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Data Engineers

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๐Ÿ“ˆ Telegram kanali Data Engineers analitikasi

Data Engineers (@sql_engineer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 10 351 obunachidan iborat bo'lib, Taสผlim toifasida 19 412-o'rinni va Hindiston mintaqasida 40 270-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 10 351 obunachiga ega boโ€˜ldi.

06 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 234 ga, soโ€˜nggi 24 soatda esa 8 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 12.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.43% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 258 marta koโ€˜riladi; birinchi sutkada odatda 252 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 5 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent sql, learning, analytic, engineer, link:- kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œFree Data Engineering Ebooks & Coursesโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

10 351
Obunachilar
+824 soatlar
+457 kunlar
+23430 kunlar
Postlar arxiv
10 Data Engineering architectures asked in Interviews. 1. Hadoop 2. Hive 3. Hbase 4. Kafka 5. Spark 6. Airflow 7. Bigquery 8. Snowflake 9. Databricks 10. MongoDB Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L All the best ๐Ÿ‘๐Ÿ‘

Repost from Generative AI
๐—๐—ฃ ๐— ๐—ผ๐—ฟ๐—ด๐—ฎ๐—ป ๐—™๐—ฅ๐—˜๐—˜ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐˜€๐Ÿ˜ JPMorgan offers free virtual internships to
๐—๐—ฃ ๐— ๐—ผ๐—ฟ๐—ด๐—ฎ๐—ป ๐—™๐—ฅ๐—˜๐—˜ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐˜€๐Ÿ˜ JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.  - Software Engineering Internship - Investment Banking Program - Quantitative Research Internship   ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:-  https://pdlink.in/4gHGofl Enroll For FREE & Get Certified ๐ŸŽ“

Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. SQL Interview Resources: https://topmate.io/analyst/864764 ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Python Interview Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C Join for more: https://t.me/datasciencefun ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐—”๐—œ & ๐— ๐—Ÿ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜ Qualcommโ€”a global tech giant offering completely FREE cours
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10 Data Engineering Projects to build your portfolio. 1. Olympic Data Analytics using Azure https://lnkd.in/gHNyz_Bg 2. Uber Data Analytics using GCP. https://lnkd.in/gqE-Y4HS 3. Stock Market Real-time Data Analysis using Kafka https://lnkd.in/gknh7ZEr 4. Twitter Data Pipeline using Airflow https://lnkd.in/g7YPnH7G 5. Smart City End to End project using AWS https://lnkd.in/gh2eWF66 6. Realtime Data Streaming using spark and Kafka https://lnkd.in/gjH2efgz 7. Zillow Data Analytics - Python, ETL https://lnkd.in/gvEVZHPR 8. End to end Azure Project https://lnkd.in/gCVZtNB5 9. End to end project using snowlake https://lnkd.in/g96n6NbA 10. Data pipeline using Data Fusion https://lnkd.in/gR5pkeRw Data Engineering Interview Preparation Resources: ๐Ÿ‘‡ https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C Hope this helps you ๐Ÿ˜Š If you've read so far, do LIKE the post๐Ÿ‘

๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐˜ƒ๐—ฒ๐—น ๐—จ๐—ฝ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to build your tech career
๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐˜ƒ๐—ฒ๐—น ๐—จ๐—ฝ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to build your tech career without breaking the bank?๐Ÿ’ฐ These 3 completely free courses are all you need to begin your journey in programming and data analysis๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3EtHnBI Learn at your own pace, sharpen your skills, and showcase your progress on LinkedIn or your resume. Letโ€™s dive in!โœ…๏ธ

Complete Data Engineering Roadmap to keep yourself in the hunt in job market. 1. I will Learn SQL --variables, data types, Aggregate functions -- Various joins, data analysis -- data wrangling, operators like(union, intersect etc.) --Advanced SQL(Regex, Having, PIVOT) --Windowing functions, CTE --finally performance optimizations. 2. I will learn Python... -- Basic functions, constructors, Lists, Tuples, Dictionaries -- Loops (IF, When, FOR), functional programming -- Libraries like(Pandas, Numpy, scikit-learn etc) 3. Learn distributed computing... --Hadoop versions/hadoop architecture --fault tolerance in hadoop --Read/understand about Mapreduce processing. --learn optimizations used in mapreduce etc. 4. Learn data ingestion tools... --Learn Sqoop/ Kafka/NIFi --Understand their functionality and job running mechanism. 5. i ll Learn data processing/NOSQL.... --Spark architecture/ RDD/Dataframes/datasets. --lazy evaluation, DAGs/ Lineage graph/optimization techniques --YARN utilization/ spark streaming etc. 6. Learn data warehousing..... --Understand how HIve store and process the data --different File formats/ compression Techniques. --partitioning/ Bucketing. --different UDF's available in Hive. --SCD concepts. --Ex Hbase. cassandra 7. Learn job Orchestration... --Learn Airflow/Oozie --learn about workflow/ CRON etc. 8. Learn Cloud Computing.... --Learn Azure/AWS/ GCP. --understand the significance of Cloud in #dataengineering --Learn Azure synapse/Redshift/Big query --Learn Ingestion tools/pipeline tools like ADF etc. 9. Learn basics of CI/ CD and Linux commands.... --Read about Kubernetes/Docker. And how crucial they are in data. --Learn about basic commands like copy data/export in Linux. Data Engineering Interview Preparation Resources: ๐Ÿ‘‡ https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C Like if you need similar content ๐Ÿ˜„๐Ÿ‘ Hope this helps you ๐Ÿ˜Š

๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐—•๐—ฒ๐˜€๐˜ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐—ง๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—๐—ฎ๐˜ƒ๐—ฎ ๐—˜๐—ฎ๐˜€๐—ถ๐—น๐˜† ๐Ÿ˜ Level up your Java skills without getting ov
๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐—•๐—ฒ๐˜€๐˜ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐—ง๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—๐—ฎ๐˜ƒ๐—ฎ ๐—˜๐—ฎ๐˜€๐—ถ๐—น๐˜† ๐Ÿ˜ Level up your Java skills without getting overwhelmed All of them are absolutely free, designed by experienced educators and top tech creators ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- https://pdlink.in/3RvvP49 Enroll For FREE & Get Certified ๐ŸŽ“

๐—ช๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—ฏ๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐——๐—ฎ๐˜๐—ฎ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ? Here is a complete week-by-week roadmap that can help ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿญ: Learn programming - Python for data manipulation, and Java for big data frameworks. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฎ-๐Ÿฏ: Understand database concepts and databases like MongoDB. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฐ-๐Ÿฒ: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow) ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฒ-๐Ÿด: Go for advanced topics like cloud computing and containerization (Docker). ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿต-๐Ÿญ๐Ÿฌ: Participate in Kaggle competitions, build projects and develop communication skills. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿญ๐Ÿญ: Create your resume, optimize your profiles on job portals, seek referrals and apply. Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best ๐Ÿ‘๐Ÿ‘

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Planning for Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. ๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best ๐Ÿ‘๐Ÿ‘

Repost from Generative AI
๐Ÿฏ ๐—™๐—ฅ๐—˜๐—˜ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—”๐—œ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Taught by industry leaders (like M
๐Ÿฏ ๐—™๐—ฅ๐—˜๐—˜ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—”๐—œ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Taught by industry leaders (like Microsoft - 100% online and beginner-friendly * Generative AI for Data Analysts * Generative AI: Enhance Your Data Analytics Career * Microsoft Generative AI for Data Analysis  ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- https://pdlink.in/3R7asWB Enroll Now & Get Certified ๐ŸŽ“

FREE RESOURCES TO LEARN DATA ENGINEERING ๐Ÿ‘‡๐Ÿ‘‡ Big Data and Hadoop Essentials free course https://bit.ly/3rLxbul Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE [4.6 stars out of 5] https://bit.ly/3fGRjLu Understanding Data Engineering from Datacamp https://clnk.in/soLY Data Engineering Free Books https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf Big Data of Data Engineering Free book https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf The Data Engineerโ€™s Guide to Apache Spark https://t.me/datasciencefun/783?single Data Engineering with Python https://t.me/pythondevelopersindia/343 Data Engineering Projects - 1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge 2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J 3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq 4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3 5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR 6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD 7. YouTube Data Analysis     (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF 8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY 9. Sentiment analysis Twitter:     Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—™๐—ฟ๐—ผ๐—บ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜, ๐—”๐—ช๐—ฆ, ๐—œ๐—•๐— , ๐—–๐—ถ๐˜€๐—ฐ๐—ผ, ๐—ฎ๐—ป๏ฟฝ
๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—™๐—ฟ๐—ผ๐—บ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜, ๐—”๐—ช๐—ฆ, ๐—œ๐—•๐— , ๐—–๐—ถ๐˜€๐—ฐ๐—ผ, ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐˜๐—ฎ๐—ป๐—ณ๐—ผ๐—ฟ๐—ฑ. ๐Ÿ˜ - Python - Artificial Intelligence, - Cybersecurity - Cloud Computing, and - Machine Learning ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- https://pdlink.in/3E2wYNr Enroll For FREE & Get Certified ๐ŸŽ“

Complete topics & subtopics of #SQL for Data Engineer role:- ๐Ÿญ. ๐—•๐—ฎ๐˜€๐—ถ๐—ฐ ๐—ฆ๐—ค๐—Ÿ ๐—ฆ๐˜†๐—ป๐˜๐—ฎ๐˜…: SQL keywords Data types Operators SQL statements (SELECT, INSERT, UPDATE, DELETE) ๐Ÿฎ. ๐——๐—ฎ๐˜๐—ฎ ๐——๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐˜๐—ถ๐—ผ๐—ป ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ (๐——๐——๐—Ÿ): CREATE TABLE ALTER TABLE DROP TABLE Truncate table ๐Ÿฏ. ๐——๐—ฎ๐˜๐—ฎ ๐— ๐—ฎ๐—ป๐—ถ๐—ฝ๐˜‚๐—น๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ (๐——๐— ๐—Ÿ): SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs) INSERT statement UPDATE statement DELETE statement ๐Ÿฐ. ๐—”๐—ด๐—ด๐—ฟ๐—ฒ๐—ด๐—ฎ๐˜๐—ฒ ๐—™๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€: SUM, AVG, COUNT, MIN, MAX GROUP BY clause HAVING clause ๐Ÿฑ. ๐——๐—ฎ๐˜๐—ฎ ๐—–๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐˜๐˜€: Primary Key Foreign Key Unique NOT NULL CHECK ๐Ÿฒ. ๐—๐—ผ๐—ถ๐—ป๐˜€: INNER JOIN LEFT JOIN RIGHT JOIN FULL OUTER JOIN Self Join Cross Join ๐Ÿณ. ๐—ฆ๐˜‚๐—ฏ๐—พ๐˜‚๐—ฒ๐—ฟ๐—ถ๐—ฒ๐˜€: Types of subqueries (scalar, column, row, table) Nested subqueries Correlated subqueries ๐Ÿด. ๐—”๐—ฑ๐˜ƒ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—ฑ ๐—ฆ๐—ค๐—Ÿ ๐—™๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€: String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER) Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD) Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD) Conditional functions (CASE, COALESCE, NULLIF) ๐Ÿต. ๐—ฉ๐—ถ๐—ฒ๐˜„๐˜€: Creating views Modifying views Dropping views ๐Ÿญ๐Ÿฌ. ๐—œ๐—ป๐—ฑ๐—ฒ๐˜…๐—ฒ๐˜€: Creating indexes Using indexes for query optimization ๐Ÿญ๐Ÿญ. ๐—ง๐—ฟ๐—ฎ๐—ป๐˜€๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€: ACID properties Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT) Transaction isolation levels ๐Ÿญ๐Ÿฎ. ๐——๐—ฎ๐˜๐—ฎ ๐—œ๐—ป๐˜๐—ฒ๐—ด๐—ฟ๐—ถ๐˜๐˜† ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†: Data integrity constraints (referential integrity, entity integrity) GRANT and REVOKE statements (granting and revoking permissions) Database security best practices ๐Ÿญ๐Ÿฏ. ๐—ฆ๐˜๐—ผ๐—ฟ๐—ฒ๐—ฑ ๐—ฃ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐—ฑ๐˜‚๐—ฟ๐—ฒ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—™๐˜‚๐—ป๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€: Creating stored procedures Executing stored procedures Creating functions Using functions in queries ๐Ÿญ๐Ÿฐ. ๐—ฃ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—ป๐—ฐ๐—ฒ ๐—ข๐—ฝ๐˜๐—ถ๐—บ๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป: Query optimization techniques (using indexes, optimizing joins, reducing subqueries) Performance tuning best practices ๐Ÿญ๐Ÿฑ. ๐—”๐—ฑ๐˜ƒ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—ฑ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€: Recursive queries Pivot and unpivot operations Window functions (Row_number, rank, dense_rank, lead & lag) CTEs (Common Table Expressions) Dynamic SQL Here you can find quick SQL Revision Notes๐Ÿ‘‡ https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C Like for more Hope it helps :)

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Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career. ๐Ÿ“ŠIntroduction to Data Engineering โœ…Overview of Data Engineering & its importance โœ…Key responsibilities & skills of a Data Engineer โœ…Difference between Data Engineer, Data Scientist & Data Analyst โœ…Data Engineering tools & technologies ๐Ÿ“ŠProgramming for Data Engineering โœ…Python โœ…SQL โœ…Java/Scala โœ…Shell scripting ๐Ÿ“ŠDatabase System & Data Modeling โœ…Relational Databases: design, normalization & indexing โœ…NoSQL Databases: key-value stores, document stores, column-family stores & graph database โœ…Data Modeling: conceptual, logical & physical data model โœ…Database Management Systems & their administration ๐Ÿ“ŠData Warehousing and ETL Processes โœ…Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema โœ…ETL: designing, developing & managing ETL processe โœ…Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue โœ…Data lakes & modern data warehousing solution ๐Ÿ“ŠBig Data Technologies โœ…Hadoop ecosystem: HDFS, MapReduce, YARN โœ…Apache Spark: core concepts, RDDs, DataFrames & SparkSQL โœ…Kafka and real-time data processing โœ…Data storage solutions: HBase, Cassandra, Amazon S3 ๐Ÿ“ŠCloud Platforms & Services โœ…Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure โœ…Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake โœ…Data storage & management on the cloud โœ…Serverless computing & its applications in data engineering ๐Ÿ“ŠData Pipeline Orchestration โœ…Workflow orchestration: Apache Airflow, Luigi, Prefect โœ…Building & scheduling data pipelines โœ…Monitoring & troubleshooting data pipelines โœ…Ensuring data quality & consistency ๐Ÿ“ŠData Integration & API Development โœ…Data integration techniques & best practices โœ…API development: RESTful APIs, GraphQL โœ…Tools for API development: Flask, FastAPI, Django โœ…Consuming APIs & data from external sources ๐Ÿ“ŠData Governance & Security โœ…Data governance frameworks & policies โœ…Data security best practices โœ…Compliance with data protection regulations โœ…Implementing data auditing & lineage ๐Ÿ“ŠPerformance Optimization & Troubleshooting โœ…Query optimization techniques โœ…Database tuning & indexing โœ…Managing & scaling data infrastructure โœ…Troubleshooting common data engineering issues ๐Ÿ“ŠProject Management & Collaboration โœ…Agile methodologies & best practices โœ…Version control systems: Git & GitHub โœ…Collaboration tools: Jira, Confluence, Slack โœ…Documentation & reporting Resources for Data Engineering 1๏ธโƒฃPython: https://t.me/pythonanalyst 2๏ธโƒฃSQL: https://t.me/sqlanalyst 3๏ธโƒฃExcel: https://t.me/excel_analyst 4๏ธโƒฃFree DE Courses: https://t.me/free4unow_backup/569 Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best ๐Ÿ‘๐Ÿ‘

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5 most asked SQL Interview Questions for Data Engineer jobs. ๐Ÿญ. ๐—™๐—ถ๐—ป๐—ฑ ๐˜๐—ต๐—ฒ ๐—ฆ๐—ฒ๐—ฐ๐—ผ๐—ป๐—ฑ ๐—›๐—ถ๐—ด๐—ต๐—ฒ๐˜€๐˜ ๐—ฆ๐—ฎ๐—น๐—ฎ๐—ฟ๐˜† ๐—ถ๐—ป ๐—ฎ ๐—ง๐—ฎ๐—ฏ๐—น๐—ฒ SELECT MAX(salary) AS SecondHighestSalary FROM Employee WHERE salary < (SELECT MAX(salary) FROM Employee); ๐Ÿฎ . ๐—™๐—ถ๐—ป๐—ฑ ๐—ผ๐˜‚๐˜ ๐—ฒ๐—บ๐—ฝ๐—น๐—ผ๐˜†๐—ฒ๐—ฒ๐˜€ ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—บ๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฎ๐—ป ๐˜๐—ต๐—ฒ๐—ถ๐—ฟ ๐—บ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—ฟ๐˜€ SELECT e2.name as Employee FROM employee e1 INNER JOIN employee e2 ON e1.id = e2.managerID WHERE e1.salary < e2.salary ๐Ÿฏ. ๐—™๐—ถ๐—ป๐—ฑ ๐—ฐ๐˜‚๐˜€๐˜๐—ผ๐—บ๐—ฒ๐—ฟ๐˜€ ๐˜„๐—ต๐—ผ ๐—ป๐—ฒ๐˜ƒ๐—ฒ๐—ฟ ๐—ผ๐—ฟ๐—ฑ๐—ฒ๐—ฟ SELECT name as Customers FROM Customers WHERE id not in ( SELECT customerId FROM Orders); ๐Ÿฐ. ๐——๐—ฒ๐—น๐—ฒ๐˜๐—ฒ ๐—ฑ๐˜‚๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ ๐—ฒ๐—บ๐—ฎ๐—ถ๐—น๐˜€ DELETE p1 FROM Person p1, Person p2 WHERE p1.Email = p2.Email AND p1.Id > p2.Id ๐Ÿฑ. ๐—–๐—ผ๐˜‚๐—ป๐˜ ๐˜๐—ต๐—ฒ ๐—ป๐˜‚๐—บ๐—ฏ๐—ฒ๐—ฟ ๐—ผ๐—ณ ๐—ผ๐—ฟ๐—ฑ๐—ฒ๐—ฟ๐˜€ ๐—ฝ๐—น๐—ฎ๐—ฐ๐—ฒ๐—ฑ ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—ฝ๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ผ๐˜‚๐˜€ ๐˜†๐—ฒ๐—ฎ๐—ฟ ๐—ฎ๐—ป๐—ฑ ๐—บ๐—ผ๐—ป๐˜๐—ต. SELECT COUNT(*) AS order_count FROM orders WHERE EXTRACT(YEAR_MONTH FROM order_date) = EXTRACT(YEAR_MONTH FROM CURDATE() - INTERVAL 1 MONTH); ๐Ÿ’ก Note: SQL interview questions vary widely based on the specific role and company. So you also need to practice questions your target companies ask. Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C All the best ๐Ÿ‘๐Ÿ‘