Shadowing-Übung: Interview with Big Data engineer in 2026. - Englisch Sprechen Lernen mit Video

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168 Sätze
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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.
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Yes.
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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.
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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.
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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.
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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.
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We migrated to Abachi iceberg this year.
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Quick 43 months migration.
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We still use protobuf.
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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.
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Thank you, Railway, for sponsoring this video.

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Wortschatz und Sprechhinweise zu dieser Lektion

Diese Sprechübung basiert auf dem Video „Interview with Big Data engineer in 2026“. Dieses Video enthält 168 Sätze und 1183 Wörter zum Nachsprechen. Der gesprochene Teil dauert 8:34. Der Sprecher spricht gleichmäßig mit etwa 138 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. Nur 76 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.

Wichtiger Wortschatz in diesem Video

15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:

WortAusspracheBedeutung
distribute Verb/dɪˈstɹɪbjuːt/aufteilen, verteilen
ignore Verb/ɪɡˈnɔː/ignorieren, missachten
confused Adjektiv/kənˈfjuːzd/verwirrt, durcheinander
railway Substantiv/ˈɹeɪlˌweɪ/Eisenbahn, Bahn
architecture Substantiv/ˈɑː.kɪˌtɛk.t͡ʃə/Architektur, Baustil
log Substantiv/lɔɡ/Rundholz (forestial), Langholz (forestial)
column Substantiv/ˈkɒləm/Säule
database Substantiv/ˈdeɪtəˌbeɪs/Datenbank
backup Substantiv/ˈbækˌʌp/Unterstützung, Deckung
availability Substantiv/əˌveɪ.ləˈbɪl.ɪ.ti/Vorhandensein, Verfügbarkeit
warehouse Substantiv/ˈwɛə(ɹ)haʊs/Lager, Lagerhaus
tolerance Substantiv/ˈtɑ.lə.ɹəns/Toleranz
pipeline Substantiv/ˈpaɪpˌlaɪn/Pipeline, Rohrleitung
cluster Substantiv/ˈklʌstɚ/Anhäufung, Büschel
query Substantiv/ˈkwɪɹ.i/Frage

Phrasal Verbs, die du hören wirst

WortBedeutung
fall apart Verbauseinanderfallen
go down Verbabsteigen, hinabsteigen
turn off Verbausschalten, abschalten

Sätze zum Wiederholen

Kurze, vollständige Sätze aus dem Video, die du im Alltag wiederverwenden kannst:

  • 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?

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

StrukturIm Video
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig isthave stopped · has existed · haven't spoken
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tutare recovered · was declared · is called

Aussprache, auf die du achten solltest

Der Sprecher verwendet 17 Kurzformen und abgeschwächte Formen, zum Beispiel don't, I'm, wasn't. Sprich sie in der kurzen Form, so wie du sie hörst.

  • Die Laute „sh“ und „zh“: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
  • Lange Wörter – auf die Betonung achten: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/

Laute, die Deutschsprachigen schwerfallen:

  • /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): railway /ˈɹeɪlˌweɪ/, warehouse /ˈwɛə(ɹ)haʊs/, query /ˈkwɪɹ.i/, equally /ˈiːkwəli/, quantity /ˈkwɑn(t)ɪti/
  • Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: confused /kənˈfjuːzd/, log /lɔɡ/, cloud /ˈklaʊ̯d/, solve /sɒlv/, arrive /əˈɹaɪv/
  • /æ/ — offener als „ä“: backup /ˈbækˌʌp/, salary /ˈsæləɹi/, relax /ɹɪˈlæks/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/

So übst du mit diesem Video

  1. Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
  2. Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
  3. Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie distribute, ignore, confused.

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →