Скачать или смотреть Mastering Apache Spark: Core APIs, Streaming, ML, and More! | Big Data & Analytics Guide

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Introduction to Spark: Prem introduced the core concepts and tools of Apache Spark, focusing on APIs and libraries, including low-level APIs (RDD), structured APIs (DataFrames and Datasets), and standard libraries for machine learning, graphics, and streaming. 0:26
Spark Application Execution: Prem explained how to run Spark applications, detailing the use of the 'spark submit' command, specifying master and deployment modes, and configuring resources such as executors and memory allocation. 4:04
DataFrames and Datasets: Prem discussed the differences between DataFrames and Datasets, highlighting that Datasets are type-safe and primarily used for Scala and Java, allowing schema definition at compile time to reduce runtime errors. 5:41
Real-Time Data Processing: Prem covered real-time data processing with Spark Streaming, emphasizing the use of micro-batching, scalability, transformation, checkpointing, and fault tolerance to handle live data streams. 6:47
Machine Learning with Spark: Prem outlined the machine learning capabilities of Spark, including support for various algorithms such as classification, regression, clustering, and feature engineering, and provided an example of logistic regression. 8:53
RDDs and Fault Tolerance: Prem described RDDs as low-level distributed collections that provide fine-grain control over computation, fault tolerance, and in-memory processing, and mentioned the use of SparkR for data scientists. 9:16
Third-Party Packages: Prem highlighted the importance of third-party packages in the Spark ecosystem, such as Delta Lake for ACID transactions, Koalas for pandas API, and hyperparameter tuning frameworks. 9:31