Target Audience for Cloudera Data Engineering: Developing Applications with Apache Spark
The Cloudera Data Engineering course is designed for professionals seeking expertise in Apache Spark and big data ecosystems.
Target Job Roles and Audience:
- Data Engineers
- Software Developers with a focus on big data processing
- Apache Spark Developers
- Big Data Architects
- IT Professionals transitioning into big data roles
- Data Scientists interested in engineering large-scale data processes
- System Administrators managing Hadoop and Spark environments
- Database Administrators expanding skills to include HDFS and big data platforms
- DevOps Engineers involved in deployment and management of big data applications
- Technical Project Managers overseeing big data projects
- Graduates and Academics pursuing a career in big data technologies
- Technical Consultants providing solutions for distributed computing challenges
Learning Objectives - What you will Learn in this Cloudera Data Engineering: Developing Applications with Apache Spark?
Course Learning Outcomes and Concepts Overview
This course equips students with hands-on experience in developing applications using Apache Spark, focusing on core competencies of data processing, analysis, and persistence in distributed systems.
Learning Objectives and Outcomes
- Gain familiarity with interactive data exploration using Apache Zeppelin notebooks.
- Understand HDFS architecture, components, and how to perform basic file operations on HDFS.
- Learn the fundamentals of YARN and how it manages resources in a cluster.
- Explore the evolution of distributed processing and the role of disk, memory, and GPUs.
- Develop proficiency in working with Resilient Distributed Datasets (RDDs) and DataFrames for scalable data processing.
- Integrate Spark with Hive for enhanced data querying and analysis capabilities.
- Master data visualization techniques using Zeppelin for insightful analytics and collaboration.
- Address distributed processing challenges such as shuffle operations, data skew, and ordering.
- Write, configure, and run Spark applications effectively, understanding different deployment modes and the Spark Application Web UI.
- Implement structured streaming applications and understand how to process streaming data in real-time.
- Work with Apache Kafka for message processing, learning to scale and manage Kafka clusters.
- Perform aggregations and joins on streaming DataFrames, leveraging Spark's structured streaming.
- For Scala users, learn how to work with Datasets in Scala for type-safe data processing.
