Video Description
Add Spark Streaming to your data science and machine learning Python projects
About This Video
- Create big data streaming pipelines with Spark using Python
- Run analytics on live tweet data from Twitter
- Integrate Spark Streaming with tools such as Apache Kafka, used by Fortune 500 companies
- Work with the new features of the most recent version of Spark: 2.3
In Detail
Spark Streaming is becoming incredibly popular, and with good reason. According to IBM, 90% of the data in the World today was created in the last two years alone. Our current output of data is roughly 2.5 quintillion bytes per day. The World is being immersed in data, more so each and every day. As such, analyzing static DataFrames for non-dynamic data is becoming less and less of a practical approach to more and more problems. This is where data streaming comes in, the ability to process data almost as soon as it's produced, recognizing the time-dependency of the data. Apache Spark Streaming gives us an unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption in the big data world. Spark provides in-memory cluster computing, which greatly boosts the speed of iterative algorithms and interactive data mining tasks. Spark also is a powerful engine for streaming data as well as processing it. The synergy between them makes Spark an ideal tool for processing gargantuan data fire hoses. Tons of companies, including Fortune 500 companies, are adapting Apache Spark Streaming to extract meaning from massive data streams; today, you have access to that same big data technology right on your desktop. This Apache Spark Streaming course is taught in Python. Python is currently one of the most popular programming languages in the World! Its rich data community, offering vast amounts of toolkits and features, makes it a powerful tool for data processing. Using PySpark (the Python API for Spark), you will be able to interact with Apache Spark Streaming's main abstraction, RDDs, as well as other Spark components, such as Spark SQL and much more! Let's learn how to write Apache Spark Streaming programs with PySpark Streaming to process big data sources today!
Table of Contents
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Chapter 1 : Getting started with Apache Spark Streaming
- The Course Overview 00:01:49
- How to Take this Course and How to Get Support 00:00:45
- Introduction to Streaming 00:07:29
- Pyspark Setup Tutorial 00:13:59
- Example Twitter Application 00:20:23
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Chapter 2 : Pyspark Basics
- What are Discretized Streams? 00:02:23
- How to Create Discretized Streams 00:06:11
- Transformations on DStreams 00:07:58
- Transformation Operation 00:07:29
- Window Operations 00:01:41
- Window 00:04:22
- countByWindow 00:03:40
- reduceByKeyAndWindow 00:04:52
- countByValueAndWindow 00:04:00
- Output Operations on DStreams 00:03:33
- forEachRDD 00:05:59
- SQL Operations 00:05:42
- Reviewing the Basics 00:05:34
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Chapter 3 : Advanced Spark Concepts
- Join Operations 00:05:31
- Stateful Transformations 00:04:44
- Checkpointing 00:05:46
- Accumulators 00:03:27
- Fault Tolerance 00:11:48
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Chapter 4 : PySpark Streaming at Scale
- Performance Tuning 00:08:39
- PySpark Streaming with Apache Kafka 00:11:22
- PySpark Streaming with Amazon Kinesis 00:13:13
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Chapter 5 : Structured Streaming
- Introduction to Structured Streaming 00:04:41
- Operations on Streaming Dataframes and DataSets 00:09:05
- Window Operations 00:08:48
- Handling Late Data and Watermarking 00:06:27
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Chapter 6 : Course Conclusion
- Final Video 00:02:42