Apache Spark

This course introduces students to foundational IT concepts required for success in modern data environments. Topics include computer hardware, networking basics, operating systems, and cloud computing fundamentals. Learners explore how software systems interact with data storage and processing components. The course also introduces key terminology used throughout data engineering and analytics workflows. By the end of the course, students will understand how IT infrastructure supports distributed data applications in Spark environments.

Students explore the evolution of big data technologies and the growing need for distributed processing. The course introduces Apache Spark as a powerful engine for large-scale data analysis, comparing it with MapReduce and Hadoop. Learners gain an understanding of Spark’s components, architecture, and application in business and scientific environments. Key concepts such as in-memory computation and scalability are emphasized. By the end, students will be familiar with the big data ecosystem and Spark’s role within it.

This course introduces the two primary programming languages used in Apache Spark: Scala and Python. Students will learn basic syntax, data structures, and functional programming concepts relevant to distributed data processing. Labs focus on writing Spark jobs and manipulating data using both languages. Emphasis is placed on practical applications such as Spark shell scripting and Jupyter-based notebooks. By course completion, students will be comfortable using Scala and Python to interact with Spark.

Students dive deep into Spark Core and learn to work with Resilient Distributed Datasets (RDDs), the foundational abstraction in Spark. Topics include RDD transformations, actions, and persistence. The course explores distributed computing principles including fault tolerance and lazy evaluation. Learners gain hands-on experience creating, manipulating, and debugging RDDs in both Python and Scala. This course builds a solid base for more advanced Spark modules involving structured and streaming data.

This course focuses on structured data processing using Spark SQL and the DataFrame API. Students learn how to execute SQL queries on distributed datasets, create temporary views, and interact with external data sources. Topics include schema inference, query optimization, and integrating SQL with Spark programming. Labs explore reading and writing data in JSON, Parquet, and ORC formats. By the end of the course, students will be able to process structured data efficiently at scale.

Building on prior SQL knowledge, this course explores deeper integration of Spark with enterprise data sources. Students will connect Spark with relational databases, NoSQL systems, and cloud data warehouses. Topics include JDBC connectivity, Apache Hive integration, and streaming SQL pipelines. Emphasis is placed on unified batch and streaming workflows. The course prepares learners to use Spark as a bridge across diverse data ecosystems.

Students learn to improve the speed and efficiency of Spark applications by applying best practices in performance tuning. Topics include memory management, shuffling, caching, partitioning, and Spark UI diagnostics. Real-world scenarios demonstrate how small changes can lead to significant performance improvements. Learners will analyze and optimize job execution plans. By the end of the course, students will be able to diagnose performance bottlenecks and enhance scalability.

This course introduces Spark’s machine learning and streaming libraries. Students will use Spark MLlib to build pipelines for classification, regression, and clustering. Spark Streaming concepts such as DStreams and microbatching are also covered. The course emphasizes processing of real-time data from sources like Kafka and socket streams. Students will complete labs that simulate live data ingestion and model training.

Students focus on developing end-to-end machine learning models using Spark’s MLlib library. Topics include data preprocessing, feature engineering, model selection, evaluation, and tuning. Learners will implement algorithms such as decision trees, logistic regression, and K-means clustering. Emphasis is placed on pipeline construction and model persistence. This course prepares students to build scalable ML workflows in Spark environments.

Students explore how Apache Spark is deployed and managed in cloud environments such as AWS, Azure, and Databricks. The course covers cloud-native features like autoscaling, job scheduling, and data lake integration. Learners will explore Spark on Kubernetes and serverless architectures. Security, compliance, and enterprise data governance topics are also discussed. Students will complete labs that simulate enterprise cloud deployment scenarios.

This course focuses on building robust data pipelines using Spark as the core processing engine. Students will design ETL workflows that extract, transform, and load large datasets from various sources. Topics include workflow orchestration, schema evolution, error handling, and monitoring. The course emphasizes production-readiness and real-time performance. By course completion, students will be prepared to manage Spark jobs in scalable, enterprisegrade environments.

This course provides targeted preparation for the Databricks and Cloudera Apache Spark certification exams. Students review core exam topics including Spark Core, SQL, MLlib, and deployment techniques. Practice exams, coding challenges, and lab reviews reinforce learning. Emphasis is placed on test-taking strategies and scenariobased problem solving. The course ensures students are fully prepared and confident in achieving certification success.

The capstone project allows students to design, build, and deploy a full-scale data pipeline using Apache Spark. Students will define project goals, ingest and clean data, and perform structured queries or machine learning tasks. Real-world datasets are used to simulate industry requirements. Each student will document their architectural choices and performance outcomes. This final course demonstrates applied knowledge and serves as a portfolio quality deliverable for potential employers.