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Updated in [February 21st, 2023]
What does this course tell?
(Please note that the following overview content is from the original platform)
Apache Spark 3 - Beyond Basics and Cracking Job Interviews | Course Introduction.
Spark Cluster and Runtime Architecture.
Spark Submit and Some Important Options.
Deploy Modes - Client and Cluster mode.
Spark Jobs - Stage, Shuffle, Task, Slots.
Spark SQL Engine and Query Planning.
Lets Practice - Quiz 1 Solution Video.
Lets Practice - Quiz 2 Solution Video.
Spark Memory Allocation.
Spark Memory Management.
Spark Adaptive Query Execution.
Spark AQE Dynamic Join Optimization.
Handling Data Skew in Spark Joins.
Spark Dynamic Partition Pruning.
Data Caching in Spark.
Repartition and Coalesce.
Dataframe Hints.
Broadcast Variables.
Accumulators.
Speculative Execution.
Dynamic Resource Allocation.
Spark Schedulers.
Lets Practice Quiz 3 Solution Video.
Lets Practice Quiz 4 Solution Video.
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
This course is designed to teach Apache Spark 3 and how to ace job interviews related to it. It covers topics related to Spark cluster and runtime architecture, Spark submit and deploy modes, Spark jobs, Spark SQL Engine and Query Planning, Memory Allocation, Memory Management, Adaptive Query Execution, Dynamic Join Optimization, Data Skew and Partition Pruning, Data Caching, Repartition and Coalesce, broadcast variables, accumulators and dynamic resource allocation.
Possible Development Paths: Learners of this course can develop their skills in Apache Spark 3 and related topics to become a data engineer, data scientist, or software engineer. They can also use their knowledge to pursue a career in big data analytics, machine learning, or artificial intelligence.
Learning Suggestions: Learners should supplement their knowledge of Apache Spark 3 with related topics such as Hadoop, Python, Java, Scala, and SQL. They should also practice their skills with hands-on exercises and quizzes. Additionally, they should stay up to date with the latest developments in the field by reading blogs and attending conferences.
[Applications]
After completing this course, students can apply their knowledge of Apache Spark 3 to develop and deploy applications in a distributed environment. They can also use the knowledge gained to ace job interviews related to Apache Spark 3. Additionally, they can use the techniques learned in this course to optimize their Spark applications for better performance.
[Career Paths]
1. Apache Spark Developer: Apache Spark Developers are responsible for developing and maintaining applications using Apache Spark. They must have a strong understanding of the Spark architecture and be able to write efficient code to optimize performance. They must also be able to troubleshoot and debug any issues that arise. Developing trends for this position include the use of machine learning and deep learning frameworks to build more complex applications, as well as the use of cloud-based solutions to scale applications.
2. Apache Spark Data Engineer: Apache Spark Data Engineers are responsible for designing and developing data pipelines and data warehouses using Apache Spark. They must have a strong understanding of the Spark architecture and be able to write efficient code to optimize performance. They must also be able to troubleshoot and debug any issues that arise. Developing trends for this position include the use of machine learning and deep learning frameworks to build more complex data pipelines, as well as the use of cloud-based solutions to scale data warehouses.
3. Apache Spark Architect: Apache Spark Architects are responsible for designing and developing distributed systems using Apache Spark. They must have a strong understanding of the Spark architecture and be able to write efficient code to optimize performance. They must also be able to troubleshoot and debug any issues that arise. Developing trends for this position include the use of machine learning and deep learning frameworks to build more complex distributed systems, as well as the use of cloud-based solutions to scale applications.
4. Apache Spark Consultant: Apache Spark Consultants are responsible for providing advice and guidance to clients on the best way to use Apache Spark. They must have a strong understanding of the Spark architecture and be able to provide advice on how to optimize performance. They must also be able to troubleshoot and debug any issues that arise. Developing trends for this position include the use of machine learning and deep learning frameworks to build more complex applications, as well as the use of cloud-based solutions to scale applications.
