This course serves as an appropriate entry point to learn Apache Spark Programming with Databricks.
Below, we describe each of the four, four-hour modules included in this course.
This course serves as an appropriate entry point to learn Apache Spark Programming with Databricks.
Below, we describe each of the four, four-hour modules included in this course.
Participants should have:
If you do not have one or more of the pre-requisites QA recommends:
This course is designed for:
Introduction to Apache Spark
This course offers essential knowledge of Apache Spark, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.
Developing Applications with Apache Spark
Master scalable data processing with Apache Spark in this hands-on course. Learn to build efficient ETL pipelines, perform advanced analytics, and optimize distributed data transformations using Spark’s DataFrame API. Explore grouping, aggregation, joins, set operations, and window functions. Work with complex data types like arrays, maps, and structs while applying best practices for performance optimization.
Stream Processing and Analysis with Apache Spark
Learn the essentials of stream processing and analysis with Apache Spark in this course. Gain a solid understanding of stream processing fundamentals and develop applications using the Spark Structured Streaming API. Explore advanced techniques such as stream aggregation and window analysis to process real-time data efficiently. This course equips you with the skills to create scalable and fault-tolerant streaming applications for dynamic data environments.
Monitoring and Optimizing Apache Spark Workloads on Databricks
This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.
Introduction to Apache Spark
Developing Applications with Apache Spark
Stream Processing and Analysis with Apache Spark
Monitoring and Optimizing Apache Spark Workloads on Databricks
Experience Apache Spark Programming with Databricks in New Zealand through Bilginç IT Academy's live and interactive virtual classroom environment, accessible from your home, office, or any location. Connect with expert trainers in real time and bring the energy of classroom learning into the digital experience.
Experience Apache Spark Programming with Databricks in a focused classroom environment in New Zealand. Bilginç IT Academy's carefully selected training venues provide a professional setting where delegates can interact directly with expert trainers and peers.
Meet your team's training needs with Bilginç IT Academy's onsite Apache Spark Programming with Databricks in New Zealand solution, delivered at your office or preferred location. Align your team's development with your business goals through a training experience tailored to your organization.
Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.
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