Shadowing Practice: How to Create Gold, Silver, and Bronze Tables in Postgres for a Medallion Data Architecture! - Learn English Speaking with Video

Elige un modo de práctica

Cada modo abre una sala de práctica a pantalla completa.

202 oraciones

Si las oraciones son demasiado cortas o largas, haz clic en Editar para ajustarlas.

Hey guys, DataGuy here, and today I have a video for you that has been requested for a long time, which is how you can set up bronze,

silver, and gold medallion tables within Postgres.

And don't worry if you're not using Postgres, the same patterns will apply for really any kind of SQL database,

you're just going to need to alter some of the syntax for your specific database.

And what I'm using here to run it is just Postgres app for MacBooks, you can download it online super easy

and then i'm going to be just connecting

and running my scripts within dbeaver just local ui for actually running actions within a database and

so what i'm going to actually also want to be using

as the data set for this is just the classic taxi

vendor data set you can download it really anywhere online just

search for taxi vendor data set it's the most classic nyc

taxi kind of pattern for running these kind of operations and examples.

And so without further ado, let's get into it and start creating our bronze schema.

And so the first thing you're going to need to do is obviously just create your schema.

If it doesn't exist, create bronze, and then drop any existing bronze table if it already exists and it doesn't already exist.

So you're going to see that error message there at start.

And then assuming that that hasn't been created right

so we just want to create a fresh table of any new data

so any pre -existing data that was already around don't want it anymore

so now we're going to create this blank table it doesn't exist

and then on the we're going to also create an index on the load timestamp for more efficient querying

so instead of just using timestamp to kind of order our

data we want to create an index make it easier and more organized to actually just query our raw data downstream.

So we're not stuck always just having to use that timestamp or use that raw timestamp as our identifying index.

And this also going to help queries run faster as well.

Then what we'll do is insert some sample data into our bronze layer.

So this would likely just be something you're going to be doing, you know, either as part of your data ingestion.

I'm only doing it here because I don't have any, you know, I just don't want to bother going to the process of uploading data from an external database, right?

So I'm just going to kind of create an example.

And this is again, just data that copied over from that taxi trip data set.

So you can download and use and basically just copy that in yourself as well.

And then once we're done with that, we're also going to, you'll notice here, I added some data that does have data quality issues within the silver layer,

we're actually going to clean this data out.

So I'm going to show you how you clean your data as part of this process.

So we have one entry that has a negative fare and then one entry that actually has some invalid coordinates.

I just took an entry and adjusted to break it.

So then once we have run that insert command, we've got our data loaded up.

The next step is going to just be running a quick verify bronze data layer load.

So, you know, just making sure every time this script gets run

that our data is of the expected size and

so here we just have a simple select statement hey count all as total records from bronze taxi trips raw

and then just sample the first five rows to make sure

the data isn't corrupted it's of the expected you know type

and look and feel that that we're anticipating here

so this is our first step so just creating that bronze table

and so you'll see we'll run this script

and now we just have our first kind of five rows of this taxi data within our database, right?

So here we go to tables.

Now we will see our taxi table.

So here have taxi trips raw.

And then next, what we'll do is start building our silver table.

So here we'll add a new silver, a new script.

So new SQL script here, and this will be our silver SQL script.

And so next we're going to do is within silver layer, typically, you want take hey we've got our raw data in

the ingestion layer now we want to clean it do some validation

standardization so that it is you know it's not fully transformed for analytics

but we've gotten you know all the bad data we have in a really easily transformable format

and so first thing we're going to do here is same thing just creating the schemas

if they don't already exist so creating silver schema dropping an existing table

so taxi trips cleaned

and then what we're going to do here is create a new table with all the different fields

that we want to track in our silver stage

so here we're just creating a surrogate key

so serial number trip trip ID and for easier joins

and this is how you know imagine is an ID you'd be using to join with other pieces of data

and we also are creating some metadata fields

so timestamp for bronze load silver loads we can see the Delta between bronze

and silver the actual source file the raw source file

so you can trip for traceability then just create a nice clean shrimp trip information with the proper data types, location data with precision for GPS coordinates,

trip details, also starting to calculate things like financial data, and then also data quality flags.

So this is how we're going to filter bad data downstream to make sure, you know, you don't want to delete bad data, but you want to kind of quarantine it.

And then we also have some fields

that we're going to calculate like the trip duration in minutes and the trip speed in mile per hour as well.

So once we've created the table, the next thing we're going to do is also create some index for common query patterns.

So these will make it easier to find the data for, you know, these particular values.

So, you know, for things like, hey, pick up date time, the amount of silver taxi trips that are, you know, running at a certain pickup or drop off time,

the vendor IDs, you can track trips by vendor, payment types, you can track by payment type, valid trips, so you can filter out any invalid trips or any invalid data.

And let me just create a few of two rows here for spacing.

And then after we've done, we're done creating the indexes, then we're just going to run a very long insert command

to insert all of our different data into this new table we just created.

So here we have insert into, go up here into silver taxi trips, all those fields we just created

and then selecting from our bronze table source file casting the

vendor id changing the integer types to the proper integer types changing cases to make sure that they're

capped at hey you can't have passengers less than one

because you don't have a ride if you don't have a passenger

and you can't have more than six

because we don't have let's say larger vans in our fleets we know

that legally you're never allowed to have more than six

and then these are just again casting different variables to the correct type

and then same thing here casting valid coordinates making sure they're within the new york city approximate bounds

so testing the coordinates for whether they're actually taking place in new york city also running fair validation

so making sure the fair is actually greater than zero for

each each entry duration has to be between one hour

and three minutes you can't have a ride less than a minute

and we don't allow rides more than three hours obviously you can change

that then we're also calculating the trim duration in minutes

so just basically here subtracting the drop -off time minus the pickup time

and just getting a value in numbers or in you know minutes

and then finally calculating the average speed distance over time in mile per hour

100

so we have some clean data rather than just raw you know entries

101

and makes it easier for obviously querying this all downstream

102

because you don't care about pickup

103

and drop off time mostly most likely you care about how long were these trips taking

104

and then also just doing some basic not null checks to make sure

105

that hey drop off is always after pickup

106

and also there has to be a pickup

107

and a drop off you obviously can't have one in the other

108

so once we've done that we've inserted all our data

109

and done some transformations in the process we're then also going to perform some data validation

110

or create a data quality table to create summaries from some data quality checks we'll run on this table.

111

So here we're creating, we're saying, hey, making sure every field has valid coordinates, valid fares, valid durations, has fully valid records.

112

So it has all those things as valid.

113

And then also the quality percentage.

114

So saying, hey, total amount of times when we don't actually have quality rides versus quality rides, so you can get an overall view of how well -maintained and how well -recorded your data is.

115

And then just saving in an external table for easier analysis and validation.

116

It also makes it easier if you're collecting data over time, right?

117

You can have each entry in this table be the sum data, sum validation of each time you ingested that data.

118

Then what we'll do is just show the data quality report.

119

So just add a select statement here.

120

And then also show another sample of our clean data.

121

Just showing that it is, in fact, cleaned.

122

And boom, that is our silver stage, all nice and complete

123

so now it's time to move on to gold and

124

so here again we can see some of the output here of our top fields within our now silver table

125

so has valid coordinates valid fair making sure this is all valid data

126

so cool now creating our next

127

and final sql script we are then going to create our gold table

128

so here let's go and this is the table

129

that is going to be you know really tightly design business -ready aggregations analytics.

130

So we're really taking high -level metrics, analytics, and doing a lot of calculations here that will be used in downstream business processes and reporting.

131

So first what we'll do is just do our classic, hey, create a schema if it doesn't exist.

132

So creating that gold table schema.

133

Then we're going to drop it if it doesn't exist and create a new table.

134

um with a select statement so here what we'll do is drop table

135

if it doesn't exist the daily trip summary table

136

and then create gold daily trip summary as selecting pickup date

137

time from as trip date the total number of trips by

138

counting all the number of trips um count distinct vendor ids as active vendors

139

so only counting distinct vendors

140

so duplicating vendors you know exactly how many you know drivers you have um also passenger metrics here

141

so the passenger count for total passengers average passengers per trip

142

averaging the total distance trip duration trip distance speed also creating

143

a lot of these average financial metrics like average total fare

144

amount total tips total revenue average trip cost the max trip cost tip analysis for tip percentage

145

and average tip

146

and then also data quality metrics analyzing you know at a

147

high level hey are a lot of these trips uh valid

148

and you're seeing pulling all this data from that silver table

149

and grouping it by date time ordered by trip date which is again that index we created at the start

150

now then what we're going to do is create another index for date based queries

151

so we're here we're going to create index daily date based on daily trip summary

152

and then we're going to do some analysis on vendor performance metrics

153

so this is very typical in a gold table you know

154

you're going to want to perform analysis some of these queries

155

so here what we're doing is you know basically same thing we did up there dropping vendor performance

156

that already doesn't know if it already exists and then here creating a new table where we're actually assigning our vendors

157

names so here we have you know create a mobile verifone else's other

158

and we're also you know making the data a lot cleaner

159

to look at instead of just you know vendor ids calculating

160

some of these things like volume metrics the amount of total trips stays active per vendor average trips per day per vendor.

161

And, you know, basically all that same kind of average, all those averages we counted at the start, but now broken down per vendor.

162

And that's really a lot of what gold tables are doing.

163

It's taking the same information, but slicing it in different ways that analysts might use, right?

164

So understanding, hey, what, you know, vendors using more cash versus credit card or what vendors are getting the most tips, understanding all of these processes.

165

And then another one, it could be something like payment type analysis.

166

So let's say you want to understand, you know, what type of payments people are using and how that has input, you know, affects the number of trips or the tips you're getting, right?

167

So here we have credit card, cash, no charge or dispute.

168

And here what we can do is start tracking, hey, people that are paying cash, what's their average trip distance?

169

What's their total revenue?

170

And tip analysis, I'd say is most relevant here for credit versus cash is saying, hey, how much do I normally get tipped when people pay with cash versus credit?

171

And then maybe, you know, you want to say, hey, I want to actually only, you know, accept cash or credit if I expect,

172

if one is giving vastly larger tips than the other, right?

173

And so I could really go on for days with all the different types of analysis you could do here, but I'll cap it off as,

174

you know, another one for, hey, trip distance duration.

175

So here, you know, let's say I want to, or, you know, analyze, hey, what is the hourly demand patterns right so here you know

176

if i want to look at what is you know i

177

want to say hey more people need to be on the road at a certain time understanding hour by hour

178

when are people most likely to you know create to schedule rides to book rides

179

and you can group them in these different periods

180

and then analyze you know schedule your drivers accordingly

181

so you don't have extra drivers on the road just sitting idle

182

when you could be having them sit home and and then also So, you know, something like a trip distance duration is also a great one to have.

183

So just, you know, really simple, hey, understanding different categories of trip distances.

184

So if you want to analyze costs and tips on that as well, you can.

185

And then another very common thing to do after you've, you know, created your tables is also create views, right?

186

And so these are basically, you know, windows within SQL that you can use to say, hey, this is an executive dashboard that's going to sum up today's metrics,

187

with the manner of trips today, revenue today, trips yesterday, revenue yesterday to compare, week to date, month to date.

188

And this is, you know, really a executive level view ready to be sent out to

189

an external party where it just has only the relevant information that's gone through these series of cleaning, validation, calculation to arrive at these actual data points.

190

And that's, again, the purpose of a gold table.

191

And then similarly, you create something like a data quality monitor, right?

192

Where here, we're creating a monitor where we're selecting,

193

you know hey bronze layer validation silver layer validation gold layer validation having all their data quality percentages added there

194

so you can actually see hey these are you know this is how data is evolving

195

and being cleaned and changing as it flows through our systems and

196

that is really everything i have for you today you know

197

i wanted to make this video i thought it was something

198

that been getting requested enough that i finally had to get around to it

199

so it took some time so i hope you enjoyed this video.

200

I hope you got some value out of it.

201

I hope you have a great rest of your day.

202

Data Guy out.

Descargar app

Todo lo que necesitas para hablar con fluidez

Pronunciación con IAEvalúa cada oración

Práctica de IPADomina cada sonido

VocabularioConstruye tu banco de palabras

Juego de vocabularioAprende mientras juegas

Escanea el QR

Download on theApp Store Get it onGoogle Play

Mismo canal

Data Ingestion Best Practices! How to Ingest Data Efficiently at Enterprise Scale!

Daily ConversationWorking Adults

What is Master Data Management? MDM Explained for Beginners

Daily ConversationWorking Adults

FOR YOU

Recomendar Video

120 oraciones

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

Business EnglishUniversity StudentsWorking Adults

309 oraciones

How to Create a Women’s Bodice Block from Body Measurements [Pattern Making Tutorial]

Business EnglishUniversity StudentsWorking Adults

193 oraciones

The Real Reason We Left the Gold Standard

Business EnglishDaily ConversationUniversity Students

131 oraciones

Understanding BGA Pads: Solder Mask Defined vs Non-Defined

Business EnglishUniversity StudentsWorking Adults

172 oraciones

The AI Tool You Actually Need for Ancient Greek (NotebookLM)

Business EnglishUniversity StudentsWorking Adults

180 oraciones

Your API Can’t Handle Real-World Integrations

Business EnglishUniversity StudentsWorking Adults

130 oraciones

Password Storage Tier List: encryption, hashing, salting, bcrypt, and beyond

Business EnglishUniversity StudentsWorking Adults

243 oraciones

Proximity Search & Geospatial Indexes Explained

Business EnglishUniversity StudentsWorking Adults

159 oraciones

So kannst du ein Kassenbuch in Excel mit Tabelle und Formeln erstellen und führen!

Business EnglishWorking Adults

Vocabulario y notas de pronunciación para esta lección

Este vídeo tiene 202 frases y 2901 palabras para practicar shadowing. La parte hablada dura 15:04. El hablante habla rápido, unas 193 palabras por minuto, así que espera sonidos enlazados y reducidos. El 85 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.

Vocabulario clave de este vídeo

15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:

Palabra	Pronunciación	Significado
vendor sustantivo	/ˈvɛn.dɚ/	vendedor, vendedora
bronze sustantivo	/bɹɑnz/	bronce
valid adjetivo	/ˈvælɪd/	válido, valedero
taxi sustantivo	/ˈtæksi/	taxi, taxímetro
validation sustantivo	/ˌvæl.əˈdeɪ.ʃən/	validación, convalidación
layer sustantivo	/ˈleɪ̯ɚ/	capa, tongada
script sustantivo	/skɹɪpt/	escrito, escripto
duration sustantivo	/dʒʊˈɹeɪ.ʃən/	duración
metric adjetivo	/ˈmɛt.ɹɪk/	métrico
query sustantivo	/ˈkwɪɹ.i/	consulta, pregunta
select verbo	/sɪˈlɛkt/	seleccionar, cribar
passenger sustantivo	/ˈpæs.ɪn.d͡ʒɚ/	pasajero, pasajera
insert verbo	/ɪnˈsɝt/	insertar, enserir
coordinate verbo	/koʊˈɔɹ.dəˌneɪt/	coordinar
database sustantivo	/ˈdeɪtəˌbeɪs/	base de datos, banco de datos

Phrasal verbs que vas a escuchar

Palabra	Significado
drop off verbo	dormirse, caer
break down verbo	descomponerse, averiarse
go through verbo	atravesar
go up verbo	subir
pick up verbo	coger, asir
sum up verbo	sumar

Gramática en este vídeo

Las estructuras que más usa el hablante, con las palabras exactas del vídeo:

Estructura	En el vídeo
Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora	has been requested · hasn't been created · have run
Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace	has been requested · been created · isn't corrupted
Oraciones de relativo who / which + oración — información extra sobre una persona o cosa	time, which is · date which is

Pronunciación a tener en cuenta

El hablante usa 97 contracciones y formas reducidas, como we're, don't, you're. Dilas en su forma corta, tal como las oyes.

Los sonidos de “sh” y “zh”: validation /ˌvæl.əˈdeɪ.ʃən/, duration /dʒʊˈɹeɪ.ʃən/, calculation /ˌkælkjuˈleɪʃn̩/, efficient /ɪˈfɪʃənt/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
Palabras largas — cuida el acento: validation /ˌvæl.əˈdeɪ.ʃən/, coordinate /koʊˈɔɹ.dəˌneɪt/, analytics /ˌæn.əˈlɪt.ɪks/, similarly /ˈsɪm.ə.lə.li/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/

Cómo practicar con este vídeo

Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
Grábate y compara con el original, prestando atención a palabras como vendor, bronze, valid.

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.

Técnica de shadowing: lee la guía completa paso a paso →

Shadowing Practice: How to Create Gold, Silver, and Bronze Tables in Postgres for a Medallion Data Architecture! - Learn English Speaking with Video

Elige un modo de práctica

Descargar app

Data Ingestion Best Practices! How to Ingest Data Efficiently at Enterprise Scale!

What is Master Data Management? MDM Explained for Beginners

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

Claude Code: The Advanced Guide (99% of Devs Skip These Features)

Why Shopify Moved Inventory Reservations from Redis to MySQL

Easy Migration from Postgres to Databricks Lakebase

How to Create a Women’s Bodice Block from Body Measurements [Pattern Making Tutorial]

The Real Reason We Left the Gold Standard

Understanding BGA Pads: Solder Mask Defined vs Non-Defined

The AI Tool You Actually Need for Ancient Greek (NotebookLM)

Your API Can’t Handle Real-World Integrations

Password Storage Tier List: encryption, hashing, salting, bcrypt, and beyond

Proximity Search & Geospatial Indexes Explained

So kannst du ein Kassenbuch in Excel mit Tabelle und Formeln erstellen und führen!

Vocabulario y notas de pronunciación para esta lección

Vocabulario clave de este vídeo

Phrasal verbs que vas a escuchar

Gramática en este vídeo

Pronunciación a tener en cuenta

Cómo practicar con este vídeo

¿Qué es la Técnica de Shadowing?