シャドーイング練習: Interview with Big Data engineer in 2026. - 動画で英語スピーキングを学ぶ
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168 文
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1
We built a real -time pipeline
2
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.
9
Big data is anything that crushes Excel.
10
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.
13
1 plus 1, 1 .9.
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95 % confidence interval.
15
Our 15 million raw data set has 800 columns.
16
The rows aren't wide, they are panoramic.
17
If the joint fits in memory, it belongs in Postgres.
18
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.
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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.
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We need that to avoid going crazy.
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We have the most modern stack in the industry.
56
Only 10 % is COBOL.
57
HDFS is not dead.
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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.
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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.
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.
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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.
80
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.
85
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.
88
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.
92
We haven't spoken to product in seven months.
93
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.
98
We anonymize the data by removing the column name.
99
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.
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.
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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.
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.
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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.
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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このレッスンの語彙とスピーキングのポイント
このスピーキングレッスンは、動画「Interview with Big Data engineer in 2026」を教材にしています。 この動画には、シャドーイング用の文が168文、単語が1183語あります。 音声の長さは8:34です。 話す速さは1分あたり約138語で安定しており、シャドーイングしやすいペースです。 英語の頻出3,000語に含まれる単語は76%だけなので、語彙は難しめです。
この動画の重要語彙
動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| distribute 動詞 | /dɪˈstɹɪbjuːt/ | 分ける |
| ignore 動詞 | /ɪɡˈnɔː/ | 無視する, 忽略する |
| railway 名詞 | /ˈɹeɪlˌweɪ/ | 鉄道 |
| architecture 名詞 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 建築, 建築学 |
| log 名詞 | /lɔɡ/ | 丸太, 丸木 |
| column 名詞 | /ˈkɒləm/ | 柱 |
| database 名詞 | /ˈdeɪtəˌbeɪs/ | データベース |
| backup 名詞 | /ˈbækˌʌp/ | 予備, 控え |
| availability 名詞 | /əˌveɪ.ləˈbɪl.ɪ.ti/ | 供給, 可用性 |
| warehouse 名詞 | /ˈwɛə(ɹ)haʊs/ | 倉庫 |
| tolerance 名詞 | /ˈtɑ.lə.ɹəns/ | 寛容 |
| pipeline 名詞 | /ˈpaɪpˌlaɪn/ | パイプライン |
| query 名詞 | /ˈkwɪɹ.i/ | 質問 |
| topic 名詞 | /ˈtɑpɪk/ | 話題, テーマ |
| cloud 名詞 | /ˈklaʊ̯d/ | 雲, 雲霞 |
動画に出てくる句動詞
| 単語 | 意味 |
|---|---|
| go down 動詞 | 降りる, 降下する |
| turn off 動詞 | 消す, 切る |
繰り返し練習したい文
動画に出てくる短く完結した文です。日常会話でそのまま使えます。
- 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?
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | have stopped · has existed · haven't spoken |
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | are recovered · was declared · is called |
注意したい発音
話し手はdon't, I'm, wasn'tなど、短縮形や弱形を17回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
- 長い単語（アクセントの位置に注意）: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: railway /ˈɹeɪlˌweɪ/, tolerance /ˈtɑ.lə.ɹəns/, strongly /ˈstɹɔŋli/, salary /ˈsæləɹi/, relax /ɹɪˈlæks/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: availability /əˌveɪ.ləˈbɪl.ɪ.ti/, solve /sɒlv/, arrive /əˈɹaɪv/, recover /ɹɪˈkʌvɚ/, developer /dɪˈvɛləpɚ/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: confused /kənˈfjuːzd/, artificial /ˌɑː.tɪˈfɪʃ.əl/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, forty /ˈfoɹti/, interface /ˈɪn(t)ɚˌfeɪs/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- 通常の速度で一文ずつシャドーイングし、話し手のリズムに合うまで繰り返します。
- 自分の声を録音して元の音声と比べます。distribute, ignore, railwayなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。