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Janani Ravi
This course will teach you how to optimize the performance of Spark clusters on Azure Databricks by identifying and mitigating various issues such as data ingestion problems and performance bottlenecks
Read more
This course will teach you how to optimize the performance of Spark clusters on Azure Databricks by identifying and mitigating various issues such as data ingestion problems and performance bottlenecks
Read more
This course will teach you how to optimize the performance of Spark clusters on Azure Databricks by identifying and mitigating various issues such as data ingestion problems and performance bottlenecks
The Apache Spark unified analytics engine is an extremely fast and performant framework for big data processing. However, you might find that your Apache Spark code running on Azure Databricks still suffers from a number of issues. These could be due to the difficulty in ingesting data in a reliable manner from a variety of sources or due to performance issues that you encounter because of disk I/O, network performance, or computation bottlenecks.
In this course, Optimizing Apache Spark on Databricks, you will first explore and understand the issues that you might encounter ingesting data into a centralized repository for data processing and insight extraction. Then, you will learn how Delta Lake on Azure Databricks allows you to store data for processing, insights, as well as machine learning on Delta tables and you will see how you can mitigate your data ingestion problems using Auto Loader on Databricks to ingest streaming data.
Next, you will explore common performance bottlenecks that you are likely to encounter while processing data in Apache Spark, issues dealing with serialization, skew, spill, and shuffle. You will learn techniques to mitigate these issues and see how you can improve the performance of your processing code using disk partitioning, z-order clustering, and bucketing.
Finally, you will learn how you can share resources on the cluster using scheduler pools and fair scheduling and how you can reduce disk read and write operations using caching on Delta tables.
When you are finished with this course, you will have the skills and knowledge of optimizing performance in Spark needed to get the best out of your Spark cluster.
Register for this course and see more details by visiting:
OpenCourser.com/course/98o0r7/optimizing
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What's inside
Syllabus
Course Overview
Exploring and Mitigating Data Ingestion Problems
Diagnosing and Mitigating Performance Problems
Optimizing Spark for Performance
Read more
Syllabus
Course Overview
Exploring and Mitigating Data Ingestion Problems
Diagnosing and Mitigating Performance Problems
Optimizing Spark for Performance
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Janani Ravi, who are recognized for their work in this field
Explores a range of issues in relation to data ingestion, which is standard practice for data analysis
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Optimizing Apache Spark on Databricks with these
activities:
Review Data Analytics Fundamentals
Show steps
Refreshing your foundational knowledge can enhance your comprehension of the course.
Browse courses on
Data Analytics
Show steps
-
Review introductory materials on data analytics concepts.
-
Summarize key data structures, algorithms, and techniques used in data processing.
Read 'High-Performance Spark'
Show steps
Gain insights into best practices and advanced techniques for Apache Spark.
View
Learning Spark: Lightning-Fast Big Data Analysis
on Amazon
Show steps
-
Obtain a copy of 'High-Performance Spark'.
-
Read and understand the foundational chapters on Spark architecture and performance.
-
Focus on chapters related to specific performance optimizations.
Compile a Glossary of Spark Concepts
Show steps
By organizing definitions, you can improve your understanding of Apache Spark.
Show steps
-
Identify key concepts and terms used in the course materials.
-
Search for definitions and explanations from reputable sources.
-
Create a structured document or spreadsheet to organize the definitions.
-
Review and update the glossary as you progress through the course.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Experiment with different performance tuning techniques
Show steps
The more you experiment with different techniques, the better you will understand how to improve the performance of your Spark code.
Show steps
-
Choose a technique to try
-
Implement the technique
-
Measure the results
-
Adjust the technique as needed
Solve Spark Performance Optimization Puzzles
Show steps
Engaging in problem-solving exercises will strengthen your optimization skills.
Show steps
-
Identify online platforms or resources that provide Spark optimization puzzles.
-
Attempt to solve the puzzles independently, researching additional concepts as needed.
-
Compare your solutions to others and learn from different approaches.
-
Document your solutions and identify areas for improvement.
Participate in Online Spark Optimization Study Group
Show steps
Collaborating with others can provide valuable perspectives and support.
Show steps
-
Join or create an online study group focused on Spark optimization.
-
Set regular meeting times to discuss course materials, share experiences, and work through problems.
-
Contribute actively to the discussions and offer help to fellow group members.
Write a blog post about performance tuning techniques
Show steps
This activity will allow you to think critically about the performance tuning techniques you have learned and how to explain them to others.
Browse courses on
Performance Optimization
Show steps
-
Choose a topic to write about
-
Research the topic
-
Write the blog post
-
Publish the blog post
-
Promote the blog post
Explore Advanced Spark Optimization Techniques
Show steps
Delving into advanced tutorials will broaden your knowledge of optimization strategies.
Show steps
-
Identify reputable sources for advanced Spark optimization tutorials.
-
Select tutorials that focus on specific performance pitfalls or bottlenecks.
-
Follow the tutorials step-by-step, implementing the techniques in your own projects.
-
Share your experiences and insights in online forums or discussion groups.
Implement an End-to-End Spark Optimization Project
Show steps
Undertaking a practical project will solidify your understanding and build valuable experience.
Show steps
-
Identify a real-world dataset and define a specific optimization goal.
-
Design and implement a Spark application that incorporates various optimization techniques.
-
Monitor and analyze the performance of your application.
-
Refine and improve your optimization strategies based on the results.
-
Document your project and share your findings with the community.
Contribute to Open-Source Spark Optimization Projects
Show steps
Engage in real-world optimization challenges and make meaningful contributions to the community.
Show steps
-
Identify open-source projects related to Apache Spark optimization.
-
Review the project documentation and familiarize yourself with the codebase.
-
Propose and implement improvements or new optimization techniques.
-
Test and validate your contributions through pull requests.
Review Data Analytics Fundamentals
Show steps
Refreshing your foundational knowledge can enhance your comprehension of the course.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Review introductory materials on data analytics concepts.
- Summarize key data structures, algorithms, and techniques used in data processing.
Read 'High-Performance Spark'
Show steps
Gain insights into best practices and advanced techniques for Apache Spark.
View
Learning Spark: Lightning-Fast Big Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'High-Performance Spark'.
- Read and understand the foundational chapters on Spark architecture and performance.
- Focus on chapters related to specific performance optimizations.
Compile a Glossary of Spark Concepts
Show steps
By organizing definitions, you can improve your understanding of Apache Spark.
Show steps
Show steps
Show steps
- Identify key concepts and terms used in the course materials.
- Search for definitions and explanations from reputable sources.
- Create a structured document or spreadsheet to organize the definitions.
- Review and update the glossary as you progress through the course.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Experiment with different performance tuning techniques
Show steps
The more you experiment with different techniques, the better you will understand how to improve the performance of your Spark code.
Show steps
Show steps
Show steps
- Choose a technique to try
- Implement the technique
- Measure the results
- Adjust the technique as needed
Solve Spark Performance Optimization Puzzles
Show steps
Engaging in problem-solving exercises will strengthen your optimization skills.
Show steps
Show steps
Show steps
- Identify online platforms or resources that provide Spark optimization puzzles.
- Attempt to solve the puzzles independently, researching additional concepts as needed.
- Compare your solutions to others and learn from different approaches.
- Document your solutions and identify areas for improvement.
Participate in Online Spark Optimization Study Group
Show steps
Collaborating with others can provide valuable perspectives and support.
Show steps
Show steps
Show steps
- Join or create an online study group focused on Spark optimization.
- Set regular meeting times to discuss course materials, share experiences, and work through problems.
- Contribute actively to the discussions and offer help to fellow group members.
Write a blog post about performance tuning techniques
Show steps
This activity will allow you to think critically about the performance tuning techniques you have learned and how to explain them to others.
Browse courses on
Performance Optimization
Show steps
Show steps
Show steps
- Choose a topic to write about
- Research the topic
- Write the blog post
- Publish the blog post
- Promote the blog post
Explore Advanced Spark Optimization Techniques
Show steps
Delving into advanced tutorials will broaden your knowledge of optimization strategies.
Show steps
Show steps
Show steps
- Identify reputable sources for advanced Spark optimization tutorials.
- Select tutorials that focus on specific performance pitfalls or bottlenecks.
- Follow the tutorials step-by-step, implementing the techniques in your own projects.
- Share your experiences and insights in online forums or discussion groups.
Implement an End-to-End Spark Optimization Project
Show steps
Undertaking a practical project will solidify your understanding and build valuable experience.
Show steps
Show steps
Show steps
- Identify a real-world dataset and define a specific optimization goal.
- Design and implement a Spark application that incorporates various optimization techniques.
- Monitor and analyze the performance of your application.
- Refine and improve your optimization strategies based on the results.
- Document your project and share your findings with the community.
Contribute to Open-Source Spark Optimization Projects
Show steps
Engage in real-world optimization challenges and make meaningful contributions to the community.
Show steps
Show steps
Show steps
- Identify open-source projects related to Apache Spark optimization.
- Review the project documentation and familiarize yourself with the codebase.
- Propose and implement improvements or new optimization techniques.
- Test and validate your contributions through pull requests.
Career center
Learners who complete Optimizing Apache Spark on Databricks will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers implement the design and architecture of big data systems, which is typically done with technologies like Apache Spark. This course, Optimizing Apache Spark on Databricks, is ideal for Data Engineers who wish to advance their skills to manage and solve complex data problems.
Data Analyst
Data Analyst
Data Analysts use Apache Spark to analyze and interpret large datasets to make better decisions. This course, Optimizing Apache Spark on Databricks, can help Data Analysts mitigate challenges like data ingestion problems and performance bottlenecks.
Data Scientist
Data Scientist
Data Scientists build complex machine learning models that are used to solve complex big data problems. This course, Optimizing Apache Spark on Databricks, helps Data Scientists quickly learn to mitigate issues with data ingestion and performance to improve the speed and efficiency of their models.
Software Engineer
Software Engineer
Software Engineers build and maintain software systems, including big data architectures. This course, Optimizing Apache Spark on Databricks, could be useful for Software Engineers to learn essential methods for optimizing cluster performance.
Database Administrator
Database Administrator
Database Administrators maintain complex data systems, which increasingly include big data clusters. This course, Optimizing Apache Spark on Databricks, can help Database Administrators learn techniques to improve the performance of their systems.
Data Architect
Data Architect
Data Architects design and build data management solutions, which often include big data systems like Apache Spark. This course, Optimizing Apache Spark on Databricks, may be useful for Data Architects as they learn to solve complex problems related to data ingestion and performance.
Business Analyst
Business Analyst
Business Analysts gather and analyze data to help businesses make better decisions. This course, Optimizing Apache Spark on Databricks, may be useful for Business Analysts to understand how big data is processed and how to improve performance.
IT Manager
IT Manager
IT Managers oversee the implementation and maintenance of technology systems, including big data systems. This course, Optimizing Apache Spark on Databricks, can help IT Managers to make better decisions about how to manage these systems and solve problems related to performance.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions, which often include big data systems. This course, Optimizing Apache Spark on Databricks, may be helpful for Cloud Architects to better understand big data technologies.
Systems Analyst
Systems Analyst
Systems Analysts identify and solve problems with computer systems, including big data systems. This course, Optimizing Apache Spark on Databricks, may be useful for Systems Analysts to learn techniques for solving performance-related problems.
Project Manager
Project Manager
Project Managers oversee the implementation of technology projects, including big data projects. This course, Optimizing Apache Spark on Databricks, can help Project Managers to make better decisions about how to manage big data projects and solve problems related to performance.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends and patterns that can help businesses make better decisions. This course, Optimizing Apache Spark on Databricks, may be useful for Business Intelligence Analysts to understand how big data is processed and how to improve performance.
Web Developer
Web Developer
Web Developers design and develop websites, which can include features that use big data. This course, Optimizing Apache Spark on Databricks, may be helpful for Web Developers to understand how big data is processed and how to improve performance.
Computer Programmer
Computer Programmer
Computer Programmers write code to implement software systems, including big data systems. This course, Optimizing Apache Spark on Databricks, may be useful for Computer Programmers to learn techniques for solving performance-related problems.
Software Tester
Software Tester
Software Testers test software systems, including big data systems. This course, Optimizing Apache Spark on Databricks, may be useful for Software Testers to learn techniques for testing big data systems.
For more career information including salaries, visit:
OpenCourser.com/course/98o0r7/optimizing
Reading list
We've selected five books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Optimizing Apache Spark on Databricks.
Is the official guide to Apache Spark, written by the creators of the framework. It covers all aspects of Spark, from installation and configuration to advanced topics such as machine learning and graph processing.
Provides a comprehensive guide to advanced analytics with Apache Spark. It covers a wide range of topics, including machine learning, graph analytics, and stream processing. The book is written by a team of experts from the Apache Spark community, and it valuable resource for anyone who wants to learn how to use Spark for advanced analytics.
Save
Provides a comprehensive overview of Apache Spark, from its basic concepts to advanced techniques. It valuable resource for both beginners and experienced Spark users.
Provides practical guidance on how to optimize Apache Spark performance. It covers topics such as data partitioning, scheduling, and caching.
Practical guide to using disk partitioning to optimize the performance of Apache Spark applications. It covers topics such as data ingestion, data cleaning, feature engineering, model training, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/98o0r7/optimizing
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Time
120 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
Taught by Janani Ravi, who are recognized for their work in this field
Explores a range of issues in relation to data ingestion, which is standard practice for data analysis
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