Apache Spark is an open-source, distributed computing system that is designed for processing large amounts of data across a cluster of computers. It was developed by the Apache Software Foundation and is widely used for big data processing, machine learning, and data analytics.
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Introduction to Big Data and Apache Spark
Introduction to Big Data
Overview of Apache Spark
Spark architecture and components
Advantages of Apache Spark over Hadoop
Spark Core
Introduction to Spark Core
Spark RDDs (Resilient Distributed Datasets)
Transformations and actions
Spark SQL
Spark Streaming
Introduction to Spark Streaming
Spark Streaming architecture
Transformations and actions in Spark Streaming
Window operations
Spark MLlib
Introduction to Spark MLlib
MLlib data types and algorithms
Supervised and unsupervised learning
Classification and regression algorithms
Clustering and collaborative filtering
GraphX
Introduction to GraphX
GraphX API and data model
Graph algorithms
Pregel API
Spark SQL and DataFrames
Introduction to Spark SQL and DataFrames
Spark SQL architecture and components
Creating DataFrames
Manipulating DataFrames
Performance Tuning and Optimization
Introduction to performance tuning and optimization
Bottlenecks and optimization techniques
Resource allocation and cluster configuration
Cache management
Spark on YARN
Introduction to YARN
Running Spark on YARN
YARN cluster configuration
Resource allocation and tuning
Spark on Mesos
Introduction to Mesos
Running Spark on Mesos
Mesos cluster configuration
Resource allocation and tuning
Spark on Kubernetes
Introduction to Kubernetes
Running Spark on Kubernetes
Kubernetes cluster configuration
Resource allocation and tuning
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