๐ฐ Learning the Skills ๐ฐ
Telegram Channel: https://t.me/zerotrusthackers
CS 642: Intro to Computer Security (http://pages.cs.wisc.edu/~ace/cs642-spring-2016.html)
academic content, full semester course, includes assigned readings, homework and github refs for exploit examples. NO VIDEO LECTURES.
CyberSec WTF (https://cybersecurity.wtf/)
CyberSec WTF Web Hacking Challenges from Bounty write-ups
Cybrary (https://www.cybrary.it/)
coursera style website, lots of user-contributed content, account required, content can be filtered by experience level
Free Cyber Security Training (https://www.samsclass.info/)
Academic content, 8 full courses with videos from a quirky instructor sam, links to research, defcon materials and other recommended training/learning
Hak5 (https://www.hak5.org/)
podcast-style videos covering various topics, has a forum, "metasploit-minute" video series could be useful
Hopper's Roppers Security Training (https://hoppersroppers.org/training.html)
Four free self-paced courses on Computing Fundamentals, Security, Capture the Flags, and a Practical Skills Bootcamp that help beginners build a strong base of foundational knowledge. Designed to prepare for students for whatever they need to learn next.
Learning Exploitation with Offensive Computer Security 2.0 (http://howto.hackallthethings.com/2016/07/learning-exploitation-with-offensive.html)
blog-style instruction, includes: slides, videos, homework, discussion. No login required.
Mind Maps (http://www.amanhardikar.com/mindmaps.html)
Information Security related Mind Maps
MIT OCW 6.858 Computer Systems Security (https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-858-computer-systems-security-fall-2014/)
academic content, well organized, full-semester course, includes assigned readings, lectures, videos, required lab files.
OffensiveComputerSecurity (https://www.cs.fsu.edu/~redwood/OffensiveComputerSecurity/lectures.html)
academic content, full semester course including 27 lecture videos with slides and assign readings
OWASP top 10 web security risks (https://www.owasp.org/index.php/Category:OWASP_Top_Ten_Project)
free courseware, requires account
SecurityTube (http://www.securitytube.net/)
tube-styled content, "megaprimer" videos covering various topics, no readable content on site.
Seed Labs (http://www.cis.syr.edu/~wedu/seed/labs.html)
academic content, well organized, featuring lab videos, tasks, needed code files, and recommended readings
TryHackMe (https://tryhackme.com/)
Designed prebuilt challenges which include virtual machines (VM) hosted in the cloud ready to be deployed
WhatsApp Channel:
https://whatsapp.com/channel/0029VaxVv551iUxRku094918
Telegram Channel: https://t.me/zerotrusthackers
CS 642: Intro to Computer Security (http://pages.cs.wisc.edu/~ace/cs642-spring-2016.html)
academic content, full semester course, includes assigned readings, homework and github refs for exploit examples. NO VIDEO LECTURES.
CyberSec WTF (https://cybersecurity.wtf/)
CyberSec WTF Web Hacking Challenges from Bounty write-ups
Cybrary (https://www.cybrary.it/)
coursera style website, lots of user-contributed content, account required, content can be filtered by experience level
Free Cyber Security Training (https://www.samsclass.info/)
Academic content, 8 full courses with videos from a quirky instructor sam, links to research, defcon materials and other recommended training/learning
Hak5 (https://www.hak5.org/)
podcast-style videos covering various topics, has a forum, "metasploit-minute" video series could be useful
Hopper's Roppers Security Training (https://hoppersroppers.org/training.html)
Four free self-paced courses on Computing Fundamentals, Security, Capture the Flags, and a Practical Skills Bootcamp that help beginners build a strong base of foundational knowledge. Designed to prepare for students for whatever they need to learn next.
Learning Exploitation with Offensive Computer Security 2.0 (http://howto.hackallthethings.com/2016/07/learning-exploitation-with-offensive.html)
blog-style instruction, includes: slides, videos, homework, discussion. No login required.
Mind Maps (http://www.amanhardikar.com/mindmaps.html)
Information Security related Mind Maps
MIT OCW 6.858 Computer Systems Security (https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-858-computer-systems-security-fall-2014/)
academic content, well organized, full-semester course, includes assigned readings, lectures, videos, required lab files.
OffensiveComputerSecurity (https://www.cs.fsu.edu/~redwood/OffensiveComputerSecurity/lectures.html)
academic content, full semester course including 27 lecture videos with slides and assign readings
OWASP top 10 web security risks (https://www.owasp.org/index.php/Category:OWASP_Top_Ten_Project)
free courseware, requires account
SecurityTube (http://www.securitytube.net/)
tube-styled content, "megaprimer" videos covering various topics, no readable content on site.
Seed Labs (http://www.cis.syr.edu/~wedu/seed/labs.html)
academic content, well organized, featuring lab videos, tasks, needed code files, and recommended readings
TryHackMe (https://tryhackme.com/)
Designed prebuilt challenges which include virtual machines (VM) hosted in the cloud ready to be deployed
WhatsApp Channel:
https://whatsapp.com/channel/0029VaxVv551iUxRku094918
Top 50 OOPS Interview Preparation Course ๐ปโ
Learn DSA Visually
Links to Sites to help you learn Data Structures and Algorithms Visually:
Data Structure Visualisations :
https://www.cs.usfca.edu/~galles/visualization/Algorithms.html
Visualgo:
https://visualgo.net/en
Visualizing Algorithms by Mike Bostock :
https://bost.ocks.org/mike/algorithms/
DSA Interview Questions: https://t.me/techpsyche/545
All the best ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Links to Sites to help you learn Data Structures and Algorithms Visually:
Data Structure Visualisations :
https://www.cs.usfca.edu/~galles/visualization/Algorithms.html
Visualgo:
https://visualgo.net/en
Visualizing Algorithms by Mike Bostock :
https://bost.ocks.org/mike/algorithms/
DSA Interview Questions: https://t.me/techpsyche/545
All the best ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
All the best ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
All the best ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
If I were to start Computer Science in 2025 ๐ซ๐
- Harvard
- Stanford
- MIT
- IBM
- Telegram
- Microsoft
- Google
โฏ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
โฏ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/
โฏ Python
http://cs50.harvard.edu/python/2022/
https://t.me/pythonresourcestp
โฏ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://t.me/sqlresourcestp
โฏ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://t.me/techpsyche/544
โฏ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://t.me/javaresourcestp
โฏ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://t.me/javascriptresourcestp
โฏ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
โฏ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
โฏ Data Science
cognitiveclass.ai/courses/data-science-101
https://t.me/datascienceresourcestp
โฏ Machine Learning
http://developers.google.com/machine-learning/crash-course
https://t.me/mlresourcestp
โฏ Deep Learning
introtodeeplearning.com
โฏ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05
https://t.me/webdevresourcestp
โฏ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/
โฏ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers
Learn DSA๐
https://t.me/techpsyche/544
Cyber Security๐
https://t.me/zerotrusthackers/41
100+ YouTube channels๐
https://t.me/techpsyche/513
Make sure to scroll through the above messages ๐ you will definitely find more interesting things ๐ค
ENJOY LEARNING ๐๐
WhatsApp Channel๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
- Harvard
- Stanford
- MIT
- IBM
- Telegram
- Microsoft
โฏ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
โฏ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/
โฏ Python
http://cs50.harvard.edu/python/2022/
https://t.me/pythonresourcestp
โฏ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://t.me/sqlresourcestp
โฏ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://t.me/techpsyche/544
โฏ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://t.me/javaresourcestp
โฏ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://t.me/javascriptresourcestp
โฏ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
โฏ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
โฏ Data Science
cognitiveclass.ai/courses/data-science-101
https://t.me/datascienceresourcestp
โฏ Machine Learning
http://developers.google.com/machine-learning/crash-course
https://t.me/mlresourcestp
โฏ Deep Learning
introtodeeplearning.com
โฏ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05
https://t.me/webdevresourcestp
โฏ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/
โฏ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers
Learn DSA๐
https://t.me/techpsyche/544
Cyber Security๐
https://t.me/zerotrusthackers/41
100+ YouTube channels๐
https://t.me/techpsyche/513
Make sure to scroll through the above messages ๐ you will definitely find more interesting things ๐ค
ENJOY LEARNING ๐๐
WhatsApp Channel๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
โค1
Forwarded from SQL Resources TP
๐๐ซ๐ ๐ฒ๐จ๐ฎ ๐ฉ๐ซ๐๐ฉ๐๐ซ๐ข๐ง๐ ๐๐จ๐ซ ๐๐๐ ๐ข๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ? ๐
Donโt miss these top SQL questions recently asked by leading companies!
Top 45 SQL Interview Questions & Answers
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Start practicing today and stand out from the competition! ๐ป
Donโt miss these top SQL questions recently asked by leading companies!
Top 45 SQL Interview Questions & Answers
๐๐ข๐ง๐ค๐:- https://bit.ly/4iH0Z3K
Start practicing today and stand out from the competition! ๐ป
Remote Full-Stack Developer Job at OnTheGoSystems
* Strong skills in PHP, JS
* React and Redux experience
* Expertise in databases
* 100% Remote
Apply Here:
https://kenyatrends.co.ke/wzic
* Strong skills in PHP, JS
* React and Redux experience
* Expertise in databases
* 100% Remote
Apply Here:
https://kenyatrends.co.ke/wzic
2 Remote Tech Jobs at Zerion
1. Senior BackEnd Engineer
2. Senior Product Designer
Apply Here:
https://kenyatrends.co.ke/c4pc
1. Senior BackEnd Engineer
2. Senior Product Designer
Apply Here:
https://kenyatrends.co.ke/c4pc
7 Remote Tech Jobs at Speechify
1. Senior Data Engineer - AI Team
2. Software Engineer, Web Core Product & Chrome Extension
3. Applied AI Engineer & Researcher
4. Software Engineer, Data Infrastructure & Acquisition
5. Software Engineer, iOS Core Product
6. Software Engineer, Platform
7. Software Engineer, Studio
Apply Here:
https://kenyatrends.co.ke/p3c9
1. Senior Data Engineer - AI Team
2. Software Engineer, Web Core Product & Chrome Extension
3. Applied AI Engineer & Researcher
4. Software Engineer, Data Infrastructure & Acquisition
5. Software Engineer, iOS Core Product
6. Software Engineer, Platform
7. Software Engineer, Studio
Apply Here:
https://kenyatrends.co.ke/p3c9
Remote Chat Support Agent - Entry Level, No Degree Required Job at NoGigiddy
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Apply Here:
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Learn This Concept to be proficient in PySpark.
๐๐ฎ๐๐ถ๐ฐ๐ ๐ผ๐ณ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- PySpark Architecture
- SparkContext and SparkSession
- RDDs (Resilient Distributed Datasets)
- DataFrames
- Transformations and Actions
- Lazy Evaluation
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ฎ๐๐ฎ๐๐ฟ๐ฎ๐บ๐ฒ๐:
- Creating DataFrames
- Reading Data from CSV, JSON, Parquet
- DataFrame Operations
- Filtering, Selecting, and Aggregating Data
- Joins and Merging DataFrames
- Working with Null Values
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ผ๐น๐๐บ๐ป ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐:
- Defining and Using UDFs (User Defined Functions)
- Column Operations (Select, Rename, Drop)
- Handling Complex Data Types (Array, Map)
- Working with Dates and Timestamps
๐ฃ๐ฎ๐ฟ๐๐ถ๐๐ถ๐ผ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฆ๐ต๐๐ณ๐ณ๐น๐ฒ ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐:
- Understanding Partitions
- Repartitioning and Coalescing
- Managing Shuffle Operations
- Optimizing Partition Sizes for Performance
๐๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฃ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ:
- When to Cache or Persist
- Memory vs Disk Caching
- Checking Storage Levels
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐ช๐ถ๐๐ต ๐ฆ๐ค๐:
- Spark SQL Introduction
- Creating Temp Views
- Running SQL Queries
- Optimizing SQL Queries with Catalyst Optimizer
- Working with Hive Tables in PySpark
๐ช๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐๐ฎ๐๐ฎ ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Data Cleaning and Preparation
- Handling Missing Values
- Data Normalization and Transformation
- Working with Categorical Data
๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Broadcasting Variables
- Accumulators
- PySpark Window Functions
- PySpark with Machine Learning (MLlib)
- Working with Streaming Data (Spark Streaming)
๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ง๐๐ป๐ถ๐ป๐ด ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Understanding Job, Stage, and Task
- Tungsten Execution Engine
- Memory Management and Garbage Collection
- Tuning Parallelism
- Using Spark UI for Performance Monitoring
Data Engineering Interview Preparation Resources
https://t.me/datascienceresourcestp/61
All the best ๐๐
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐๐ฎ๐๐ถ๐ฐ๐ ๐ผ๐ณ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- PySpark Architecture
- SparkContext and SparkSession
- RDDs (Resilient Distributed Datasets)
- DataFrames
- Transformations and Actions
- Lazy Evaluation
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ฎ๐๐ฎ๐๐ฟ๐ฎ๐บ๐ฒ๐:
- Creating DataFrames
- Reading Data from CSV, JSON, Parquet
- DataFrame Operations
- Filtering, Selecting, and Aggregating Data
- Joins and Merging DataFrames
- Working with Null Values
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ผ๐น๐๐บ๐ป ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐:
- Defining and Using UDFs (User Defined Functions)
- Column Operations (Select, Rename, Drop)
- Handling Complex Data Types (Array, Map)
- Working with Dates and Timestamps
๐ฃ๐ฎ๐ฟ๐๐ถ๐๐ถ๐ผ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฆ๐ต๐๐ณ๐ณ๐น๐ฒ ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐:
- Understanding Partitions
- Repartitioning and Coalescing
- Managing Shuffle Operations
- Optimizing Partition Sizes for Performance
๐๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฃ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ:
- When to Cache or Persist
- Memory vs Disk Caching
- Checking Storage Levels
๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐ช๐ถ๐๐ต ๐ฆ๐ค๐:
- Spark SQL Introduction
- Creating Temp Views
- Running SQL Queries
- Optimizing SQL Queries with Catalyst Optimizer
- Working with Hive Tables in PySpark
๐ช๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐๐ฎ๐๐ฎ ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Data Cleaning and Preparation
- Handling Missing Values
- Data Normalization and Transformation
- Working with Categorical Data
๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Broadcasting Variables
- Accumulators
- PySpark Window Functions
- PySpark with Machine Learning (MLlib)
- Working with Streaming Data (Spark Streaming)
๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ง๐๐ป๐ถ๐ป๐ด ๐ถ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ:
- Understanding Job, Stage, and Task
- Tungsten Execution Engine
- Memory Management and Garbage Collection
- Tuning Parallelism
- Using Spark UI for Performance Monitoring
Data Engineering Interview Preparation Resources
https://t.me/datascienceresourcestp/61
All the best ๐๐
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