PySpark power guide.pdf
1.2 MB
๐ช๐ต๐ ๐๐๐ฒ๐ฟ๐ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ
If youโre working with large datasets, tools like Pandas can hit limits fast. Thatโs where ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ comes inโdesigned to scale effortlessly across big data workloads.
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ?
PySpark is the Python API for Apache Sparkโa powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
๐ช๐ต๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ ๐ฎ ๐ ๐๐๐-๐๐ฎ๐๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐:
โ๏ธ Scales to handle massive datasets
โ๏ธ Designed for distributed computing
โ๏ธ Blends SQL with Python for flexible logic
โ๏ธ Perfect for building end-to-end ETL pipelines
โ๏ธ Supports integrations like Hive, Kafka, and Delta Lake
๐ค๐๐ถ๐ฐ๐ธ ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
If youโre working with large datasets, tools like Pandas can hit limits fast. Thatโs where ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ comes inโdesigned to scale effortlessly across big data workloads.
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ?
PySpark is the Python API for Apache Sparkโa powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
๐ช๐ต๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ ๐ฎ ๐ ๐๐๐-๐๐ฎ๐๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐:
โ๏ธ Scales to handle massive datasets
โ๏ธ Designed for distributed computing
โ๏ธ Blends SQL with Python for flexible logic
โ๏ธ Perfect for building end-to-end ETL pipelines
โ๏ธ Supports integrations like Hive, Kafka, and Delta Lake
๐ค๐๐ถ๐ฐ๐ธ ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.filter(df["age"] > 30).show()
#PySpark #DataEngineering #BigData #ETL #ApacheSpark #DistributedComputing #PythonForData #DataPipelines #SparkSQL #ScalableAnalytics
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๐ DataCamp has officially partnered with Polars**โa cutting-edge DataFrame library designed for speed and efficiency!
To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *โIntroduction to Polarsโ* for the next 90 days. ๐
This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.
Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.
๐ Start learning now:
https://www.datacamp.com/courses/introduction-to-polars
๐ Join the communities:
To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *โIntroduction to Polarsโ* for the next 90 days. ๐
This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.
Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.
๐ Start learning now:
https://www.datacamp.com/courses/introduction-to-polars
#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Polars.pdf
391.5 KB
โ
โ โพ๏ธ Google Colab
โ
#Polars #DataEngineering #PythonLibraries #PandasAlternative #PolarsCheatSheet #DataScienceTools #FastDataProcessing #GoogleColab #DataAnalysis #PythonForDataScience๏ปฟ
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ฌ๐ผ๐๐ฟ_๐๐ฎ๐๐ฎ_๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ_๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐_๐ฆ๐๐๐ฑ๐_๐ฃ๐น๐ฎ๐ป.pdf
7.7 MB
1. Master the fundamentals of Statistics
Understand probability, distributions, and hypothesis testing
Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
2. Get hands-on with Python & SQL
Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
4. Practice case study interviews
Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
Gather feedback on your explanation and presence
6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
Familiarize with tools: Apache Spark, Kafka, Airflow
8. Strengthen storytelling with data
Apply the STAR method in behavioral questions
Simplify complex technical topics
Emphasize business impact and insight-driven decisions
9. Customize your resume and portfolio
Tailor your resume for each job role
Include links to projects or GitHub profiles
Match your skills to job descriptions
10. Stay consistent and track progress
Set clear weekly goals
Monitor covered topics and completed tasks
Reflect regularly and adapt your plan as needed
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ ๐ฎ๐๐๐ฒ๐ฟ_๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ_๐๐ถ๐ธ๐ฒ_๐ฎ_๐ฃ๐ฟ๐ผ_โ_๐๐น๐น_๐ถ๐ป_๐ข๐ป๐ฒ_๐๐๐ถ๐ฑ๐ฒ_๐ณ๐ผ๐ฟ_๐๐ฎ๐๐ฎ_๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐.pdf
2.6 MB
๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ถ๐ธ๐ฒ ๐ฎ ๐ฃ๐ฟ๐ผ โ ๐๐น๐น-๐ถ๐ป-๐ข๐ป๐ฒ ๐๐๐ถ๐ฑ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews โ this one is for you.
Iโm sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
๐ช๐ต๐ฎ๐'๐ ๐ถ๐ป๐๐ถ๐ฑ๐ฒ? โข Spark vs MapReduce
โข Spark Architecture โ Driver, Executors, DAG
โข RDDs vs DataFrames vs Datasets
โข SparkContext vs SparkSession
โข Transformations: map, flatMap, reduceByKey, groupByKey
โข Optimizations โ caching, persisting, skew handling, salting
โข Joins โ Broadcast joins, Shuffle joins
โข Deployment modes โ Cluster vs Client
โข Real interview-ready Q&A from top use cases
โข CSV, JSON, Parquet, ORC โ Format comparisons
โข Common commands, schema creation, data filtering, null handling
๐ช๐ต๐ผ ๐ถ๐ ๐๐ต๐ถ๐ ๐ณ๐ผ๐ฟ? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews โ this one is for you.
Iโm sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
๐ช๐ต๐ฎ๐'๐ ๐ถ๐ป๐๐ถ๐ฑ๐ฒ? โข Spark vs MapReduce
โข Spark Architecture โ Driver, Executors, DAG
โข RDDs vs DataFrames vs Datasets
โข SparkContext vs SparkSession
โข Transformations: map, flatMap, reduceByKey, groupByKey
โข Optimizations โ caching, persisting, skew handling, salting
โข Joins โ Broadcast joins, Shuffle joins
โข Deployment modes โ Cluster vs Client
โข Real interview-ready Q&A from top use cases
โข CSV, JSON, Parquet, ORC โ Format comparisons
โข Common commands, schema creation, data filtering, null handling
๐ช๐ต๐ผ ๐ถ๐ ๐๐ต๐ถ๐ ๐ณ๐ผ๐ฟ? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
#PySpark #DataEngineering #BigData #SparkArchitecture #RDDvsDataFrame #SparkOptimization #DistributedComputing #SparkInterviewPrep #DataPipelines #ApacheSpark #MapReduce #ETL #BroadcastJoin #ClusterComputing #SparkForEngineers
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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