Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
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 Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Covers data stream processing concepts, focusing on Apache Kafka and Spark Streaming integration, event time management, and project implementation guidelines.
Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.
Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Covers the fundamentals of data stream processing, including real-time insights, industry applications, and practical exercises on Kafka and Spark Streaming.
