Apache Spark Core and Structured Streaming 3.0 In-Depth
In-Depth, Hands-On driven exposure to the features and concepts of Spark Core with tips on tuning its performance
Big Data,Data & Analytics,Data Science,PySpark,Hadoop
Lectures -50
Quizzes -8
Duration -13.5 hours
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Course Description
Apache Spark has turned out to be the most sought-after skill for any big data engineer. An evolution of MapReduce programming paradigm, Spark provides unified data processing from writing SQL to performing graph processing to implementing Machine Learning algorithms. It effectively uses cluster nodes and better memory management to spread the load across cluster of nodes to get faster results. Apache Spark drives the mission of data-driven-decision-making in thousands of organizations.
In order to fairly appreciate the benefits of the libraries of Apache Spark, it is essential to know the foundations right. This course aims exactly at that part. It starts from the beginner level and gradually explains all the complex concepts in an easy to reflect manner. It gives a profound description of the features and working of the framework through 5 different use cases with detailed hands on implementations. In fact, some hands-on sessions and solutions to the use-cases are explained in a full classroom mode with videos extending over 40 mins. After taking this course, you will gain the expertise on Spark Core and usage of further libraries like Spark SQL, Structured Streaming, Spark ML and GraphX will be much easier to visualize, implement and optimize.
This illustrative course will build your foundational knowledge. You will learn the differences between batch & stream processing, programming model, the APIs and the challenges specific to stream processing. Quickly we'll move to understand the concepts of stream processing with wide varieties of examples & hands-on, dealing with inner working and taking a use case towards the end. All of this activity will be on cloud using Spark 3.0.
Goals
What will you learn in this course:
- Strong focus on the practicality by getting into hands-on mode with plentiful of examples
- Develop in-depth understanding of the underlying concepts the core of Apache Spark
- Know the ways to get the best performance from Spark in production
- Avoid the common pitfalls when writing Spark applications
- In Depth exploration of Spark Structured Streaming 3.0 using Python API
- Get introduced to Apache Kafka on a high level in the process
- Understand the nuances of Stream Processing in Apache Spark
- Discover various features Spark provides out of the box for Stream Processing
Prerequisites
What are the prerequisites for this course?
We'll be using Python API in Spark Programming. However, we'll explain all the programs in details but fundamental knowledge of Python will be beneficial.
Curriculum
Check out the detailed breakdown of what’s inside the course
Distributed Data Processing
2 Lectures
- Expectations from Data Processing Framework 11:41 11:41
- Introduction to Map Reduce 22:09 22:09
Resources: Spark Core
1 Lectures
Concepts of Apache Spark
3 Lectures
Starting HandsOn with Spark Applications
3 Lectures
Deep Dive into Spark Concepts
3 Lectures
Exploring operations on RDDs
5 Lectures
Advanced Concepts of Apache Spark
9 Lectures
Running Spark Application in a Cluster
1 Lectures
Tuning Spark Applications
5 Lectures
Interview with Spark Expert
1 Lectures
First Steps with Spark Structured Streaming
5 Lectures
Resources: Spark Structured Streaming
1 Lectures
Deep Dive into Spark Structured Streaming
4 Lectures
Integrating Spark Structured Streaming with Kafka
3 Lectures
Applying Structured Streaming in Production and Road to Expertise
4 Lectures
Instructor Details
Amit Ranjan
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