About This Course
When data becomes too large or moves too quickly to be processed by a single computer, traditional databases fail. Big Data Analytics focuses on using distributed networks of computers (clusters) to store and process terabytes or petabytes of data simultaneously.
In this course, you will dive into the ecosystem of Big Data tools. You will learn to build scalable data pipelines, process both batch and real-time streaming data, and deploy big data solutions on modern cloud platforms to drive enterprise analytics.
Skills You Will Gain
Course Syllabus
Module 1: Introduction to Distributed Computing
Understand the fundamentals of Big Data architectures. Explore the Hadoop ecosystem, understand the limitations of vertical scaling, and learn how clustering allows for massive horizontal scale.
Module 2: HDFS and MapReduce
Dive into the Hadoop Distributed File System (HDFS). Learn how large files are split across nodes for fault tolerance, and write MapReduce jobs in Python/Java to process data locally on each node.
Module 3: Apache Spark Fundamentals
Move beyond MapReduce to high-speed, in-memory processing. Use PySpark to write complex data transformations using Resilient Distributed Datasets (RDDs) and the Spark SQL DataFrame API.
Module 4: Real-Time Data Streaming
Handle high-velocity data. Learn the publish-subscribe messaging model using Apache Kafka, and consume real-time streams using Spark Structured Streaming to build live dashboards.
Module 5: Big Data in the Cloud
Transition from on-premise to the cloud. Deploy and manage Big Data clusters on demand using services like AWS EMR (Elastic MapReduce) or Google Cloud Dataproc for scalable data engineering.