๐ PyTorch vs TensorFlow โ Which Should YOU Choose?
If youโre starting in AI or planning to build real-world apps, this is the big question.
๐ PyTorch โ simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
๐ TensorFlow โ built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.
โจ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
๐ Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.
๐ก Think of it like this:
PyTorch = Notebook for experiments โ๏ธ
TensorFlow = Office suite for real apps ๐ข
So the choice is simple:
Learning & Research โ PyTorch
Scaling & Deployment โ TensorFlow
If youโre starting in AI or planning to build real-world apps, this is the big question.
๐ PyTorch โ simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
๐ TensorFlow โ built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.
โจ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
๐ Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.
๐ก Think of it like this:
PyTorch = Notebook for experiments โ๏ธ
TensorFlow = Office suite for real apps ๐ข
So the choice is simple:
Learning & Research โ PyTorch
Scaling & Deployment โ TensorFlow
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Amazon Interview Process for Data Scientist position
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
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โจ๏ธ MongoDB Cheat Sheet
This Post includes a MongoDB cheat sheet to make it easy for our followers to work with MongoDB.
Working with databases
Working with rows
Working with Documents
Querying data from documents
Modifying data in documents
Searching
MongoDB is a flexible, document-orientated, NoSQL database program that can scale to any enterprise volume without compromising search performance.
This Post includes a MongoDB cheat sheet to make it easy for our followers to work with MongoDB.
Working with databases
Working with rows
Working with Documents
Querying data from documents
Modifying data in documents
Searching
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๐ Walk-in Hiring Drive Alert! ๐
AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!
* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3โ6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai
๐๐ป Data Analyst: https://go.acciojob.com/47HSHh
๐๐ป Data Engineer: https://go.acciojob.com/PnRTK2
AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!
* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3โ6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai
๐๐ป Data Analyst: https://go.acciojob.com/47HSHh
๐๐ป Data Engineer: https://go.acciojob.com/PnRTK2
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๐ Step-by-Step Guide to Become a Data Engineer in 2025 ๐ ๏ธ๐
1๏ธโฃ Start with Programming Basics
Learn Python or Java โ essential for scripting, automation & handling data.
2๏ธโฃ Understand Databases
Master SQL for querying, plus NoSQL (MongoDB, Cassandra) for unstructured data.
3๏ธโฃ Learn Data Warehousing
Get comfy with ETL, OLAP, Star/Snowflake schemas. Tools: Snowflake, Redshift, BigQuery.
4๏ธโฃ Work with Big Data Tools
Explore Hadoop, Spark, Kafka โ key for large-scale data processing.
5๏ธโฃ Get Hands-On with Cloud Platforms
Focus on AWS, Azure, or GCP โ master data services like S3, Lambda, Glue, BigQuery.
6๏ธโฃ Practice Building Data Pipelines
Use Apache Airflow, dbt, or Prefect to build and orchestrate workflows end-to-end.
7๏ธโฃ Version Control & CI/CD
Learn GitHub, Docker, Jenkins for collaboration and deployment.
8๏ธโฃ Build a Strong Portfolio
Show off pipeline projects, cloud workflows, and architecture diagrams.
9๏ธโฃ Apply for Data Engineering Roles
Look for titles like Data Engineer, ETL Developer, Cloud Data Engineer.
๐ Keep Growing & Learning
Dive into real-time streaming, data security, optimization, and advanced data modeling.
โโโโโโโโโโ
๐ฅ In 2025, top skills include cloud computing, big data, ETL, programming (Python/Java), and data warehousing. Focus where demand is highest!
๐ก Start small, build projects, experiment on free cloud tiers, and stay updated with emerging tech.
๐ฌ Tap โค๏ธ for more!
1๏ธโฃ Start with Programming Basics
Learn Python or Java โ essential for scripting, automation & handling data.
2๏ธโฃ Understand Databases
Master SQL for querying, plus NoSQL (MongoDB, Cassandra) for unstructured data.
3๏ธโฃ Learn Data Warehousing
Get comfy with ETL, OLAP, Star/Snowflake schemas. Tools: Snowflake, Redshift, BigQuery.
4๏ธโฃ Work with Big Data Tools
Explore Hadoop, Spark, Kafka โ key for large-scale data processing.
5๏ธโฃ Get Hands-On with Cloud Platforms
Focus on AWS, Azure, or GCP โ master data services like S3, Lambda, Glue, BigQuery.
6๏ธโฃ Practice Building Data Pipelines
Use Apache Airflow, dbt, or Prefect to build and orchestrate workflows end-to-end.
7๏ธโฃ Version Control & CI/CD
Learn GitHub, Docker, Jenkins for collaboration and deployment.
8๏ธโฃ Build a Strong Portfolio
Show off pipeline projects, cloud workflows, and architecture diagrams.
9๏ธโฃ Apply for Data Engineering Roles
Look for titles like Data Engineer, ETL Developer, Cloud Data Engineer.
๐ Keep Growing & Learning
Dive into real-time streaming, data security, optimization, and advanced data modeling.
โโโโโโโโโโ
๐ฅ In 2025, top skills include cloud computing, big data, ETL, programming (Python/Java), and data warehousing. Focus where demand is highest!
๐ก Start small, build projects, experiment on free cloud tiers, and stay updated with emerging tech.
๐ฌ Tap โค๏ธ for more!
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Stop obsessing over Python and SQL skills.
Here are 5 non-technical skills that make exceptional data analysts:
- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.
- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.
- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.
- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isnโt the one that truly needs solving. Itโs your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.
Technical skills may get you started, but itโs the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.
The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
Here are 5 non-technical skills that make exceptional data analysts:
- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.
- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.
- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.
- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isnโt the one that truly needs solving. Itโs your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.
Technical skills may get you started, but itโs the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.
The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
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Databricks_Fundamentals_Interview_Preparation_Q_A_1754131443.pdf
6.7 KB
Databricks Fundamentals
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๐ฅ 20 Data Engineering Interview Questions
1. What is Data Engineering?
Data engineering is the design, construction, testing, and maintenance of systems that collect, manage, and convert raw data into usable information for data scientists and business analysts.
2. What are the key responsibilities of a Data Engineer?
Building and maintaining data pipelines, ETL processes, data warehousing solutions, and ensuring data quality, availability, and security.
3. What is ETL?
Extract, Transform, Load - A data integration process that extracts data from various sources, transforms it into a consistent format, and loads it into a data warehouse.
4. What is a Data Warehouse?
A central repository for storing structured, filtered data that has already been processed for a specific purpose.
5. What is a Data Lake?
A storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.
6. What are the differences between Data Warehouse and Data Lake?
- Structure: Data Warehouse stores structured data; Data Lake stores structured, semi-structured, and unstructured data.
- Processing: Data Warehouse processes data before storage; Data Lake processes data on demand.
- Purpose: Data Warehouse for reporting and analytics; Data Lake for exploration and discovery.
7. What is a Data Pipeline?
A series of steps that move data from source systems to a destination, cleaning and transforming it along the way.
8. What are the common tools used by Data Engineers?
Hadoop, Spark, Kafka, AWS S3, AWS Glue, Azure Data Factory, Google Cloud Dataflow, SQL, Python, Scala, and various database technologies (SQL and NoSQL).
9. What is Apache Spark?
A fast, in-memory data processing engine used for large-scale data processing and analytics.
10. What is Apache Kafka?
A distributed streaming platform that enables real-time data pipelines and streaming applications.
11. What is Hadoop?
A framework for distributed storage and processing of large datasets across clusters of computers.
12. What is the difference between Batch Processing and Stream Processing?
- Batch: Processes data in bulk at scheduled intervals.
- Stream: Processes data continuously in real-time.
13. Explain the concept of schema-on-read and schema-on-write.
- Schema-on-write: Data is validated and transformed before being written into a data warehouse.
- Schema-on-read: Data is stored as is and the schema is applied when the data is read.
14. What are some popular cloud platforms for data engineering?
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
15. What is an API and why is it important in Data Engineering?
Application Programming Interface - Enables different software systems to communicate and exchange data. Crucial for integrating data from various sources.
16. How do you ensure data quality in a data pipeline?
Implementing data validation rules, monitoring data for anomalies, and setting up alerting mechanisms.
17. What is data modeling?
The process of creating a visual representation of data and its relationships within a system.
18. What are some common data modeling techniques?
- Entity-Relationship (ER) modeling
- Dimensional modeling (Star Schema, Snowflake Schema)
19. Explain Star Schema and Snowflake Schema.
- Star Schema: A simple data warehouse schema with a central fact table and surrounding dimension tables.
- Snowflake Schema: An extension of the star schema where dimension tables are further normalized into sub-dimensions.
20. What are some challenges in Data Engineering?
- Handling large volumes of data
- Ensuring data quality and consistency
- Integrating data from diverse sources
- Managing data security and compliance
- Keeping up with evolving technologies
โค๏ธ React for more Interview Resources
1. What is Data Engineering?
Data engineering is the design, construction, testing, and maintenance of systems that collect, manage, and convert raw data into usable information for data scientists and business analysts.
2. What are the key responsibilities of a Data Engineer?
Building and maintaining data pipelines, ETL processes, data warehousing solutions, and ensuring data quality, availability, and security.
3. What is ETL?
Extract, Transform, Load - A data integration process that extracts data from various sources, transforms it into a consistent format, and loads it into a data warehouse.
4. What is a Data Warehouse?
A central repository for storing structured, filtered data that has already been processed for a specific purpose.
5. What is a Data Lake?
A storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.
6. What are the differences between Data Warehouse and Data Lake?
- Structure: Data Warehouse stores structured data; Data Lake stores structured, semi-structured, and unstructured data.
- Processing: Data Warehouse processes data before storage; Data Lake processes data on demand.
- Purpose: Data Warehouse for reporting and analytics; Data Lake for exploration and discovery.
7. What is a Data Pipeline?
A series of steps that move data from source systems to a destination, cleaning and transforming it along the way.
8. What are the common tools used by Data Engineers?
Hadoop, Spark, Kafka, AWS S3, AWS Glue, Azure Data Factory, Google Cloud Dataflow, SQL, Python, Scala, and various database technologies (SQL and NoSQL).
9. What is Apache Spark?
A fast, in-memory data processing engine used for large-scale data processing and analytics.
10. What is Apache Kafka?
A distributed streaming platform that enables real-time data pipelines and streaming applications.
11. What is Hadoop?
A framework for distributed storage and processing of large datasets across clusters of computers.
12. What is the difference between Batch Processing and Stream Processing?
- Batch: Processes data in bulk at scheduled intervals.
- Stream: Processes data continuously in real-time.
13. Explain the concept of schema-on-read and schema-on-write.
- Schema-on-write: Data is validated and transformed before being written into a data warehouse.
- Schema-on-read: Data is stored as is and the schema is applied when the data is read.
14. What are some popular cloud platforms for data engineering?
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
15. What is an API and why is it important in Data Engineering?
Application Programming Interface - Enables different software systems to communicate and exchange data. Crucial for integrating data from various sources.
16. How do you ensure data quality in a data pipeline?
Implementing data validation rules, monitoring data for anomalies, and setting up alerting mechanisms.
17. What is data modeling?
The process of creating a visual representation of data and its relationships within a system.
18. What are some common data modeling techniques?
- Entity-Relationship (ER) modeling
- Dimensional modeling (Star Schema, Snowflake Schema)
19. Explain Star Schema and Snowflake Schema.
- Star Schema: A simple data warehouse schema with a central fact table and surrounding dimension tables.
- Snowflake Schema: An extension of the star schema where dimension tables are further normalized into sub-dimensions.
20. What are some challenges in Data Engineering?
- Handling large volumes of data
- Ensuring data quality and consistency
- Integrating data from diverse sources
- Managing data security and compliance
- Keeping up with evolving technologies
โค๏ธ React for more Interview Resources
โค9
Prompt Engineering in itself does not warrant a separate job.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts ๐ . Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts ๐ . Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
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