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๐Ÿฒ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐˜๐—ต๐—ฒ ๐— ๐—ผ๐˜€๐˜ ๐—œ๐—ป-๐——๐—ฒ๐—บ๐—ฎ๐—ป๐—ฑ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€๐Ÿ˜

๐Ÿš€ Want to future-proof your career without spending a single rupee?๐Ÿ’ต

These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ€” from Data Analytics to Machine Learning๐Ÿ“Š๐Ÿง‘โ€๐Ÿ’ป

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๐Ÿš€๐—ง๐—ผ๐—ฝ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ-๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

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โœ… Future You Will Thank You!
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๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—”๐—ง๐—” ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ (๐—ช๐—ถ๐˜๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ)๐Ÿ˜

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Data Engineering Tools
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Interview questions for Data Architect and Data Engineer positions:

Design and Architecture


1.โ  โ Design a data warehouse architecture for a retail company.
2.โ  โ How would you approach data governance in a large organization?
3.โ  โ Describe a data lake architecture and its benefits.
4.โ  โ How do you ensure data quality and integrity in a data warehouse?
5.โ  โ Design a data mart for a specific business domain (e.g., finance, healthcare).


Data Modeling and Database Design


1.โ  โ Explain the differences between relational and NoSQL databases.
2.โ  โ Design a database schema for a specific use case (e.g., e-commerce, social media).
3.โ  โ How do you approach data normalization and denormalization?
4.โ  โ Describe entity-relationship modeling and its importance.
5.โ  โ How do you optimize database performance?


Data Security and Compliance


1.โ  โ Describe data encryption methods and their applications.
2.โ  โ How do you ensure data privacy and confidentiality?
3.โ  โ Explain GDPR and its implications on data architecture.
4.โ  โ Describe access control mechanisms for data systems.
5.โ  โ How do you handle data breaches and incidents?


Data Engineer Interview Questions!!


Data Processing and Pipelines


1.โ  โ Explain the concepts of batch processing and stream processing.
2.โ  โ Design a data pipeline using Apache Beam or Apache Spark.
3.โ  โ How do you handle data integration from multiple sources?
4.โ  โ Describe data transformation techniques (e.g., ETL, ELT).
5.โ  โ How do you optimize data processing performance?


Big Data Technologies


1.โ  โ Explain Hadoop ecosystem and its components.
2.โ  โ Describe Spark RDD, DataFrame, and Dataset.
3.โ  โ How do you use NoSQL databases (e.g., MongoDB, Cassandra)?
4.โ  โ Explain cloud-based big data platforms (e.g., AWS, GCP, Azure).
5.โ  โ Describe containerization using Docker.


Data Storage and Retrieval


1.โ  โ Explain data warehousing concepts (e.g., fact tables, dimension tables).
2.โ  โ Describe column-store and row-store databases.
3.โ  โ How do you optimize data storage for query performance?
4.โ  โ Explain data caching mechanisms.
5.โ  โ Describe graph databases and their applications.


Behavioral and Soft Skills


1.โ  โ Can you describe a project you led and the challenges you faced?
2.โ  โ How do you collaborate with cross-functional teams?
3.โ  โ Explain your experience with Agile development methodologies.
4.โ  โ Describe your approach to troubleshooting complex data issues.
5.โ  โ How do you stay up-to-date with industry trends and technologies?


Additional Tips


1.โ  โ Review the company's technology stack and be prepared to discuss relevant tools and technologies.
2.โ  โ Practice whiteboarding exercises to improve your design and problem-solving skills.
3.โ  โ Prepare examples of your experience with data architecture and engineering concepts.
4.โ  โ Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders.
5.โ  โ Show enthusiasm and passion for data architecture and engineering.
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๐Ÿณ ๐— ๐˜‚๐˜€๐˜-๐—ž๐—ป๐—ผ๐˜„ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€ ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐˜€๐—ฝ๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐Ÿ˜

If youโ€™re serious about becoming a data analyst, thereโ€™s no skipping SQL. Itโ€™s not just another technical skill โ€” itโ€™s the core language for data analytics.๐Ÿ“Š

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

https://pdlink.in/44S3Xi5

This guide covers 7 key SQL concepts that every beginner must learnโœ…๏ธ
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ETL vs ELT โ€“ Explained Using Apple Juice analogy! ๐ŸŽ๐Ÿงƒ

We often hear about ETL and ELT in the data world โ€” but how do they actually apply in tools like Excel and Power BI?

Letโ€™s break it down with a simple and relatable analogy ๐Ÿ‘‡

โœ… ETL (Extract โ†’ Transform โ†’ Load)

๐Ÿงƒ First you make the juice, then you deliver it

โžก๏ธ Apples โ†’ Juice โ†’ Truck

๐Ÿ”น In Power BI / Excel:

You clean and transform the data in Power Query
Then load the final data into your report or sheet
๐Ÿ’ก Thatโ€™s ETL โ€“ transformation happens before loading



โœ… ELT (Extract โ†’ Load โ†’ Transform)

๐Ÿ First you deliver the apples, and make juice later

โžก๏ธ Apples โ†’ Truck โ†’ Juice

๐Ÿ”น In Power BI / Excel:

You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
๐Ÿ’ก Thatโ€™s ELT โ€“ transformation happens after loading
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๐—”๐—ฐ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ๐Ÿฌ ๐— ๐—ผ๐˜€๐˜-๐—”๐˜€๐—ธ๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€! ๐Ÿ˜

๐Ÿคฆ๐Ÿปโ€โ™€๏ธStruggling with SQL interviews? Not anymore!๐Ÿ“

SQL interviews can be challenging, but preparation is the key to success. Whether youโ€™re aiming for a data analytics role or just brushing up, this resource has got your back!๐ŸŽŠ

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

https://pdlink.in/4olhd6z

Letโ€™s crack that interview together!โœ…๏ธ
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Understand the power of Data Lakehouse Architecture for ๐—™๐—ฅ๐—˜๐—˜ here...


๐Ÿšจ๐—ข๐—น๐—ฑ ๐˜„๐—ฎ๐˜†
โ€ข Complicated ETL processes for data integration.
โ€ข Silos of data storage, separating structured and unstructured data.
โ€ข High data storage and management costs in traditional warehouses.
โ€ข Limited scalability and delayed access to real-time insights.

โœ…๐—ก๐—ฒ๐˜„ ๐—ช๐—ฎ๐˜†
โ€ข Streamlined data ingestion and processing with integrated SQL capabilities.
โ€ข Unified storage layer accommodating both structured and unstructured data.
โ€ข Cost-effective storage by combining benefits of data lakes and warehouses.
โ€ข Real-time analytics and high-performance queries with SQL integration.

The shift?

Unified Analytics and Real-Time Insights > Siloed and Delayed Data Processing

Leveraging SQL to manage data in a data lakehouse architecture transforms how businesses handle data.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best ๐Ÿ‘๐Ÿ‘
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๐ŸŒˆ Greetings from PVR CLOUD TECH!

๐Ÿ“” Course : Azure Data Engineering


๐Ÿ—“ Date: 4th August 2025

๐Ÿ•— Time: 9 PM to 10 PM IST | Monday

Duration: 3 Months

๐Ÿ€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ ๐—–๐—ผ๐—ป๐˜๐—ฒ๐—ป๐˜:
https://lnkd.in/gX55prky

๐Ÿ€ ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—ต๐—ฒ๐—ฟ๐—ฒ:
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๐Ÿ€ ๐—๐—ผ๐—ถ๐—ป ๐—ช๐—ต๐—ฎ๐˜๐˜€๐—”๐—ฝ๐—ฝ ๐—š๐—ฟ๐—ผ๐˜‚๐—ฝ:
https://lnkd.in/gRDKcb-y

๐Ÿ€ ๐—ช๐—ต๐—ฎ๐˜๐˜€๐—ฎ๐—ฝ๐—ฝ ๐—–๐—ต๐—ฎ๐—ป๐—ป๐—ฒ๐—น:
https://lnkd.in/gA6jRBYN

Thanks,
PVR Cloud Tech
๐Ÿ“ฑ +91-9346060794
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