A small win I wanted to share with you 💙
I’ve been featured in Topmate’s “25 in 25 – Data Engineering” list and ranked #5 among data engineering creators in India.
This list recognises mentors and creators who are contributing consistently to learning, mentoring, and https://t.co/g3op0bX12t
I’ve been featured in Topmate’s “25 in 25 – Data Engineering” list and ranked #5 among data engineering creators in India.
This list recognises mentors and creators who are contributing consistently to learning, mentoring, and https://t.co/g3op0bX12t
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Spark memory management is responsible for 70%+ of interview questions.
It's the #1 reason production jobs fail.
Yet most data engineers guess their configs.
The problem starts with understanding overhead.
When you set executor memory, Spark adds overhead:
Overhead = max(384
It's the #1 reason production jobs fail.
Yet most data engineers guess their configs.
The problem starts with understanding overhead.
When you set executor memory, Spark adds overhead:
Overhead = max(384
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https://t.co/Zd6rr8CV5Q
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AI feels magical until the data pipeline breaks.
Then it’s just statistics waiting on a fix.
Data engineering is the real backbone.
Then it’s just statistics waiting on a fix.
Data engineering is the real backbone.
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Krishna worked at Infosys on legacy tech.
Just became a father.
He asked his manager for modern tech.
Offered to train others on current stack.
Got AWS and Snowflake.
Cloud was completely new to him.
He joined my bootcamp.
Finished it while managing new parent
Just became a father.
He asked his manager for modern tech.
Offered to train others on current stack.
Got AWS and Snowflake.
Cloud was completely new to him.
He joined my bootcamp.
Finished it while managing new parent
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I just published Why Experienced Data Professionals Struggle to Crack Cloud Data Engineering Interviews https://t.co/EZjhcK2tl3
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Overwhelmed by too many tools, services, and jargon?
I created a clear, step-by-step roadmap.
The one I personally teach in my bootcamps.
From foundational skills to advanced pipeline design:
Python and SQL fundamentals
Spark, Glue, PySpark
S3, Redshift, Athena, Lake Formation
I created a clear, step-by-step roadmap.
The one I personally teach in my bootcamps.
From foundational skills to advanced pipeline design:
Python and SQL fundamentals
Spark, Glue, PySpark
S3, Redshift, Athena, Lake Formation
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2
Would you prefer
Working for 3 months to build, test, deploy & maintain a data pipeline.
Or
Build an AI agent in 3 hours that would build, test, deploy & maintain a data pipeline.
Working for 3 months to build, test, deploy & maintain a data pipeline.
Or
Build an AI agent in 3 hours that would build, test, deploy & maintain a data pipeline.
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I wouldn't advise trying data engineering right out of college in India.
There are rare cases where fresh graduates find DE roles.
Data engineering demands expertise, experience,
and deep understanding of complex systems.
It's more than just writing code:
Designing
There are rare cases where fresh graduates find DE roles.
Data engineering demands expertise, experience,
and deep understanding of complex systems.
It's more than just writing code:
Designing
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#dezoomcamp
🐳 Module 1 of Data Engineering Zoomcamp done!
- Docker containers
- Postgres & SQL
- Terraform & GCP
- NYC taxi data pipeline
My solution: https://t.co/N3MLMJsK3r
Free course by @DataTalksClub: https://t.co/gn7EfGWrnw
🐳 Module 1 of Data Engineering Zoomcamp done!
- Docker containers
- Postgres & SQL
- Terraform & GCP
- NYC taxi data pipeline
My solution: https://t.co/N3MLMJsK3r
Free course by @DataTalksClub: https://t.co/gn7EfGWrnw
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How did you prepare for Data Engineer interviews?
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Git can be confusing for beginners.
But there's not a lot you need to learn to get interview-ready.
I never used Git until 2019.
Used GitHub for a Machine Learning course project.
2020 was when I started AWS work.
Since then, regular Git user.
Master these fundamentals:
But there's not a lot you need to learn to get interview-ready.
I never used Git until 2019.
Used GitHub for a Machine Learning course project.
2020 was when I started AWS work.
Since then, regular Git user.
Master these fundamentals:
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Serious question: what part of your job do you hope AI never touches?
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After 17 years, if I were starting data engineering today,
here are the top 6 things I'd focus on:
Data Modeling:
Master creating efficient structures.
Understand relational, dimensional, and NoSQL models.
Know when to choose each.
Data Integration:
Combine different sources
here are the top 6 things I'd focus on:
Data Modeling:
Master creating efficient structures.
Understand relational, dimensional, and NoSQL models.
Know when to choose each.
Data Integration:
Combine different sources
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Your data is lying to you.
And it might be costing your company millions.
73% of datasets have nulls.
Average cost of bad data: $3.1M.
Time to fix it in PySpark: 8 minutes.
If you're ignoring missing values, your analytics are wrong.
Detect nulls: https://t.co/R3VNOZrKV8
And it might be costing your company millions.
73% of datasets have nulls.
Average cost of bad data: $3.1M.
Time to fix it in PySpark: 8 minutes.
If you're ignoring missing values, your analytics are wrong.
Detect nulls: https://t.co/R3VNOZrKV8
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"My Spark job failed but I had 8 GB executor memory."
Ever said this yourself?
Let me tell you a hard truth:
Spark memory management is responsible for 70%+ of interview questions.
It's also the #1 reason production jobs fail.
Yet most data engineers guess their memory configs. https://t.co/RFxp3bPeMN
Ever said this yourself?
Let me tell you a hard truth:
Spark memory management is responsible for 70%+ of interview questions.
It's also the #1 reason production jobs fail.
Yet most data engineers guess their memory configs. https://t.co/RFxp3bPeMN
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Most candidates learn Spark DataFrames and Spark SQL.
Then the interview question hits:
"How do you optimize Spark jobs in production?"
That's when memorized answers fail.
Here's what actually matters:
Use Parquet for columnar reads.
Partition data if downstream consumers
Then the interview question hits:
"How do you optimize Spark jobs in production?"
That's when memorized answers fail.
Here's what actually matters:
Use Parquet for columnar reads.
Partition data if downstream consumers
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Here’s the hard truth: Companies pay a premium for engineers who understand data warehousing.
If you’ve only built pipelines or done ETL transformations — you’re missing a key piece of the puzzle.
Skills in demand:
Dimensional modeling & star/snowflake schemas
Partitioning,
If you’ve only built pipelines or done ETL transformations — you’re missing a key piece of the puzzle.
Skills in demand:
Dimensional modeling & star/snowflake schemas
Partitioning,
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Data warehousing is here to stay.
When I joined Cognizant in 2015, there was an entire business unit called:
Enterprise Information Management (formerly DWBI—Data Warehousing & BI).
One BU. Just for data warehousing.
That's how crucial this foundation is.
And with Amazon
When I joined Cognizant in 2015, there was an entire business unit called:
Enterprise Information Management (formerly DWBI—Data Warehousing & BI).
One BU. Just for data warehousing.
That's how crucial this foundation is.
And with Amazon
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Batch vs Streaming isn't about technology preference.
It's about business requirements vs operational complexity.
Streaming sounds exciting. Kafka, Spark Streaming, real-time dashboards.
But streaming means: always-on infrastructure, complex failure handling, and 3 AM debugging.
It's about business requirements vs operational complexity.
Streaming sounds exciting. Kafka, Spark Streaming, real-time dashboards.
But streaming means: always-on infrastructure, complex failure handling, and 3 AM debugging.
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50K LinkedIn|On a mission to get Data professionals AWS Data Engineering Jobs! | Founder - Data Engineering Hub | Lead Data Engineer @ World’s #1 Airline
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Principal Software Engineer | Building Data & AI systems at scale | Career, pay, architecture & hard lessons | Travel & lifestyle occasionally ✈️
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