PySpark - Combining Machine Learning & Big Data

How do you apply machine learning when your dataset is too big for a single machine? Discover PySpark's powerful, distributed ML pipelines.

#1about 3 minutes

Combining big data and machine learning for business insights

The exponential growth of data necessitates combining big data processing with machine learning to personalize user experiences and drive revenue.

#2about 3 minutes

An introduction to the Apache Spark analytics engine

Apache Spark is a unified analytics engine for large-scale data processing that provides high-level APIs and specialized libraries like Spark SQL and MLlib.

#3about 4 minutes

Understanding Spark's core data APIs and abstractions

Spark's data abstractions evolved from the low-level Resilient Distributed Dataset (RDD) to the more optimized and user-friendly DataFrame and Dataset APIs.

#4about 11 minutes

How the Spark cluster architecture enables parallel processing

Spark's architecture uses a driver program to coordinate tasks across a cluster manager and multiple worker nodes, which run executors to process data in parallel.

#5about 5 minutes

Using Python with Spark through the PySpark library

PySpark provides a Python API for Spark, using the Py4J library to communicate between the Python process and Spark's core JVM environment.

#6about 5 minutes

Exploring the key features of the Spark MLlib library

Spark's MLlib offers a comprehensive toolkit for machine learning, including pre-built algorithms, featurization tools, pipelines for workflow management, and model persistence.

#7about 4 minutes

The standard workflow for machine learning in PySpark

A typical machine learning workflow in Spark involves using DataFrames, applying Transformers for feature engineering, training a model with an Estimator, and orchestrating these steps with a Pipeline.

#8about 3 minutes

Pre-built algorithms and utilities available in Spark MLlib

MLlib includes a variety of common, pre-built algorithms for classification, regression, and clustering, such as logistic regression, SVM, and K-means clustering.