Lecture

Data Stream Processing: Apache Kafka and Spark

Related lectures (46)
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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.
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Analytics on Data at Rest and Data in Motion
Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.
General Introduction to Big Data
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
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Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.
General Introduction to Data Science
Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
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Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Advanced Data Stream Processing Concepts
Explores event time vs. processing time, stream processing operations, stream-stream joins, and handling late/out-of-order data in data stream processing.
Data Wrangling with Hadoop: Storage Formats and Hive
Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.
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