Apache Spark And Scala
Instructor-led training in Apache Spark And Scala. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
In this course, you will explore the powerful combination of Apache Spark and Scala, two essential tools for big data processing and analytics. You will learn how to harness Spark's capabilities for distributed data processing and how to use Scala to write efficient and scalable applications, making you proficient in handling large datasets.
What you'll learn
- You will be able to set up and configure Apache Spark for your data processing needs.
- You will be able to write Spark applications using the Scala programming language.
- You will be able to perform data transformations and actions using Spark's RDD and DataFrame APIs.
- You will be able to implement Spark SQL for structured data processing.
- You will be able to optimize Spark jobs for performance and resource management.
- You will be able to integrate Apache Spark with various data sources such as HDFS, S3, and databases.
- You will be able to apply machine learning algorithms using Spark MLlib.
- You will be able to troubleshoot and debug Spark applications effectively.
Curriculum
8 modules · outline is indicative and can be tailored to your team.
1Introduction to Apache Spark
- Overview of Big Data and Spark
- Setting up Spark environment
- Understanding Spark architecture and components
2Programming with Scala
- Scala basics: Syntax, data types, and control structures
- Functional programming concepts in Scala
- Collections and pattern matching in Scala
3Working with RDDs
- Creating and manipulating RDDs
- Transformations and actions
- RDD persistence and optimization
4DataFrames and Spark SQL
- Introduction to DataFrames
- Using Spark SQL for data querying
- DataFrame operations and performance tuning
5Spark Streaming
- Understanding stream processing with Spark
- Building streaming applications
- Integrating with data sources like Kafka
6Machine Learning with Spark MLlib
- Introduction to machine learning concepts
- Building and evaluating machine learning models
- Using MLlib for feature extraction and model training
7Performance Tuning and Best Practices
- Job optimization techniques
- Resource management and cluster configuration
- Best practices for writing efficient Spark code
8Project and Case Studies
- Real-world case studies of Spark applications
- Hands-on project to consolidate learning
- Presentation of project results
Prerequisites
Basic programming knowledge is recommended.
Who should attend
This course is ideal for data engineers, data scientists, and software developers looking to enhance their skills in big data processing.
Certification
Frequently asked questions
What is the delivery format of the course?
The course is delivered live-online with interactive sessions.
How long is the course?
The course spans 8 weeks, with weekly live sessions and assignments.
Will I receive a certificate upon completion?
Yes, you will receive an Skilvi course-completion certificate.
Are exam vouchers included in the course?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Basic programming knowledge is recommended.
Can I access recorded sessions if I miss a live class?
Yes, recorded sessions will be available for review after each class.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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