Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.
Explores accelerated data management systems through real-time specialization on heterogeneous hardware and GPU accessing fresh data from CPU memory.
Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.
Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.
Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.
