Mastering Data Engineering with Apache Spark
Overview
The Mastering Data Engineering with Apache Spark program is designed for data engineers, big data professionals, and software developers looking to build scalable, distributed data processing pipelines. This program covers Apache Spark’s core architecture, Spark SQL, DataFrames, structured streaming, and advanced optimizations, along with integration with Kafka, Delta Lake, and cloud platforms like AWS, Azure, and GCP. Participants will gain hands-on experience in real-time and batch data processing, performance tuning, and deploying Spark applications in production environments.
- Process and analyze big data using Spark’s distributed computing framework.
- Implement real-time analytics and machine learning pipelines with Spark.
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Introduction to Apache Spark & Big Data Ecosystem
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Working with Spark Core & RDDs
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Spark SQL & DataFrames – Structured Data Processing
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Streaming Data Processing with Spark Structured Streaming
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Advanced Data Engineering with Spark & Delta Lake
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Machine Learning with Spark MLlib
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Performance Optimization & Debugging in Spark
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Deploying Spark Applications on Cloud & Kubernetes
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Spark & Data Lake Integration
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Capstone Project – End-to-End Data Pipeline with Spark
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