Pyspark-

Introduction to PySpark:

• Explanation of what PySpark is and its significance in big data processing.
• Brief history and evolution of PySpark.
• Overview of the Spark ecosystem.

Basic Concepts of PySpark

• Understanding Resilient Distributed Datasets (RDDs) and DataFrames.
• How PySpark leverages the concept of lazy evaluation for optimization.
• Introduction to transformations and actions in PySpark.

Data Computing & Processing Framework: Spark 3.x
- Log Analytics Tools: Splunk
- PYTHON 3.x: Programming Language for Data Engineering
- NoSQL –Elastic Search, Cassandra
- Real time Messaging Tools: Kafka – Developer level
- Batch Processing, Real Time Streaming Application, Full Load, Incremental
Load etc
Soft skill training-Building CV and Interview
Live session : 50hrs + ( 12 hrs project work Included)

Q&A Session

• Open floor for attendees to ask questions.
• Addressing common challenges and concerns faced by beginners in PySpark.
• Conclusion and Next Steps
• • Recap of key takeaways from the webinar.
  • Resources for further learning and exploration (e.g., documentation, tutorials, online courses).
  • Encouragement for attendees to start experimenting with PySpark on their own projects.