शैडोइंग अभ्यास: Interview with Big Data engineer in 2026. - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।

168 वाक्य
यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।
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.

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Interview with Big Data engineer in 2026” पर आधारित है। इस वीडियो में शैडोइंग के लिए 168 वाक्य और 1183 शब्द हैं। बोला गया हिस्सा 8:34 लंबा है। वक्ता लगभग 138 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। केवल 76% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्दउच्चारणअर्थ
ignore क्रिया/ɪɡˈnɔː/उपेक्षा, उपेक्षा करना
confused विशेषण/kənˈfjuːzd/भ्रमित
railway संज्ञा/ˈɹeɪlˌweɪ/रेलवे
architecture संज्ञा/ˈɑː.kɪˌtɛk.t͡ʃə/वास्तुकला, स्थापत्य
log संज्ञा/lɔɡ/कुंदा, लट्ठा
column संज्ञा/ˈkɒləm/स्तंभ, सुतून
database संज्ञा/ˈdeɪtəˌbeɪs/डेटाबेस
warehouse संज्ञा/ˈwɛə(ɹ)haʊs/कोठी, मालगोदाम
pipeline संज्ञा/ˈpaɪpˌlaɪn/पाइपलाइन
cluster संज्ञा/ˈklʌstɚ/गुच्छ
topic संज्ञा/ˈtɑpɪk/प्रसंग, विषय
cloud संज्ञा/ˈklaʊ̯d/मेघ, बादल
solve क्रिया/sɒlv/सुलझाना, हल करना
arrive क्रिया/əˈɹaɪv/पहुँचना, आना
introduce क्रिया/ˌɪntɹəˈdus/परिचय

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्दअर्थ
turn off क्रियाबंद करना

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

  • 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?

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचनावीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी हैhave stopped · has existed · haven't spoken
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता हैare recovered · was declared · is called

ध्यान देने योग्य उच्चारण

वक्ता 17 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'm, wasn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

  • “sh” और “zh” की ध्वनियाँ: artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, transaction /tɹænˈzækʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/
  • लंबे शब्द — बलाघात सही रखें: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, interpretation /ɪnˌtɜːpɹəˈteɪʃən/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

  • /v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: railway /ˈɹeɪlˌweɪ/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, warehouse /ˈwɛə(ɹ)haʊs/, query /ˈkwɪɹ.i/, solve /sɒlv/
  • /z/ — इसे “ज” (/dʒ/) न बनाएँ: confused /kənˈfjuːzd/, transaction /tɹænˈzækʃən/, preserve /pɹəˈzɝv/, columns /ˈkɑləmz/, unused /ʌnˈjuzd/
  • शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: strongly /ˈstɹɔŋli/, slide /ˈslaɪ̯d/, stack /stæk/, sponsor /ˈspɒn.səː/, swamp /ˈswɑmp/

इस वीडियो से अभ्यास कैसे करें

  1. पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
  2. सामान्य गति पर एक-एक वाक्य की शैडोइंग करें और हर वाक्य तब तक दोहराएँ जब तक आपकी लय वक्ता से न मिल जाए।
  3. अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर ignore, confused, railway जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →