on-demand course

Building Spark Applications

Name: Building Spark Applications
Availability: InStock

with Jonathan Dinu

November 2015

Intermediate

13h 17m

English

Pearson

Closed Captioning available in English, Japanese, Korean, Chinese (Simplified), Chinese (Traditional)

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Course outline

Building Spark Applications LiveLessons: Introduction
5m 3s
Topics
49s
1.1 Getting the Materials
2m 40s
1.2 A Brief Historical Diversion
7m 17s
1.3 Origins of the Framework
7m 23s
1.4 Why Spark?
19m 12s
1.5 Getting Set Up: Spark and Java
9m 48s
1.6 Getting Set Up: Scientific Python
5m 8s
1.7 Getting Set Up: R Kernel for Jupyter
9m 11s
1.8 Your First PySpark Job
18m 4s
1.9 Introduction to RDDs: Functions, Transformations, and Actions
23m 6s
1.10 MapReduce with Spark: Programming with Key-Value Pairs
17m 16s
Topics
1m 2s
2.1 Introduction to the Spark Programming APIs
10m 53s
2.2 PySpark: Loading and Importing Data
19m 31s
2.3 PySpark: Parsing and Transforming Data
9m 41s
2.4 PySpark: Analyzing Flight Delays
20m 52s
2.5 SparkR: Introduction to DataFrames
20m 33s
2.6 SparkR: Aggregations and Analysis
8m 33s
2.7 SparkR: Visualizing Data with ggplot2
9m 41s
2.8 Why (Spark) SQL?
3m 42s
2.9 Spark SQL: Adding Structure to Your Data
31m 47s
2.10 Spark SQL: Integration into Existing Workflows
4m 42s
Topics
1m 10s
3.1 How Spark Fits into the Data Science Process
14m 29s
3.2 Introduction to Exploratory Data Analysis
10m 9s
3.3 Case Study: DonorsChoose.org
17m 40s
3.4 Data Quality Checks with Accumulators
18m 49s
3.5 Making Sense of Data: Summary Statistics and Distributions
14m 51s
3.6 Working with Text: Introduction to NLP
7m 43s
3.7 Tokenization and Vectorization with Spark
17m 53s
3.8 Summarization with tf-idf
20m 17s
3.9 Introduction to Machine Learning
20m 47s
3.10 Unsupervised Learning with Spark: Implementing k-means
24m 4s
3.11 Testing k-means with DonorsChoose.org Essays
9m 15s
3.12 Challenges of k-means: Latent Features, Interpretation, and Validation
21m 38s
Topics
55s
4.1 Introduction to Distributed Systems
15m 56s
4.2 Building Systems That Scale
11m 37s
4.3 The Spark Execution Context
10m 8s
4.4 RDD Deep Dive: Dependencies and Lineage
11m 49s
4.5 A Day in the Life of a Spark Application
14m 1s
4.6 How Code Runs: Stages, Tasks, and the Shuffle
13m 21s
4.7 Spark Deployment: Local and Cluster Modes
20m 50s
4.8 Setting Up Your Own Cluster
22m 36s
4.9 Spark Performance: Monitoring and Optimization
9m 25s
4.10 Tuning Your Spark Application
20m 8s
4.11 Making Spark Fly: Parallelism
7m 34s
4.12 Making Spark Fly: Caching
13m 5s
Topics
53s
5.1 Machine Learning on Spark: MLlib and spark.ml
13m 40s
5.2 The KDD Cup Competition: Preparing Data and Imputing Values
22m 43s
5.3 Introduction to Supervised Learning: Logistic Regression
17m 36s
5.4 Building a Model with MLlib
13m 9s
5.5 Model Evaluation and Metrics
14m 41s
5.6 Leveraging scikit-learn to Evaluate MLlib Models
21m 37s
5.7 Training Models with spark.ml
16m 7s
5.8 Machine Learning Pipelines with spark.ml
11m 3s
5.9 Tuning Models: Features, Cross Validation, and Grid Search
13m 43s
5.10 Serializing and Deploying Models
8m 22s
Building Spark Applications LiveLessons:Summary
8m 6s

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Building Spark Applications

with Jonathan Dinu

Introduction

Lesson 1: Introduction to the Spark Environment

Lesson 2: Spark Programming APIs

Lesson 3: Your First Spark Application

Lesson 4: Spark Internals

Lesson 5: Advanced Applications

Summary

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