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WeAreDevelopers LIVE • Oct 12, 2020

PySpark - Combining Machine Learning & Big Data

Ayon Roy

Bypass complex Scala programming and deploy native Python operations across distributed clusters. Learn how PySpark and MLlib unite to accelerate robust, scalable machine learning pipelines.

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#1 about 4 min

Defining big data and machine learning fundamentals

The four Vs conceptualize big data while machine learning enables systems to predict outcomes without explicit programming.

#2 about 3 min

Why organizations combine big data and machine learning

Tapping into massive amounts of new data with machine learning drives personalized consumer experiences.

#3 about 3 min

Capabilities of the Apache Spark processing engine

Spark provides a large-scale data processing environment with integrated libraries for machine learning, SQL, and streaming.

#4 about 4 min

Understanding RDDs, DataFrames, and Datasets in Spark

Data ingestion evolved from complex resilient distributed datasets into developer-friendly structures like DataFrames and Datasets.

#5 about 5 min

Exploring the Apache Spark layer architecture

Cluster managers drive resource allocation while Spark Core processes diverse data sources using top-layer libraries.

#6 about 8 min

How cluster managers orchestrate distributed tasks

A driver program coordinates with a cluster manager via a Spark context to execute computations in parallel on worker nodes.

#7 about 5 min

Harnessing Spark with Python using PySpark and Py4J

The PySpark library relies on Py4J sockets to translate Python instructions into processes compatible with Java Virtual Machines.

#8 about 5 min

Advantages of utilizing Spark's native MLlib

Built-in classification, feature extraction, and pipeline support make MLlib a scalable choice for data analytics.

#9 about 5 min

Constructing ML pipelines with transformers and estimators

DataFrames pass through transformers to engineer features and enter estimators that output functional predictive models.

#10 about 4 min

Pre-built algorithms and further learning resources

Spark MLlib offers accessible, pre-built regression and clustering algorithms for integrating big data processing with predictive modeling.

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