O’Reilly – Learning Path Scaling Python for Big Data
English | Size: 1.62 GB
Category: CBTs
If you have some Python experience, and you want to take it to the next level, this practical, hands-on Learning Path will be a helpful resource. Video tutorials in this Learning Path will show you how to use Python for distributed task processing, and perform large-scale data processing in Spark using the PySpark API.
Table of Contents
Building Data Pipelines with Python
Welcome To The Course 00:02:53
About The Author 00:01:55
How To Access Your Working Files 00:01:15
Introduction To Automation 00:02:48
Adventures With Servers 00:06:37
Being A Good Systems Caretaker 00:06:03
What Is A Queue? 00:02:32
What Is A Consumer? What Is A Producer? 00:02:00
Why Celery? 00:01:49
Celery Architecture & Set Up 00:05:25
Writing Your First Tasks 00:07:49
Deploying Your Tasks 00:06:08
Scaling Your Workers 00:08:52
Monitoring With Flower 00:05:05
Advanced Celery Features 00:06:00
Why Dask? 00:03:01
First Steps With Dask 00:10:08
Dask Bags 00:10:18
Dask Distributed 00:09:58
What Are Data Pipelines? What Is Dag? 00:02:37
Luigi And Airflow: A Comparison 00:05:50
First Steps With Luigi 00:07:12
More Complex Luigi Tasks 00:09:17
Introduction To Hadoop 00:08:21
First Steps With Airflow 00:08:07
Custom Tasks With Airflow 00:09:16
Advanced Airflow: Subdags And Branches 00:11:17
Using Luigi With Hadoop 00:10:15
Apache Spark 00:08:28
Apache Spark Streaming 00:06:32
Django Channels 00:09:39
And Many More 00:05:59
Introduction To Testing With Python 00:07:24
Property-Based Testing With Hypothesis 00:06:09
What’s Next? 00:03:57
Introduction to PySpark
Introduction And Course Overview 00:02:01
About The Author 00:01:02
Installing Python 00:04:38
Installing iPython And Using Notebooks 00:06:28
How To Access Your Working Files 00:01:15
Download And Setup 00:03:24
Running The Spark Shell 00:05:35
Running The Spark Shell With iPython 00:06:38
What Is A Resilient Distributed Dataset – RDD? 00:04:54
Reading A Text File 00:03:34
Actions 00:02:13
Transformations 00:02:30
Persisting Data 00:04:11
Map 00:03:04
Filter 00:03:56
Flatmap 00:03:16
MapPartitions 00:04:07
MapPartitionsWithIndex 00:01:51
Sample 00:02:36
Union 00:01:11
Intersection 00:01:28
Distinct 00:02:02
Cartesian 00:03:17
Pipe 00:03:40
Coalesce 00:02:12
Repartition 00:02:29
RepartitionAndSortWithinPartitions 00:03:58
Reduce 00:04:19
Collect 00:01:56
Count 00:03:05
First 00:01:20
Take 00:01:05
TakeSample 00:03:03
TakeOrdered 00:02:10
SaveAsTextFile 00:04:09
CountByKey 00:02:40
ForEach 00:03:11
GroupByKey 00:02:31
ReduceByKey 00:03:30
AggregateByKey 00:03:44
SortByKey 00:02:47
Join 00:04:16
CoGroup 00:02:09
WholeTextFile 00:03:15
Pickle Files 00:03:59
HadoopInputFormat 00:05:35
HadoopOutputFormat 00:05:31
Broadcast Variables 00:04:17
Accumulators 00:05:08
Using A Custom Accumulator 00:04:52
Partitioning 00:07:56
Spark Standalone Cluster 00:04:26
Mesos 00:03:38
Yarn 00:02:28
Client Versus Cluster Mode 00:02:41
Spark Streaming 00:04:21
Dataframes And SQL 00:03:28
MLlib 00:04:29
Resources And Where To Go From Here 00:01:02
Wrap Up 00:01:28
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