BEST PySpark TRAINING IN PUNE | ONLINE

Duration of Training : 32 hrs

Batch type : Weekdays/Weekends

Mode of Training : Classroom/Online/Corporate Training

Why Radical Technologies

Module 1: Introduction to PySpark

• What is PySpark?
• PySpark vs. Spark: Understanding the difference
• Spark architecture and components
• Setting up PySpark environment
• Creating RDDs (Resilient Distributed Datasets)
• Transformations and actions in RDDs
• Hands-on exercises

Module 2: PySpark DataFrames

• Introduction to DataFrames
• Creating DataFrames from various data sources (CSV, JSON, Parquet, etc.)
• Basic DataFrame operations (filtering, selecting, aggregating)
• Handling missing data
• DataFrame joins and unions
• Hands-on exercises

Module 3: PySpark SQL

• Introduction to Spark SQL
• Creating temporary views and global temporary views
• Executing SQL queries on DataFrames
• Performance optimization techniques
• Working with user-defined functions (UDFs)
• Hands-on exercises

Module 4: PySpark MLlib (Machine Learning Library)

• Introduction to MLlib
• Data preprocessing and feature engineering
• Building and evaluating regression models
• Classification algorithms and evaluation metrics
• Clustering and collaborative filtering
• Model selection and tuning
• Hands-on exercises with real-world datasets

Module 5: PySpark Streaming

• Introduction to Spark Streaming
• DStream (Discretized Stream) and input sources
• Windowed operations and stateful transformations
• Integration with Kafka for real-time data processing
• Hands-on exercise

Module 6: PySpark and Big Data Ecosystem

• Overview of Hadoop, HDFS, and YARN
• Integrating PySpark with Hadoop and Hive
• PySpark and NoSQL databases (e.g., HBase)
• Spark on Kubernetes
• Hands-on exercises

Module 7: PySpark Optimization and Best Practices

• Understanding Spark’s execution plan
• Performance tuning and optimization techniques
• Broadcast variables and accumulators
• PySpark configuration and memory management
• Coding best practices for PySpark
• Hands-on exercises

Module 8: Advanced PySpark Concepts (Optional)

• Spark GraphX for graph processing
• SparkR: R language integration with PySpark
• Deep learning with Spark using TensorFlow or Keras
• PySpark and SparkML integration
• Hands-on exercises and mini-projects

Learn PySpark Course in Pune with Training, Certification & Guaranteed Job Placement Assistance!

Online Batches Available for the Areas-

Ambegaon Budruk | Aundh | Baner | Bavdhan Khurd | Bavdhan Budruk | Balewadi | Shivajinagar | Bibvewadi | Bhugaon | Bhukum | Dhankawadi | Dhanori | Dhayari | Erandwane | Fursungi | Ghorpadi | Hadapsar | Hingne Khurd | Karve Nagar | Kalas | Katraj | Khadki | Kharadi | Kondhwa | Koregaon Park | Kothrud | Lohagaon | Manjri | Markal | Mohammed Wadi | Mundhwa | Nanded | Parvati (Parvati Hill) | Panmala | Pashan | Pirangut | Shivane | Sus | Undri | Vishrantwadi | Vitthalwadi | Vadgaon Khurd | Vadgaon Budruk | Vadgaon Sheri | Wagholi | Wanwadi | Warje | Yerwada | Akurdi | Bhosari | Chakan | Charholi Budruk | Chikhli | Chimbali | Chinchwad | Dapodi | Dehu Road | Dighi | Dudulgaon | Hinjawadi | Kalewadi | Kasarwadi | Maan | Moshi | Phugewadi | Pimple Gurav | Pimple Nilakh | Pimple Saudagar | Pimpri | Ravet | Rahatani | Sangvi | Talawade | Tathawade | Thergaon | Wakad