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.
Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Explores event time vs. processing time, stream processing operations, stream-stream joins, and handling late/out-of-order data in data stream processing.
Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
