8 Best Apache Spark Courses - Learn Apache Spark Online
Highly curated best Apache Spark tutorials for beginners. Start with the best Apache Spark Courses and learn Spark as a beginner.
The Best Apache Spark tutorials for beginners to learn Apache Spark in 2024.
In an increasingly interconnected world, data is being created faster than Moore's law can keep up, requiring us to be smarter in our analysis. Previously, we had Hadoop's MapReduce framework for batch processing, but modern big data processing demands have outgrown it.
This is when Apache Spark comes along, offering speeds up to 100X faster than Hadoop and setting the world record for large-scale sorting. Its general abstraction means its capabilities go way beyond simple batch processing, making it capable of super-fast iterative algorithms and exactly once streaming semantics.
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Top Apache Spark Courses and Certifications List
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Big Data Analytics with Hadoop and Apache Spark Online Class
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Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru
1. Learn Apache Spark 3 with Scala: Hands On with Big Data!
Dive right in with 20+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop!
In this course, you will learn how to:
- frame big data analysis problems as Apache Spark scripts.
- develop distributed code using the Scala programming language.
- optimize Spark jobs through partitioning, caching, and other techniques.
- build, deploy, and run Spark scripts on Hadoop clusters.
- process continual streams of data with Spark Streaming.
- transform structured data using SparkSQL, DataSets, and DataFrames.
- traverse and analyze graph structures using GraphX.
- analyze massive data set with Machine Learning on Spark.
- understand the concepts of Spark's Resilient Distributed Datasets, DataFrames, and Datasets.
- get a crash course in the Scala programming language.
- develop and run Spark jobs quickly using Scala, IntelliJ, and SBT.
- translate complex analysis problems into iterative or multi-stage Spark scripts.
- scale up to larger data sets using Amazon's Elastic MapReduce service.
- understand how Hadoop YARN distributes Spark across computing clusters.
- practice using other Spark technologies, like Spark SQL, DataFrames, DataSets, Spark Streaming, Machine Learning, and GraphX.
The course includes:
- Introduction to Apache Spark
- Spark Basics
- What's New in Spark 3
- Scala Crash Course
- Using Resilient Distributed Datasets (RDDs)
- SparkSQL, DataFrames, and DataSets
- Advanced Examples of Spark Programs
- Running Spark on a Cluster
- Machine Learning with Spark ML
- Intro to Spark Streaming
- Intro to GraphX
Initially, you will get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, you will move to some more complex and interesting tasks.
This course will use a million movie ratings to find movies that are similar to each other. Next, you will analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes.
By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.
You can take Learn Apache Spark 3 with Scala: Hands On with Big Data! Certificate Course on Udemy.
- Course rating: 4.5 out of 5.0 ( 13,587 Ratings total)
- Duration: 9 h
- Certificate: Certificate on completion
- View course
2. Big Data Analytics with Hadoop and Apache Spark
Discover how to build scalable and optimized data analytics pipelines by combining the powers of Apache Hadoop and Spark.
The course includes:
- Introduction and Setup
- Apache Hadoop overview
- Apache Spark overview
- Integrating Hadoop and Spark
- Setting up the environment
- HDFS Data Modeling for Analytics
- Storage formats
- Compression
- Partitioning
- Bucketing
- Best practices for data storage
- Data Ingestion with Spark
- Reading external files into Spark
- Writing to HDFS
- Parallel writes with partitioning
- Parallel writes with bucketing
- Best practices for ingestion
- Data Extraction with Spark
- How Spark works
- Reading HDFS files with schema
- Reading partitioned data
- Reading bucketed data
- Best practices for data extraction
- Optimizing Spark Processing
- Pushing down projections
- Pushing down filters
- Managing partitions
- Managing shuffling
- Improving joins
- Storing intermediate results
- Best practices for data processing
In this course, learn how to leverage these two technologies to build scalable and optimized data analytics pipelines. It explores ways to optimize data modeling and storage on HDFS; discusses scalable data ingestion and extraction using Spark; and provides tips for optimizing data processing in Spark.
You can take Big Data Analytics with Hadoop and Apache Spark Certificate Course on LinkedIn.
- Course rating: 16,562 total enrollments
- Duration: 1 h 2 m
- Certificate: Certificate on completion
- View course
3. Taming Big Data with Apache Spark and Python - Hands On!
Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets on your desktop or on Hadoop with Python.
In this course, you will learn how to:
- use DataFrames and Structured Streaming in Spark 3.
- frame big data analysis problems as Spark problems.
- use Amazon's Elastic MapReduce service to run your job on a cluster with Hadoop YARN.
- install and run Apache Spark on a desktop computer or on a cluster.
- use Spark's Resilient Distributed Datasets to process and analyze large data sets across many CPUs.
- implement iterative algorithms such as breadth-first-search using Spark.
- use the MLLib machine learning library to answer common data mining questions.
- understand how Spark SQL lets you work with structured data.
- understand how Spark Streaming lets your process continuous streams of data in real-time.
- tune and troubleshoot large jobs running on a cluster.
- Share information between nodes on a Spark cluster using broadcast variables and accumulators.
- understand how the GraphX library helps with network analysis problems.
- understand the concepts of Spark's DataFrames and Resilient Distributed Datastores.
- develop and run Spark jobs quickly using Python.
- translate complex analysis problems into iterative or multi-stage Spark scripts.
- scale up to larger data sets using Amazon's Elastic MapReduce service.
- understand how Hadoop YARN distributes Spark across computing clusters.
- understand other Spark technologies, like Spark SQL, Spark Streaming, and GraphX.
Initially, you will get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, you will move to some more complex and interesting tasks.
This course will use a million movie ratings to find movies that are similar to each other. Next, you will analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes.
By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.
You can take Taming Big Data with Apache Spark and Python - Hands On! Certificate Course on Udemy.
- Course rating: 4.5 out of 5.0 ( 10,699 Ratings total)
- Duration: 7 h
- Certificate: Certificate on completion
- View course
4. Apache Spark for Java Developers
Get processing Big Data using RDDs, DataFrames, SparkSQL, and Machine Learning - and real-time streaming with Kafka!
In this course, you will learn how to:
- use functional style Java to define complex data processing jobs.
- understand the differences between the RDD and DataFrame APIs.
- use an SQL-style syntax to produce reports against Big Data sets.
- use Machine Learning Algorithms with Big Data and SparkML.
- connect Spark to Apache Kafka to process Streams of Big Data.
- structured streaming can be used to build pipelines with Kafka.
The course includes:
- Spark Architecture and RDDs
- Getting Started
- Reduces on RDDs
- Mapping and Outputting
- Tuples
- PairRDDs
- FlatMaps and Filters
- Reading from Disk
- Keyword Ranking Practical
- Sorts and Coalesce
- Deploying to AWS EMR
- Big Data Big Exercise
- RDD Performance
- Module 2 - Chapter 1 SparkSQL Introduction
- SparkSQL Getting Started
- Datasets
- The Full SQL Syntax
- In-Memory Data
- Groupings and Aggregations
- Date Formatting
- Multiple Groupings
- Ordering
- DataFrames API
- Pivot Tables
- More Aggregations
- Practical Exercise
- User-Defined Functions
- SparkSQL Performance
- HashAggregation
- SparkSQL Performance vs RDDs
- SparkML for Machine Learning
- Linear Regression Models
- Training Data
- Model Fitting Parameters
- Feature Selection
- Non-Numeric Data
- Pipelines
- Logistic Regression
- Decision Trees
- K Means Clustering
- Recommender Systems
- Spark Streaming and Structured Streaming with Kafka
- Streaming with Apache Kafka
- Structured Streaming
This course covers all of the fundamentals you need to understand the main operations you can perform in Spark Core, SparkSQL, and DataFrames in detail, with examples. You'll be able to follow along with all of the examples and run them on your own local development computer.
Included with the course is a module covering SparkML, an addition to Spark that allows you to apply Machine Learning models to your Big Data without any mathematical experience necessary.
Optionally, if you have an AWS account, you'll see how to deploy your work to a live EMR (Elastic Map Reduce) hardware cluster.
You will be going deep into the internals of Spark and you'll find out how it optimizes your execution plans. You will be comparing the performance of RDDs vs SparkSQL, and you'll learn about the major performance pitfalls which could save a lot of money for live projects.
Finally, you will learn about Spark Streaming, where you will get hands-on experience of integrating Spark with Apache Kafka to handle real-time big data streams. You will use both the DStream and the Structured Streaming APIs.
You can take Apache Spark for Java Developers Certificate Course on Udemy.
- Course rating: 4.6 out of 5.0 ( 1,724 Ratings total)
- Duration: 21 h 5 m
- Certificate: Certificate on completion
- View course
5. Apache Spark with Scala useful for Databricks Certification
Apache Spark with Scala useful for Databricks Certification.
In this course, you will learn:
- art of framing data analysis problems as Spark problems.
- how to execute them up to run on Databricks cloud computing services.
The course includes:
- Spark Architecture Components
- Driver
- Core/Slots/Threads,
- Executor
- Partitions
- Spark Execution
- Jobs
- Tasks
- Stages
- Spark Concepts
- Caching,
- DataFrame Transformations vs. Actions, Shuffling
- Partitioning, Wide vs. Narrow Transformations
- DataFrames API
- DataFrameReader
- DataFrameWriter
- DataFrame [Dataset]
- Row & Column (DataFrame)
- Spark SQL Functions
You can take Apache Spark with Scala useful for Databricks Certification Certificate Course on Eduonix.
- Course rating: 4.3 out of 5.0 ( 37 Ratings total)
- Duration: 5 h 5 m
- Certificate: Certificate on completion
- View course
6. Apache Spark Fundamentals
This course will teach you how to use Apache Spark to analyze your big data at lightning-fast speeds; leaving Hadoop in the dust! For a deep dive on SQL and Streaming check out the sequel, Handling Fast Data with Apache Spark SQL and Streaming.
The course includes:
- Getting Started with Apache Spark
- Spark Core: Part 1
- Spark Core: Part 2
- Distribution and Instrumentation
- Spark LibrariesOptimizations and the Future
Here, you will learn Spark from the ground up, starting with its history before creating a Wikipedia analysis application as one of the means for learning a wide scope of its core API. That core knowledge will make it easier to look into Spark's other libraries, such as the streaming and SQL APIs.
Finally, you'll learn how to avoid a few commonly encountered rough edges of Spark. You will leave this course with a tool belt capable of creating your own performance-maximized Spark application.
You can take Apache Spark Fundamentals Certificate Course on Pluralsight.
- Course rating: 4.0 out of 5.0 ( 320 Ratings total)
- Duration: 4 h 15 m
- Certificate: Certificate on completion
- View course
7. Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru
Learn analyzing large data sets with Apache Spark by 10+ hands-on examples. Take your big data skills to the next level.
In this course, you will learn how to:
- explore the price trend by looking at the real estate data in California.
- write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data.
- develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom.
- understand an overview of the architecture of Apache Spark.
- work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
- develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL.
- scale-up Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service.
- analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.
- share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators.
- perform advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching, and persisting RDDs.
- perform best practices of working with Apache Spark in the field.
The course includes:
- Get Started with Apache Spark
- RDD
- Spark Architecture and Components
- Pair RDD
- Advanced Spark Topic
- Spark SQL
- Running Spark in a Cluster
This course covers all the fundamentals of Apache Spark with Java and teaches you everything you need to know about developing Spark applications with Java.
At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adapt Apache Spark for building big data processing pipelines and data analytics applications.
This course covers a variety of big data examples. You will learn about how to frame data analysis problems as Spark problems. You will learn about examples such as aggregating NASA Apache weblogs from different sources
You can take Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru Certificate Course on Udemy.
- Course rating: 4.6 out of 5.0 ( 2,679 Ratings total)
- Duration: 3 h 5 m
- Certificate: Certificate on completion
- View course
8. Machine Learning with Apache Spark 3.0 using Scala
Machine Learning with Apache Spark 3.0 using Scala.
In this course, you will learn how to:
- master the art of framing data analysis problems as Spark problems.
- build Apache Spark machine learning projects.
- explore Apache Spark and machine learning on the Databricks platform.
- execute them up to run on Databricks cloud computing services.
The course includes:
- What is Spark ML?
- Types of Machine Learning
- Steps Involved in the Machine learning program
- Basic Statics
- Data Sources
- Pipelines
- Extracting, transforming, and selecting features
- Classification and Regression
- Clustering
Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services (Free Service) in this course.
You can take Machine Learning with Apache Spark 3.0 using Scala Certificate Course on Eduonix.
- Course rating: 4.7 out of 5.0 ( 5 Ratings total)
- Duration: 7 h 42 m
- Certificate: Certificate on completion
- View course
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