Shadowing Practice: Interview with Big Data engineer in 2026. - Learn English Speaking with Video
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168 oraciones
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1
We built a real -time pipeline
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So leadership can ignore insights at millisecond speeds
3
What was the question?
4
Okay, sorry.
5
I had quick
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Problem in database, but no worries all 200 tetabytes are recovered
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Big data most companies don't actually have big data
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They have medium data.
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Big data is anything that crushes Excel.
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We actually have two big data for fault tolerance.
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Two wrongs don't make a right, except in a distributed database.
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Big data is different math.
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1 plus 1, 1 .9.
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95 % confidence interval.
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Our 15 million raw data set has 800 columns.
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The rows aren't wide, they are panoramic.
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If the joint fits in memory, it belongs in Postgres.
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Our architecture preserves every invariant except the budget.
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Our cloud bill was $340 ,000.
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We don't know if the processing even ran.
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That was just observability.
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We support real -time processing, of course.
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Unless the event arrives early, late, duplicated, malformed, reordered, eventual consistency was too hard.
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So we went with immediately inaccurate.
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70 % of records are accurate.
26
It's higher than the weather forecast.
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The numbers are not wrong.
28
They are early.
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Wait, two, three, maybe four days, they will be late.
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It's almost interactive analytics.
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Final numbers mean numbers we have stopped refreshing.
32
The slide says we handle billions of events.
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It does not say we handle them well.
34
It is only truly idempotent if all availability zones go down equally during the outage.
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Management.
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Management asked us to build a data lake, but it wasn't scalable enough.
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So McKinsey asked us to build a data swamp.
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The project was declared successful before all the metrics arrived.
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We call that watermarking.
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Dashboard is green because the job that checks if the dashboard is wrong is red.
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Ah, what happened here?
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Let me quickly roll back 30 billion events.
43
A production workaround becomes durable the moment the engineer who wrote it leaves.
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Yes.
45
We removed an unused column and it broke Portugal.
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The bug has existed for eight years.
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At this point it's an interface.
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Quick Slack message.
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20 terabytes lost again.
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This is our architecture.
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Very simple.
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Kafka, Airflow, Databricks, Flink, Spark, SQL, Iceberg, S3, Presto, DBT.
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What does DBT stands for dialect behavioral therapy.
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We need that to avoid going crazy.
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We have the most modern stack in the industry.
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Only 10 % is COBOL.
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HDFS is not dead.
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It's waiting for someone with root access.
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We migrated from Kroon to Airflow for reliability.
60
Then we wrote a Kroon job to check if Airflow silently died.
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We would switch from Databricks to Excel if it wasn't for date parsing.
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We are a data driven company of course.
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Every week somebody reads the invoices out of PDFs and types the data into Excel.
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Running on Kubernetes.
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We moved from Hive tables to Iceberg tables because we wanted the metadata to fail with better governance.
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Last week, we had some ransomware attack.
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We lost the backups, yes.
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Whenever we lose a backup, we reconstruct what the customer did from the logs.
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No, that's one of our industry's best practices.
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Our customers didn't even know.
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To be honest, most of our customers don't even use our product anyway.
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The board deck says petabyte scale, yeah.
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Scale is a direction, not a quantity.
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I checked with a legal.
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Management says one petabyte.
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I checked, yeah.
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We only process 18 terabytes, but we round up.
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Our P99 is four minutes.
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But the P50 is excellent.
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And most people are median.
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Streaming gets simple once you understand watermarks.
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Event time, processing time, state, back pressure.
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Batch and streaming produce different numbers.
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We average them.
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Our recommendation pipeline had three hours of Kafka lag.
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So technically it was a real time communication for your past self.
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Kafka rebalanced so often I started listing it under team building activities.
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We decoupled storage and compute.
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Now the storage team and the compute team have separate outages.
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Ah, again, we lost the logs from Tuesday.
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The data team, the data team has excellent isolation.
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We haven't spoken to product in seven months.
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All problems become your problems at one terabyte an hour.
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Exactly once processing is easy.
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You just need two topics, transactions, offsets, recovery state and a relaxed interpretation of easy.
96
GDPR in a data warehouse.
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This is a funny joke.
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We anonymize the data by removing the column name.
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We persist it in places nobody knows how to query.
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The only thing in this company with strong durability is a bad architectural decision.
101
Salary, job, security, holidays, pick two, a CIP compliant.
102
Can we have this by Friday?
103
In distributed systems, what does Friday mean?
104
Before the meeting is only a partial order.
105
The project isn't late.
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The clocks just disagree.
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It's a distributed organization.
108
Every quarter we solve capacity problems by increasing input.
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We maintain system availability by shedding JIRA tickets.
110
Last year, they fired 80 % of our team as part of our operational transformation.
111
They should have used CRDTs instead.
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A backfill is when you fix one historical bug by introducing a brand new historical bug.
113
The Snowflake warehouse runs a two -second query every minute.
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A very sophisticated way of never letting it turn off.
115
Artificial intelligence?
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No. We still rely on humans, a single point of failure.
117
Yes, David.
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The game.
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You pull something from the bottom, everything falls apart.
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It's educational game for industry.
121
Back 2016, learning Hadoop in the simple 760 steps.
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That's right.
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Hadoop certified.
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Cloudera certified.
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Cloudera no longer offers this cert.
126
We migrated to Abachi iceberg this year.
127
Quick 43 months migration.
128
We still use protobuf.
129
Yes, we are still confused, but at least confused strongly type.
130
Some say data is the new oil.
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We cannot stop producing it and we will pay to clean it up for years.
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From what I see, data is the new asbestos.
133
The AI team spent four million and it called investment.
134
I spent three hundred forty thousand.
135
It is called a review.
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I'm in FinOps review every Thursday.
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The AI team has never ever been to FinOps.
138
They have a different word for money.
139
My cluster is a cost center.
140
Their cluster is a strategy.
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Same rack in AWS where I make, there are my glasses.
142
Duh!
143
Ah, only disk usage 80%.
144
This is sweet spot.
145
A soft outage is when the checkpoint directory grows faster than S3 versioning can build.
146
Back in the days, big data means you bring the intelligence to the data.
147
That's why I go straight to big data center.
148
We have two big data centers for fault tolerance.
149
I wanted to become simple app developer.
150
Take app, build it, host it on railway, no problems.
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No thinking required.
152
I can go on holidays.
153
But the team came with Airflow Graph.
154
We use what?
155
1, 2, 3, 4, 13, 14, 23, 60, 400 services just to clean the locks.
156
Whole dashboard?
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You need whole company.
158
I am on call today, yes.
159
As you can see.
160
Oh, I turn my phone off.
161
If it burns, it burns.
162
I don't have keys to service anyway.
163
What am I gonna do?
164
I'm not ignoring Slack.
165
I'm just following our load shedding policy.
166
Don't ship late and don't ship too early.
167
Ship peacefully.
168
Thank you, Railway, for sponsoring this video.
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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación se basa en el vídeo “Interview with Big Data engineer in 2026”. Este vídeo tiene 168 frases y 1183 palabras para practicar shadowing. La parte hablada dura 8:34. El hablante mantiene un ritmo constante de unas 138 palabras por minuto, cómodo para el shadowing. Solo el 76 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| distribute verbo | /dɪˈstɹɪbjuːt/ | distribuir |
| ignore verbo | /ɪɡˈnɔː/ | ignorar, desoír |
| confused adjetivo | /kənˈfjuːzd/ | confundido |
| railway sustantivo | /ˈɹeɪlˌweɪ/ | ferrocarril, ferrovía |
| architecture sustantivo | /ˈɑː.kɪˌtɛk.t͡ʃə/ | arquitectura |
| log sustantivo | /lɔɡ/ | leño, tronco |
| column sustantivo | /ˈkɒləm/ | columna, pilastra |
| database sustantivo | /ˈdeɪtəˌbeɪs/ | base de datos, banco de datos |
| backup sustantivo | /ˈbækˌʌp/ | respaldo |
| availability sustantivo | /əˌveɪ.ləˈbɪl.ɪ.ti/ | disponibilidad |
| warehouse sustantivo | /ˈwɛə(ɹ)haʊs/ | almacén, depósito |
| tolerance sustantivo | /ˈtɑ.lə.ɹəns/ | tolerancia |
| pipeline sustantivo | /ˈpaɪpˌlaɪn/ | oleoducto, tubería |
| cluster sustantivo | /ˈklʌstɚ/ | aglomeración, agrupamiento |
| query sustantivo | /ˈkwɪɹ.i/ | consulta, pregunta |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| fall apart verbo | despedazarse, desintegrarse |
| roll back verbo | revertir |
| turn off verbo | apagar |
Frases que vale la pena repetir
Frases cortas y completas del vídeo que puedes reutilizar en la conversación diaria:
- We don't know if the processing even ran.
- No, that's one of our industry's best practices.
- Our customers didn't even know.
- We haven't spoken to product in seven months.
- What am I gonna do?
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 | have stopped · has existed · haven't spoken |
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | are recovered · was declared · is called |
Pronunciación a tener en cuenta
El hablante usa 17 contracciones y formas reducidas, como don't, I'm, wasn't. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
- Palabras largas — cuida el acento: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: strongly /ˈstɹɔŋli/, slide /ˈslaɪ̯d/, stack /stæk/, sponsor /ˈspɒn.səː/, swamp /ˈswɑmp/
- /v/ — no es /b/: los dientes tocan el labio inferior: availability /əˌveɪ.ləˈbɪl.ɪ.ti/, solve /sɒlv/, arrive /əˈɹaɪv/, recover /ɹɪˈkʌvɚ/, developer /dɪˈvɛləpɚ/
- /z/ — sonora, no /s/: confused /kənˈfjuːzd/, transaction /tɹænˈzækʃən/, preserve /pɹəˈzɝv/, columns /ˈkɑləmz/, unused /ʌnˈjuzd/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Haz shadowing frase por frase a velocidad normal, repitiendo cada una hasta que tu ritmo coincida con el del hablante.
- Grábate y compara con el original, prestando atención a palabras como distribute, ignore, confused.
¿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.