跟读练习: Interview with Big Data engineer in 2026. - 通过视频学习英语口语
选择练习模式
每种模式都会打开全屏练习室。
168 句
如果句子过短或过长，请点击 Edit 进行调整。
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.
14
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.
19
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.
同频道
FOR YOU
推荐视频
C11
358 句
It’s not just you
Daily ConversationUniversity StudentsWorking Adults
C11
77 句
The Secret Sauce Behind NoSQL: LSM Tree
Daily ConversationUniversity StudentsWorking Adults
C13
134 句
How Uber Scaled from One Server to 150 Million Reads Per Second
Daily ConversationUniversity Students
C12
118 句
The Anatomy of Fraud
Daily ConversationUniversity StudentsWorking Adults
C11
398 句
Sec Growth DataScience staff meeting Sep 14 2022
Daily ConversationUniversity StudentsWorking Adults
C138 句
"Dive Deep" Leadership Principle Explained by Amazon CEO Andy Jassy
Daily ConversationUniversity StudentsWorking Adults
C11
689 句
Database Sharding! Designing Data-Intensive Applications chapter 6
Daily ConversationUniversity StudentsWorking Adults
C11
389 句
They JUST Accidentally Revealed the Truth: The AI Bubble
Daily ConversationUniversity StudentsWorking Adults
B21
220 句
How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)
Daily ConversationUniversity Students
C1231 句
Redis Crash Course - the What, Why and How to use Redis as your primary database
Daily ConversationUniversity StudentsWorking Adults
C11
532 句
How an Ex-Googler Accidentally Founded a Database Company
Daily ConversationUniversity StudentsWorking Adults
C11
37 句
How Anthropic uses Claude in Product Management
Daily ConversationUniversity StudentsWorking Adults
本课的词汇与口语要点
这节口语课以视频“Interview with Big Data engineer in 2026”为素材。 这段视频共有 168 个句子、1183 个单词可供跟读。 讲话部分时长为 8:34。 说话人语速平稳，每分钟约 138 个词，很适合跟读。 只有 76% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| distribute 动词 | /dɪˈstɹɪbjuːt/ | 分配 |
| ignore 动词 | /ɪɡˈnɔː/ | 無視 /无视, 忽略 |
| confused 形容词 | /kənˈfjuːzd/ | 困惑 |
| railway 名词 | /ˈɹeɪlˌweɪ/ | 鐵路 /铁路, 鐵道 /铁道 |
| architecture 名词 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 建築 /建筑, 建築學 /建筑学 |
| log 名词 | /lɔɡ/ | 圓木 /圆木 |
| column 名词 | /ˈkɒləm/ | 柱子, 柱 |
| database 名词 | /ˈdeɪtəˌbeɪs/ | 數據庫 /数据库 |
| backup 名词 | /ˈbækˌʌp/ | 備份 /备份 |
| availability 名词 | /əˌveɪ.ləˈbɪl.ɪ.ti/ | 可及性 |
| warehouse 名词 | /ˈwɛə(ɹ)haʊs/ | 倉庫 /仓库 |
| tolerance 名词 | /ˈtɑ.lə.ɹəns/ | 寬容 /宽容 |
| pipeline 名词 | /ˈpaɪpˌlaɪn/ | 管道, 管線 /管线 |
| cluster 名词 | /ˈklʌstɚ/ | 串, 叢 /丛 |
| query 名词 | /ˈkwɪɹ.i/ | 問題 /问题, 質問 /质问 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| go down 动词 | 下降, 下去 |
| turn off 动词 | 關 /关, 關掉 /关掉 |
值得反复跟读的句子
视频中简短而完整的句子，可以直接用在日常对话里：
- 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?
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | have stopped · has existed · haven't spoken |
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | are recovered · was declared · is called |
需要注意的发音
说话人用了 17 次缩略和弱读形式，例如 don't, I'm, wasn't。请按听到的简短形式来说。
- “sh” 和 “zh” 音: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
- 长单词——注意重音位置: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: availability /əˌveɪ.ləˈbɪl.ɪ.ti/, solve /sɒlv/, arrive /əˈɹaɪv/, recover /ɹɪˈkʌvɚ/, developer /dɪˈvɛləpɚ/
- 词尾辅音 — 要读清楚，后面不要加元音: distribute /dɪˈstɹɪbjuːt/, confused /kənˈfjuːzd/, log /lɔɡ/, database /ˈdeɪtəˌbeɪs/, backup /ˈbækˌʌp/
- /r/ 和 /l/ 的区别: railway /ˈɹeɪlˌweɪ/, tolerance /ˈtɑ.lə.ɹəns/, strongly /ˈstɹɔŋli/, salary /ˈsæləɹi/, relax /ɹɪˈlæks/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
- 录下自己的声音并与原声对比，特别注意 distribute, ignore, confused 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。