Learn Apache Spark Core 3.0 In-Depth online

Apache Spark Core 3.0 In-Depth

In-Depth, Hands-On driven exposure to the features and concpets of Spark Core with tips on tuning its performance

Created by : Rated :4.8 85138 views

Last Updated: 2022-12-16 05:12:51 English Course Type: Self Learning

Apache spark in detail with focus on hands-on and the underlying concepts. Learners will get to know the ways to get the best performance from Spark in production and avoid the common pitfalls.

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.

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.
Learning Desire

Data engineers and developers who wish to leverage the fast analytics using Apache Spark in production environments.
Big Data Developers

Section 1 : Distributed Data Processing 2 Lectures 00:33:50
Lecture 1 :
Expectations from Data Processing Framework Preview
Lecture 2 :
Introduction to Map Reduce
Section 2 : Resources 1 Lectures
Lecture 1 :
Links to resources
Section 3 : Concepts of Apache Spark 3 Lectures 00:44:58
Lecture 1 :
Introduction to Spark
Lecture 2 :
Introduction to Resilient Distributed Dataset (RDD)
Lecture 3 :
Architecture of Spark
Section 4 : Starting HandsOn with Spark Applications 3 Lectures 00:49:14
Lecture 1 :
Downloading and Installing
Lecture 2 :
Our first Spark Program
Lecture 3 :
Our first exposure to data aggregation in Spark
Section 5 : Deep Dive into Spark Concepts 3 Lectures 01:00:46
Lecture 1 :
Deeper dive into our first Spark Program
Lecture 2 :
Partitioners
Lecture 3 :
Quiz
Quiz
Section 6 : Exploring operations on RDDs 5 Lectures 01:33:00
Lecture 1 :
Operations on RDDs without Aggregations
Lecture 2 :
Aggregations on RDD [Part 1]
Lecture 3 :
Aggregations on RDD [Part 2]
Lecture 4 :
Quiz
Quiz
Lecture 5 :
Joining RDDs
Section 7 : Advanced Concepts of Apache Spark 8 Lectures 04:00:18
Lecture 1 :
Broadcast Variables
Lecture 2 :
Hands On with Broadcast Variables | Analyzing Stock Data
Lecture 3 :
Caching
Lecture 4 :
Hands On with Caching | Analyzing Healthcare Dataset
Lecture 5 :
Hands On with Accumulators | Analyzing Log Data
Lecture 6 :
Hands On with combineByKey | Credit Card Transaction Analysis
Lecture 7 :
Hands On with Secondary Sort | Analyzing Car Loan Data
Lecture 8 :
Quiz
Quiz
Section 8 : Running Spark Application in a Cluster 1 Lectures 00:43:09
Lecture 1 :
Running Spark on a Cluster | New York Yellow Taxi Trip Data Analysis
Section 9 : Tuning Spark Applications 5 Lectures 01:13:44
Lecture 1 :
Deciding the right size for executor
Lecture 2 :
Exploring Dynamic Allocation
Lecture 3 :
Handling Skewed Data
Lecture 4 :
Further tips on optimising the performance
Lecture 5 :
Quiz
Quiz

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