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What you'll learn?
- 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.
Course Overview
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.
Pre-requisites
- 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
Target Audience
- Data engineers and developers who wish to leverage the fast analytics using Apache Spark in production environments.
- Big Data Developers
Curriculum 31 Lectures 10:38:59
-
Section 1 : Distributed Data Processing
- Lecture 2 :
- Introduction to Map Reduce
-
Section 2 : Resources
- Lecture 1 :
- Links to resources
-
Section 3 : Concepts of Apache Spark
- 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
- 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
- Lecture 1 :
- Deeper dive into our first Spark Program
- Lecture 2 :
- Partitioners
- Lecture 3 :
- Quiz
-
Section 6 : Exploring operations on RDDs
- Lecture 1 :
- Operations on RDDs without Aggregations
- Lecture 2 :
- Aggregations on RDD [Part 1]
- Lecture 3 :
- Aggregations on RDD [Part 2]
- Lecture 4 :
- Quiz
- Lecture 5 :
- Joining RDDs
-
Section 7 : Advanced Concepts of Apache Spark
- 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
-
Section 8 : Running Spark Application in a Cluster
- Lecture 1 :
- Running Spark on a Cluster | New York Yellow Taxi Trip Data Analysis
-
Section 9 : Tuning Spark Applications
- 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
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