쉐도잉 연습: Interview with Big Data engineer in 2026. - 영상으로 영어 말하기 배우기
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168 문장
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1
We built a real -time pipeline
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So leadership can ignore insights at millisecond speeds
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What was the question?
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Okay, sorry.
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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.
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It's higher than the weather forecast.
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The numbers are not wrong.
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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.
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The slide says we handle billions of events.
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It does not say we handle them well.
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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.
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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.
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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.
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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.
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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.
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Salary, job, security, holidays, pick two, a CIP compliant.
102
Can we have this by Friday?
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In distributed systems, what does Friday mean?
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Before the meeting is only a partial order.
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The project isn't late.
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The clocks just disagree.
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It's a distributed organization.
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Every quarter we solve capacity problems by increasing input.
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We maintain system availability by shedding JIRA tickets.
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Last year, they fired 80 % of our team as part of our operational transformation.
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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.
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Artificial intelligence?
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No. We still rely on humans, a single point of failure.
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Yes, David.
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The game.
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You pull something from the bottom, everything falls apart.
120
It's educational game for industry.
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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.
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Quick 43 months migration.
128
We still use protobuf.
129
Yes, we are still confused, but at least confused strongly type.
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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.
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The AI team spent four million and it called investment.
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I spent three hundred forty thousand.
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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.
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They have a different word for money.
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My cluster is a cost center.
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Their cluster is a strategy.
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Same rack in AWS where I make, there are my glasses.
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Duh!
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Ah, only disk usage 80%.
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This is sweet spot.
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A soft outage is when the checkpoint directory grows faster than S3 versioning can build.
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Back in the days, big data means you bring the intelligence to the data.
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That's why I go straight to big data center.
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We have two big data centers for fault tolerance.
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I wanted to become simple app developer.
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Take app, build it, host it on railway, no problems.
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No thinking required.
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I can go on holidays.
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But the team came with Airflow Graph.
154
We use what?
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1, 2, 3, 4, 13, 14, 23, 60, 400 services just to clean the locks.
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Whole dashboard?
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You need whole company.
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I am on call today, yes.
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As you can see.
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Oh, I turn my phone off.
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If it burns, it burns.
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I don't have keys to service anyway.
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What am I gonna do?
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I'm not ignoring Slack.
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I'm just following our load shedding policy.
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Don't ship late and don't ship too early.
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Ship peacefully.
168
Thank you, Railway, for sponsoring this video.
앱 다운로드
유창하게 말하는 데 필요한 모든 것
AI 발음 평가문장마다 점수 확인
IPA 발음 연습모든 소리 마스터
단어 학습나만의 단어장 만들기
단어 게임놀면서 외우기
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이 레슨의 어휘와 말하기 포인트
이 말하기 레슨은 영상 “Interview with Big Data engineer in 2026”을(를) 바탕으로 합니다. 이 영상에는 섀도잉할 문장 168개와 단어 1183개가 있습니다. 말하는 구간의 길이는 8:34입니다. 화자는 분당 약 138단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 76%뿐이라 어휘가 어려운 편입니다.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| ignore 동사 | /ɪɡˈnɔː/ | 무시하다 |
| railway 명사 | /ˈɹeɪlˌweɪ/ | 철도 |
| architecture 명사 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 건축, 건축학 |
| log 명사 | /lɔɡ/ | 통나무 |
| column 명사 | /ˈkɒləm/ | 기둥 |
| database 명사 | /ˈdeɪtəˌbeɪs/ | 데이터베이스 |
| backup 명사 | /ˈbækˌʌp/ | 백업 |
| availability 명사 | /əˌveɪ.ləˈbɪl.ɪ.ti/ | 가용도 |
| warehouse 명사 | /ˈwɛə(ɹ)haʊs/ | 창고 |
| pipeline 명사 | /ˈpaɪpˌlaɪn/ | 관로, 파이프라인 |
| query 명사 | /ˈkwɪɹ.i/ | 질문 |
| topic 명사 | /ˈtɑpɪk/ | 화제, 주제 |
| cloud 명사 | /ˈklaʊ̯d/ | 구름 |
| solve 동사 | /sɒlv/ | 해결하다 |
| deck 명사 | /ˈdɛk/ | 갑판 |
영상에 나오는 구동사
| 단어 | 뜻 |
|---|---|
| go down 동사 | 내려가다 |
| turn off 동사 | 끄다, 턴오프하다 |
따라 말해 볼 만한 문장
영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:
- 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?
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | have stopped · has existed · haven't spoken |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | are recovered · was declared · is called |
주의할 발음
화자는 don't, I'm, wasn't 같은 축약형과 약화된 형태를 17번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
- 긴 단어 — 강세 위치에 주의: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: confused /kənˈfjuːzd/, artificial /ˌɑː.tɪˈfɪʃ.əl/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, forty /ˈfoɹti/, interface /ˈɪn(t)ɚˌfeɪs/
- /v/ — ㅂ(/b/)과 구별하기: availability /əˌveɪ.ləˈbɪl.ɪ.ti/, solve /sɒlv/, arrive /əˈɹaɪv/, recover /ɹɪˈkʌvɚ/, developer /dɪˈvɛləpɚ/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: confused /kənˈfjuːzd/, transaction /tɹænˈzækʃən/, preserve /pɹəˈzɝv/, columns /ˈkɑləmz/, unused /ʌnˈjuzd/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
- 자신의 목소리를 녹음해 원본과 비교하고, ignore, railway, architecture 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.