anything that you do inside databricks will be stored

and saved inside your github repo and as well you have the version control

so if you have one already you can go

and connect it to your databricks account

so as you can see we don't have any crazy things

inside the settings it is the classical usual things now we're

gonna go to something really interesting in the interface by the way this is completely new

if you go over here to this icon it is the genie code

100

so let's go and open it so basically the genie code is the ai assistant

101

that is inside databricks that's gonna help you work with data and code

102

so here we have a basic interface where you're gonna start writing your prompts like for example what is databricks

103

and what is very interesting you can decide between those two

104

so either an agent or a shot a shot it's just simple like question

105

and answer like we do in chat gpt

106

but an agent it is way more powerful it can go

107

and handle for you a task so

108

that means you can give it a goal and it's gonna go

109

and complete it step by step

110

and this could be anything like creating pipelines creating for you a script a notebook exploring the data building visualizations

111

and charts so this is very powerful things

112

and as well very new in data breaks

113

so you can write your prompts by asking questions

114

or giving it a task as you can see we got an answer for our question

115

and later we can start giving it command in order to do things for us in databricks

116

so this is where you can interact with the genie code

117

now let's go to the landing page of databricks now my

118

friends the most important part in this interface is actually the left navigation bar

119

because you're gonna see a lot of tools and things in databricks

120

and since we have many things databricks is gonna go

121

and group them into like sections

122

so the first set of tools they are the main section then after

123

that we have a lot of tools

124

that are dedicated only for the sql a whole section for sql

125

and another one for data engineering and the last one for ai

126

and machine learning so with that you're now you are getting the feeling

127

that i can use this platform actually for many things for

128

data engineering data analytics data science machine learning even ai engineering

129

now before we start talking about all those tools usually

130

if you are learning any new data platforms you have to

131

think about three things where is the data where is the compute

132

and what tools do i have in order to use my data

133

and the compute to do my projects

134

so let's start with the most important question where is the data in databricks

135

and you can find that in the main section in the catalog

136

so this is the most important component in the interface the catalog of databricks now

137

if you open it you're gonna have like two sections on

138

the left side it's gonna look like a traditional database

139

so you have like hierarchy and you can start browsing this catalog by just expanding things

140

so at the start we have the my organization

141

and then we have different workspaces and

142

if you start like expanding you're gonna find inside them schemas

143

and inside the schemas guess what we have tables

144

so we are just browsing like the structure the hierarchy of the catalog

145

and by the way the data that we see underneath samples it is something

146

that prepare from databricks but course you can go

147

and bring your data to the catalog now

148

if you want to see more details about each of those objects you can go

149

and click for example on the schema

150

and then on the right side you're gonna see more details about it like what are the tables inside it

151

and then here more details about the storage the permissions the policies

152

so you're gonna have the details on the right side same things

153

if you go for example to a table you're gonna see

154

more details like the history the lineage the insights quality

155

so we are seeing here all the details same thing

156

if i go for example to the workspace we're going to

157

see as well what is inside the workspaces with more details

158

and permissions so that's it this is the catalog where the data lives in

159

and of course we have to deep dive into this later

160

because this is the fundamentals that we have to learn how to organize

161

and structure our data inside Databricks

162

so this is about the data now the second most important

163

thing to check is where is the compute where are the resources the powers

164

that we're use in order to process our data

165

and here in databricks we have like let's say two tools

166

for it in the interface in the main section you can see here we have compute

167

and as well in the sql we have the sql warehouses now let's go inside the warehouses

168

so now whether you are using the free edition

169

or the express like me you're gonna find that

170

that are already created for us like a warehouse

171

so this is our compute you can see the status is offline

172

so it stops we see the name we see the size it is very small

173

and the type the serverless and at the end you can go

174

and start the warehouse now my friends the compute is usually the most expensive things

175

that we could use in the platform that's why databricks has a lock in you cannot go

176

and change the size of your warehouse or you cannot go

177

and create other computes

178

because then the costs of the free edition is going to be very high

179

but now for me since i'm using the express edition i can go

180

and create a new warehouse or change the size of it.

181

And of course, don't worry about it.

182

It is more than enough to practice in Databricks.

183

You are not processing terabytes of data you're gonna use some medium data sets in order to explore the platform

184

and learn the features inside it so that's why it is for you frozen

185

if you are using free edition but for me I can go

186

and create here like for example new warehouse now look at

187

this this is exactly why those data platforms are speeding up a lot of projects

188

so we are just deciding on very simple things like for example the name of the warehouse

189

and as well the cluster size that's it everything else is

190

going to be done behind the scenes like configuring the servers

191

the infrastructure the sizes of the workers the scaling their resource management

192

and monitoring everything going to be done behind the scenes

193

and we don't have to configure anything only the size of our compute

194

and for the free edition you cannot do that of course

195

and with this you can have some understanding how easy is things in such platforms

196

so i will not go

197

and create anything i'm going to stick with the default one

198

that's it as you can see the status is already online

199

so the server is ready to be used

200

and i can use it everywhere i can use it in

201

order to execute my notebooks my pipelines my sql queries the dashboards the ai

202

so all the tools

203

that i have over here on the list side it's going

204

to be powered by this sql warehouse of course in the enterprise edition you can go

205

and create multiple different types of clusters you can see here we are using the serverless

206

but there are other types for different purposes

207

but currently this is one of the best types in order to use both sql

208

and python by spark on all other tools

209

so as you can see things are simple and easy it's not that hard

210

but don't forget behind the scenes we still have this big data engine the spark with all data distribution

211

and parallel processing and stuff that is running as you start the warehouse okay

212

so with this you know where is the data where is the compute

213

and of course the question is

214

which tools i'm gonna use in order to do my projects

215

my job well of course this depends on what you plan to do

216

if you want to do data analytics data engineering science ai

217

engineering you're gonna have different tools to use now let's say i am data analyst

218

and i would like to analyze the data inside the data bricks then of course beside the catalog

219

and the warehouse the most important section for me gonna be the sql section

220

and everything else is not that important or let's say you can go

221

and ignore so what do we have here the first one is the sql editor

222

so here again you're gonna see our catalog in order to navigate through your data

223

and in the middle you can go and write sql query

224

so it looks like any traditional sql databases you will just go

225

and write here an sql query from table and then you're gonna go

226

and execute it and see the result in the output so

227

that you are like exploring and analyzing your data

228

that you have inside the catalog

229

and i can say this can be your main tool in

230

order to interact with the data to do data exploration

231

and data analyzes so here it's like a place where you're gonna go

232

and store all your sql queries as you can see i just created one

233

and we have now a query for

234

that it just gives you an overview of all the queries

235

that is used inside databricks for now it is empty

236

because we didn't do yet anything so i think but not

237

that important now we go to something really nice

238

that dashboards my friends as a data analyst you can actually go and build dashboards like i'm not

239

very strong in order to do data explorations

240

and let's say to create the first drafts for the analyzers

241

and here we have some examples I'm gonna go

242

and just click on one of those samples it might take some time in order to query the data

243

and present the result in the dashboard look at this we

244

have a dashboard where you have a title for it you can go

245

and filter the data you have big number some details dot plot

246

and analyzes over the time but again not pixel perfect like power bi

247

or tableau but still you can build the first visuals

248

and dashboards directly inside the same platform where you are working

249

with the data without having you to jump to a new tool

250

so this is just a dummy dashboard

251

that we get from databricks now let's go to more interesting

252

stuff for you we have something called genie now of course at the start everything gonna be empty

253

but what you actually do you can go

254

and create an ai bi genie by just selecting any of your data

255

and now look at this this already looks like shad gpt

256

so this is another ai assistance where you go

257

and define a subset of your data that you want to analyze

258

but this time without writing any code you will just go

259

and use the natural language in order to ask any question about your data

260

so what is the total number of rows inside my table

261

so look at this we have already answered the total number of rows in your table is 750

262

and if you go and expand this you can see it as a table

263

and as well you can see the code

264

that is generated behind the scenes and it is sql

265

so the genie gonna generate from our prompts and sql query

266

that gonna use our warehouse in order to interact with the data

267

and you can see the code is totally correct crazy stuff right

268

so now let's keep going the other things is not

269

that important so the alerts over here now we don't see anything yet

270

but what you can do we can go and create an sql query

271

and based on the thresholds it can go and give us notifications

272

and alerts so again based on sql query

273

and the last one you can see the query history

274

so here you can see a full log and protocol of all queries

275

that got executed in your databricks it's like we are monitoring let's say what is going on

276

so can see the query when it started the duration on

277

which data sets what is the resource that is used to compute

278

and who did that so again as i said some kind of monitoring it is not thing

279

and the last one we have the sql warehouse

280

so my friends for you as a data analyst we have three main tools in order to explore

281

and analyze our data so either we're going to go

282

and use the sql editor in order to write simple queries

283

or we can go and build a dashboard in order to analyze our data

284

or a third cool option is that you're gonna go

285

and use the ai bi genie where you're gonna go

286

and connect your data to the ai and start having simple chats

287

so that's it those are the tools

288

that you have inside data bricks for you as a data analyst in order to work with your data okay

289

so now let's go and switch and let's say

290

that you are a data engineer

291

and you would like to use data bricks in order to build your projects

292

so which tools you have to focus on again the catalog is the most important thing

293

because your main job is actually to build the catalog for the others

294

and the compute we talked about it

295

so either you can use the compute here

296

or the sql warehouse now the third component

297

that is really important for you to understand how to deal with it is the workspace

298

so now what is our main job as data engineers we go

299

and write codes so we need a place for that

300

and this is gonna happen inside the workspace so here we can go

301

and create notebooks write codes and organize the whole projects

302

so we can go and create folders and put inside it our python

303

or notebook codes and by the way all other interactions

304

that we are doing like for example writing an SQL query

305

building a dashboard creating a new genie you're gonna find all those stuff inside the workspace again

306

so that you can go and organize it

307

and reuse it inside your projects

308

so look at this we have the genie space inside the workspace

309

that we just created same thing goes for the dashboards and

310

if i do any queries it's going to be landing inside

311

this workspace now of course at this level at the home anything

312

that i'm creating in notebook code it's going to be stored inside my account in the databricks

313

but of course usually we don't do that we usually organize

314

and store our code inside github repository and for

315

that we have a section over here called workspace and then repos

316

so if you go inside it now you can go

317

and connect your github repository inside databricks

318

and inside it you will be creating your code and project

319

and with that you have version control on your code

320

and your work will not stay only inside your account in data breaks

321

and there are many other like not important things like for example here you can see all the notebooks

322

that are shared with you and so on

323

so again workspace is the place where you organize your projects

324

and to be honest as a data engineer i spend most

325

of my time just working in this component inside the workspace now the second thing

326

that we usually do is we go and create jobs

327

and orchestrate our work so in this place the automation of my work can happen

328

so i can go and create a pipeline

329

and then schedule it to run automatically instead of me doing everything manually

330

and here in the interface like we have different types on

331

how to do this the most like important one i'm gonna say the jobs this is the way

332

that i use in order to orchestrate the notebooks and codes

333

that i'm creating so now like any data pipelining

334

and orchestration i'm gonna go and connect all the notebooks

335

that have created at the workspace and start connecting them one after one

336

and at the ends what we're gonna do we're gonna go

337

and add a trigger for it to run automatically like for

338

example here we have different options the scheduled one i'm gonna say okay run it once every day

339

so i'm gonna say this is very simple for data engineering

340

to create an automated pipeline now for you it can be empty

341

but previously i have created something in order like just to

342

test now we can see like more details about my pipeline

343

and in the task you can see here i have created

344

like like three notebooks that's gonna build for me a data leak house

345

so I'm gonna say this is the easiest thing

346

that we do in Databricks

347

and data engineers in order to create a visual data pipeline

348

now what else we do as data engineers after we write the code

349

and build the pipelines we have to monitor every day whether everything is correct

350

and for this in Databricks we have a dedicated section in

351

that data engineering so here we have something called runs

352

and this kind of gives you a nice

353

and easy overview of the health of your jobs like for example here i tried to run it twice

354

and as you can see the status is failed of course you can see the start time

355

which notebook the type who run this and so on

356

so again here is the place where we can monitor our jobs

357

and do the operations and if something is wrong

358

so it's not green you have to go

359

and debug where exactly the issue happens

360

so actually that's it those are the main components

361

that are specially used by data engineers and data bricks

362

so the workspace where we go and write our logic our code

363

and organize the whole project and then we go

364

and connect everything to automate our code by building jobs

365

and pipelines and at the end so we go

366

and do the operations by monitoring the health of the jobs okay

367

so now let's move to the AI

368

and machine learning part this is mainly used by data scientists ML engineers

369

and AI engineers each role is slightly different

370

but they all work on building models

371

and intelligent system on top of our data

372

so let's go through the components that are relevant for them

373

so again they will be using workspaces because they're gonna go

374

and write codes so they have to write notebooks in order to explore data train models

375

and write code in python and of course they are interacting with data

376

so the catalog is again important the compute in order to execute things

377

but at the end over here we have a whole dedicated sections for ai

378

and machine learning

379

so what do you have here the first one is the

380

playground this is where you experiment with the ai models especially the language models

381

so you can test prompts you can try a few ideas quickly see how the model responds

382

so it is mainly used for exploration

383

and testing now the next one is actually new one the agents

384

so again agent it is an advanced ai system

385

that actually gonna go and do something you give it a task

386

and it's gonna go

387

and complete it step by tip it is more than just conversation with the ai

388

and here we can see that databricks is already like suggesting for us ideas

389

and some common use cases like the document parsing where you can go

390

and give the ai your documents like pdfs and images

391

and you can use it in order to start extracting the data from them another one we have information extraction

392

so again the same thing you're gonna give it your documents

393

and extract some key features and classifications another one

394

which is very famous one where you can go

395

and build a shot bot based on your documents

396

so again you can upload your things

397

and start having conversation with the ai about the contents of your files

398

and then the last one we already talked about it the ai bi genie we're gonna go

399

and convert your text to sql and many other things

400

so i'm gonna say

401

if you want to work with ai in databricks this component is gonna be the most interesting one

402

so what else do we have we have the ai gateway way.

403

It's all about managing the AI models that are used in your projects.

404

It's like the other monitoring tools that we saw inside Databricks but this time for the AI models.

405

Now let's go to the next one.

406

The experiments.

407

This is mainly used by data scientists in order to track the training of the models.

408

Go and compare the different trends

409

and parameters of the models in order to see at the end which model performs the best.

410

Of course we don't see anything yet because we didn't train any model

411

so we cannot see the content now let's go to the features

412

so this is something classical for machine learning engineers where you're gonna go

413

and store the features that you extracted in order to reuse it later

414

so that you don't have to go and rebuild them every time

415

and the next one we have the models

416

so again it's all about the ai models

417

so here you're gonna see where the models are stored

418

and versions you can go and manage the different versions

419

that are used inside your projects track the changes and reuse models when needed

420

and finally we have the serving this is where you deploy your model once the model is ready you go

421

and expose it as an api so the applications can go

422

and use it in real time

423

so as you can see the full section is all about ai

424

and machine learning it is the full life cycle from experimenting to building managing

425

and finally deploying the ai and machine learning solutions and

426

if you are not data scientist

427

or ai engineer i can tell you the most interesting one gonna be the agents all right my friends

428

so that was a quick tour into the data breaks interface as you can see there are many things

429

that we can do inside this platform

430

and i totally understand at the beginning it might looks a lot

431

and you might get overwhelmed with all those things

432

but now you have at least a clear idea on what each component is doing

433

and who uses what and now my friends there is something

434

that i really wanted you to understand that we don't just go

435

and learn everything that we see inside data bricks we focus on the parts

436

and the components that actually match our role

437

so now what i want you to do next based on your goal

438

and your role whether data analyst engineering scientist ai engineering go

439

and explore the platform and the interface on your own

440

so i want you to take few minutes by clicking around

441

and trying all those components

442

that i just showed you in order to get comfortable with the interface

443

and don't worry about it you will not break anything at the end

444

if you are using the free edition you are not paying for anything

445

so this is what you're gonna do next but before

446

that there is something important if you enjoy this type of content

447

and you would like to support my work then subscribe like

448

and comment this really helps in order to reach nice people like you thank you

449

so much for watching and i will see you in the next video bye

📺 Same Channel

How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)

Business EnglishUniversity StudentsWorking Adults

✨ Recommend Video

339 sentences

Learn Data Modeling in 8 minutes: Dimensional Data Modeling, Data Vault, and One Big Table

Business EnglishUniversity StudentsWorking Adults

14 sentences

CS50x 2026 - Introduction

Business EnglishUniversity StudentsWorking Adults

44 sentences

TIA Portal 101: The Beginner's Guide to Starting a New Project

Business EnglishUniversity StudentsWorking Adults

29 sentences

Amazon Athena explained in 120 seconds💡

Business EnglishUniversity StudentsWorking Adults

364 sentences

Claude Cowork Tutorial: Build Monthly Reports in Minutes

Business EnglishUniversity StudentsWorking Adults

125 sentences

Introduction to statistical prediction models

Business EnglishUniversity StudentsWorking Adults

42 sentences

1.2 How Google Analytics collects and processes data - Analytics Academy on Skillshop

Business EnglishUniversity StudentsWorking Adults

537 sentences

Understand Microsoft Entra ID | AZ-104 | Episode 2

Business EnglishUniversity StudentsWorking Adults

172 sentences

How to Create Your Own HABIT TRACKER | Step-by-Step Tutorial for Beginners | Google Sheets

Business EnglishUniversity StudentsWorking Adults

Vocabulary and speaking notes for this lesson

This C1 speaking lesson is built on the video “Learn Databricks for FREE”. The speaker keeps coming back to these words: databricks, inside, sql. This video has 449 sentences and 5213 words to shadow. The speech runs for 24:48. The speaker talks fast, about 210 words per minute, so expect linked and reduced sounds. 87% of the words are among the 3,000 most common in English; the rest is worth studying before you start.

Key vocabulary in this video

The 15 most advanced words in the video, with pronunciation and meaning:

Word	Pronunciation	Meaning
workspace noun	/ˈwɝkˌspeɪs/	An area allocated for someone to work in, especially in an office.
compute verb	/kəmˈpjuːt/	To reckon, calculate.
query noun	/ˈkwɪɹ.i/	A question, an inquiry (US), an enquiry (UK).
warehouse noun	/ˈwɛə(ɹ)haʊs/	A facility for storing large amounts of merchandise or products.
catalogue noun	/ˈkæt.əˌlɔɡ/	A systematic list of books, names, pictures, etc.
notebook noun	/ˈnoʊtˌbʊk/	A book (physical or digital) in which notes or memoranda are written.
dashboard noun	/ˈdæʃˌboɹd/	A panel beneath the windshield of a vehicle or aircraft containing indicator gauges, compartments, and sometimes controls and indicator lights.
genie noun	/ˈd͡ʒiː.ni/	A jinn, a being descended from the jann, normally invisible to the human eye, but who may also appear in animal or human form.
pipeline noun	/ˈpaɪpˌlaɪn/	A conduit made of pipes used to convey water, gas or petroleum, etc.
analyze verb	/ˈæn.əˌlaɪz/	To subject to analysis.
organize verb	/ˈɔɹɡənaɪz/	To arrange in working order.
configure verb	/kənˈfɪɡə(ɹ)/	To set up or arrange something in such a way that it is ready for operation for a particular purpose, or to someone's particular liking
analyst noun	/ˈænəlɪst/	Someone who analyzes.
prompt verb	/pɹɑmpt/	To lead (someone) toward what they should say or do.
execute verb	/ˈɛksɪˌkjuːt/	To kill, especially as punishment for a capital crime.

Phrasal verbs you will hear

Word	Pronunciation	Meaning
go over verb		To look at carefully; to scrutinize; to analyze.
go through verb		To travel from one end of something to the other.
have over verb		To host (a visiting guest) at one's house.
set up verb	/ˌsɛt ˈʌp/	To make ready for use.
sign up verb		To add a name to the list of people who are participating in something.
speed up verb	/spiːdˈʌp/	To accelerate; to increase speed.

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Passive voice be + past participle — the focus is on what happens, not who does it	is built · is prepared · is connected
Present perfect have/has + past participle — a past action that still matters now	have already answered · have created

Pronunciation to watch

The speaker uses 142 contractions and reduced forms, such as gonna, you're, I'm. Say them the short way, as you hear them.

The “th” sounds: threshold /ˈθɹɛʃ(h)oʊld/, underneath /ˌʌndɚˈniθ/
The “sh” and “zh” sounds: dashboard /ˈdæʃˌboɹd/, azure /ˈæʒɚ/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, limitation /lɪmɪˈteɪʃən/, extraction /ɪkˈstɹækʃən/
Long words — get the stress right: 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/

How to practise with this video

Listen to the whole video once without speaking and note the words you do not know.
Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
Record yourself and compare with the original, paying attention to words like workspace, compute, query.

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

Shadowing Practice: Learn Databricks for FREE (Step-by-Step Guide) - Learn English Speaking with Video

How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)

Easy Migration from Postgres to Databricks Lakebase

FIN 1 Welcome to Finance!

JUST $1 For Billions of Tokens?! (DeepSeek v4 Pro)

Learn Data Modeling in 8 minutes: Dimensional Data Modeling, Data Vault, and One Big Table

CS50x 2026 - Introduction

TIA Portal 101: The Beginner's Guide to Starting a New Project

Amazon Athena explained in 120 seconds💡

Claude Cowork Tutorial: Build Monthly Reports in Minutes

Introduction to statistical prediction models

1.2 How Google Analytics collects and processes data - Analytics Academy on Skillshop

Understand Microsoft Entra ID | AZ-104 | Episode 2

How to Create Your Own HABIT TRACKER | Step-by-Step Tutorial for Beginners | Google Sheets

Vocabulary and speaking notes for this lesson

Key vocabulary in this video

Phrasal verbs you will hear

Grammar in this video

Pronunciation to watch

How to practise with this video

What is the Shadowing Technique?