24 Hrs
4818+ Enrollments
Apache Spark Application Performance Tuning
This course is designed to help developers, data engineers, and data scientists optimize the performance of Apache Spark applications. You will learn best practices for tuning Spark applications and gain practical experience with various tools and techniques for improving performance. You will also learn how to diagnose and troubleshoot performance issues in Spark applications.
Apache Spark Application Performance Tuning
Overview of Course
The Apache Spark Application Performance Tuning course teaches you how to optimize Apache Spark applications for better performance, scalability, and efficiency. This course covers various aspects of tuning Spark applications, including memory management, resource allocation, and data serialization.
Course Highlights
Learn how to optimize Spark applications for better performance
Understand the key components of Spark and how they affect performance
Learn how to diagnose and troubleshoot performance issues in Spark applications
Key Differentiators
Personalized Learning with Custom Curriculum
Training curriculum to meet the unique needs of each individual
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Real-world Projects
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Skills You’ll Learn
Gain practical experience with various tools and techniques for tuning Spark applications
Techniques for optimizing Apache Spark applications
Best practices for memory management, resource allocation, and data serialization in Spark
Diagnosing and troubleshooting performance issues in Spark applications
Working with various tools and techniques for improving Spark application performance<br /><br />
Training Options
1-on-1 Training
Access to live online classes- Flexible schedule including weekends
- Hands-on exercises with virtual labs
- Session recordings and learning courseware included
- 24X7 learner support and assistance
- Book a free demo before you commit!
Corporate Training
- Everything in 1-on-1 Training plus
- Custom Curriculum
- Extended access to virtual labs
- Detailed reporting of every candidate
- Projects and assessments
- Consulting Support
- Training aligned to business outcomes
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- Reduce employee turnovers and associated costs, such as recruitment and onboarding expenses.
- Obtain long-term organizational growth and success.
Course Reviews
Curriculum
- RDDs
- DataFrames and Datasets
- Lazy Evaluation
- Pipelining
- Available Formats Overview
- Impact on Performance
- The Small Files Problem
- The Cost of Inference
- Mitigating Tactics
- Recognizing Skew
- Mitigating Tactics
- Catalyst Overview
- Tungsten Overview
- Denormalization
- Broadcast Joins
- Map-Side Operations
- Sort Merge Joins
- Partitioned Tables
- Bucketed Tables
- Impact on Performance
- Skewed Joins
- Bucketed Joins
- Incremental Joins
- Pyspark Overhead
- Scalar UDFs
- Vector UDFs using Apache Arrow
- Scala UDFs
- Caching Options
- Impact on Performance
- Caching Pitfalls
- WXM Overview
- WXM for Spark Developers
- Adaptive Number of Shuffle Partitions
- Skew Joins
- Convert Sort Merge Joins to Broadcast Joins
- Dynamic Partition Pruning
- Dynamic Coalesce Shuffle Partitions
Meet the instructor and learn about the course content and teaching style.- Make informed decisions about whether to enroll in the course or not.
- Get a perspective with a glimpse of what the learning process entails.
Description
Target Audience:
- Developers
- Data Engineers
- Data Scientists
- Anyone who wants to learn how to optimize Apache Spark applications for better performance
Prerequisite:
- Knowledge of Apache Spark and Scala or Python programming languages
- Familiarity with Linux command line
Benefits of the course:
- Gain a deeper understanding of Apache Spark and how it works
- Learn how to optimize Spark applications for better performance
- Gain practical experience with various tools and techniques for tuning Spark applications
- Learn how to diagnose and troubleshoot performance issues in Spark applications
- Boost your career prospects by adding Spark performance tuning skills to your resume
Exam details to pass the course:
- There is no exam for this course. You will receive a certificate of completion after finishing all the modules and quizzes.
Certification path:
- There are no specific certifications needed to learn this course. However, having a certification in Apache Spark can be beneficial.
Career options after doing the course:
- Big Data Engineer
- Data Scientist
- Data Analyst
- Data Architect
- Hadoop Developer
Why should you take this course from Skillzcafe:
Industry-relevant curriculum designed by experts- Practical and hands-on learning experience
- Learn at your own pace with lifetime access to course materials
- 24/7 support and assistance from industry experts
- Certificate of completion to showcase your skills
FAQs
No, this course is designed for intermediate and advanced level learners with prior knowledge of Apache Spark and Scala/Python programming.
You will need a computer with a minimum of 8GB RAM and Linux/MacOS operating system. You will also need to install Apache Spark and Scala/Python on
Spark application performance tuning can significantly improve the efficiency and scalability of Spark applications, enabling them to process larger datasets faster and with lower resource consumption. This can result in lower costs and faster time-to-insight for data-driven applications.
Participants should have prior experience with Apache Spark and basic knowledge of distributed systems, data processing, and programming in either Scala or Python.
This course covers a range of topics related to Spark application performance tuning, including memory management, data serialization, caching, shuffle optimization, parallelism, and troubleshooting performance issues.
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