Concept

Apache Spark

Related lectures (32)

Advanced Data Stream Processing Concepts

Explores event time vs. processing time, stream processing operations, stream-stream joins, and handling late/out-of-order data in data stream processing.

Analytics on Data at Rest and Data in Motion

Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.

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Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.

Introduction to Data Stream Processing

Covers the fundamentals of data stream processing, including real-time insights, industry applications, and practical exercises on Kafka and Spark Streaming.

Collaborative Data Science

Covers collaborative data science tools, big data concepts, Spark, and data stream processing, with tips for the final project.

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Spark DataFrames: Basics and Optimization

Covers the basics of Spark DataFrames, their advantages, performance comparison with RDDs, and practical demos.

Introduction to Spark runtime architecture

Introduces Apache Spark, covering its key features, history, RDDs, architecture, and distributed computing framework.

Spark Data Frames

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Exam Instructions: Types of Questions and Code Grading

Covers exam instructions, types of questions, and code grading for understanding concepts and coding.

Programming Models: Overview and Examples

Explores programming models for big data processing, including Spark's RDDs and optimizations.