Pratique du Shadowing: Interview with Big Data engineer in 2026. - Apprendre l'anglais à l'oral avec la vidéo

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168 phrases
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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
6
Problem in database, but no worries all 200 tetabytes are recovered
7
Big data most companies don't actually have big data
8
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.
11
Two wrongs don't make a right, except in a distributed database.
12
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.
17
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.
20
We don't know if the processing even ran.
21
That was just observability.
22
We support real -time processing, of course.
23
Unless the event arrives early, late, duplicated, malformed, reordered, eventual consistency was too hard.
24
So we went with immediately inaccurate.
25
70 % of records are accurate.
26
It's higher than the weather forecast.
27
The numbers are not wrong.
28
They are early.
29
Wait, two, three, maybe four days, they will be late.
30
It's almost interactive analytics.
31
Final numbers mean numbers we have stopped refreshing.
32
The slide says we handle billions of events.
33
It does not say we handle them well.
34
It is only truly idempotent if all availability zones go down equally during the outage.
35
Management.
36
Management asked us to build a data lake, but it wasn't scalable enough.
37
So McKinsey asked us to build a data swamp.
38
The project was declared successful before all the metrics arrived.
39
We call that watermarking.
40
Dashboard is green because the job that checks if the dashboard is wrong is red.
41
Ah, what happened here?
42
Let me quickly roll back 30 billion events.
43
A production workaround becomes durable the moment the engineer who wrote it leaves.
44
Yes.
45
We removed an unused column and it broke Portugal.
46
The bug has existed for eight years.
47
At this point it's an interface.
48
Quick Slack message.
49
20 terabytes lost again.
50
This is our architecture.
51
Very simple.
52
Kafka, Airflow, Databricks, Flink, Spark, SQL, Iceberg, S3, Presto, DBT.
53
What does DBT stands for dialect behavioral therapy.
54
We need that to avoid going crazy.
55
We have the most modern stack in the industry.
56
Only 10 % is COBOL.
57
HDFS is not dead.
58
It's waiting for someone with root access.
59
We migrated from Kroon to Airflow for reliability.
60
Then we wrote a Kroon job to check if Airflow silently died.
61
We would switch from Databricks to Excel if it wasn't for date parsing.
62
We are a data driven company of course.
63
Every week somebody reads the invoices out of PDFs and types the data into Excel.
64
Running on Kubernetes.
65
We moved from Hive tables to Iceberg tables because we wanted the metadata to fail with better governance.
66
Last week, we had some ransomware attack.
67
We lost the backups, yes.
68
Whenever we lose a backup, we reconstruct what the customer did from the logs.
69
No, that's one of our industry's best practices.
70
Our customers didn't even know.
71
To be honest, most of our customers don't even use our product anyway.
72
The board deck says petabyte scale, yeah.
73
Scale is a direction, not a quantity.
74
I checked with a legal.
75
Management says one petabyte.
76
I checked, yeah.
77
We only process 18 terabytes, but we round up.
78
Our P99 is four minutes.
79
But the P50 is excellent.
80
And most people are median.
81
Streaming gets simple once you understand watermarks.
82
Event time, processing time, state, back pressure.
83
Batch and streaming produce different numbers.
84
We average them.
85
Our recommendation pipeline had three hours of Kafka lag.
86
So technically it was a real time communication for your past self.
87
Kafka rebalanced so often I started listing it under team building activities.
88
We decoupled storage and compute.
89
Now the storage team and the compute team have separate outages.
90
Ah, again, we lost the logs from Tuesday.
91
The data team, the data team has excellent isolation.
92
We haven't spoken to product in seven months.
93
All problems become your problems at one terabyte an hour.
94
Exactly once processing is easy.
95
You just need two topics, transactions, offsets, recovery state and a relaxed interpretation of easy.
96
GDPR in a data warehouse.
97
This is a funny joke.
98
We anonymize the data by removing the column name.
99
We persist it in places nobody knows how to query.
100
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.
106
The clocks just disagree.
107
It's a distributed organization.
108
Every quarter we solve capacity problems by increasing input.
109
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.
112
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.
114
A very sophisticated way of never letting it turn off.
115
Artificial intelligence?
116
No. We still rely on humans, a single point of failure.
117
Yes, David.
118
The game.
119
You pull something from the bottom, everything falls apart.
120
It's educational game for industry.
121
Back 2016, learning Hadoop in the simple 760 steps.
122
That's right.
123
Hadoop certified.
124
Cloudera certified.
125
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.
131
We cannot stop producing it and we will pay to clean it up for years.
132
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.
136
I'm in FinOps review every Thursday.
137
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.
141
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.
151
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?
157
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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Vocabulaire et conseils d’expression pour cette leçon

Cette leçon d’expression orale s’appuie sur la vidéo « Interview with Big Data engineer in 2026 ». Cette vidéo contient 168 phrases et 1183 mots à répéter en shadowing. La partie parlée dure 8:34. Le locuteur parle à un rythme régulier d’environ 138 mots par minute, confortable pour le shadowing. Seuls 76 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.

Vocabulaire clé de cette vidéo

15 mots de la vidéo à retenir, avec leur prononciation et leur sens :

MotPrononciationSens
distribute verbe/dɪˈstɹɪbjuːt/distribuer
ignore verbe/ɪɡˈnɔː/ignorer, ne pas prêter attention à
confused adjectif/kənˈfjuːzd/désorienté, perdu
railway nom/ˈɹeɪlˌweɪ/chemin de fer
architecture nom/ˈɑː.kɪˌtɛk.t͡ʃə/architecture
log nom/lɔɡ/grume, tronc
column nom/ˈkɒləm/colonne
database nom/ˈdeɪtəˌbeɪs/base de données, banque de données
backup nom/ˈbækˌʌp/remplaçant
availability nom/əˌveɪ.ləˈbɪl.ɪ.ti/disponibilité
warehouse nom/ˈwɛə(ɹ)haʊs/entrepôt, dépôt
tolerance nom/ˈtɑ.lə.ɹəns/tolérance
pipeline nom/ˈpaɪpˌlaɪn/pipeline
cluster nom/ˈklʌstɚ/groupe
query nom/ˈkwɪɹ.i/question

Les verbes à particule que vous entendrez

MotSens
fall apart verbetomber en morceaux
go down verbedescendre
turn off verbeéteindre

Des phrases à répéter

Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :

  • 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?

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

StructureDans la vidéo
Present perfect have/has + participe passé — une action passée qui compte encore maintenanthave stopped · has existed · haven't spoken
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le faitare recovered · was declared · is called

Prononciation à surveiller

Le locuteur utilise 17 contractions et formes réduites, comme don't, I'm, wasn't. Prononcez-les sous leur forme courte, telles que vous les entendez.

  • Les sons « sh » et « zh »: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
  • Mots longs — placez bien l’accent: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/

Les sons difficiles pour les francophones :

  • /h/ — il se souffle, il n’est pas muet: warehouse /ˈwɛə(ɹ)haʊs/, behavioral /bɪˈheɪvjəɹəl/
  • /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, usage /ˈjuːsɪd͡ʒ/, architectural /ˌɑɹkɪˈtɛkt͡ʃəɹəl/, batch /bæt͡ʃ/, eventual /ɪˈvɛn.t͡ʃu.əl/
  • /r/ anglais — langue recourbée, sans frotter la gorge: distribute /dɪˈstɹɪbjuːt/, railway /ˈɹeɪlˌweɪ/, warehouse /ˈwɛə(ɹ)haʊs/, tolerance /ˈtɑ.lə.ɹəns/, query /ˈkwɪɹ.i/

Comment s’entraîner avec cette vidéo

  1. Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
  2. Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
  3. Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme distribute, ignore, confused.

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →