Using Kafka, Spark, and Cassandra to Solve Time Series Problems
As it becomes easier to create data, we are now faced with the need to collect and analyze at scales never seen before. In this practical video course, Apache Cassandra evangelist Patrick McFadin shows how to solve time-series data problems with technologies from Team Apache: Kafka, Spark and Cassandra. To learn how to work with these technologies, you'll work with an example weather collection network and the challenges it can produce.
Through the course of this video, you'll investigate:
Kafka: get your data under control by handling real-time data feeds with this message broker
Spark: learn how this parallel processing framework can quickly and efficiently analyze massive amounts of data
Spark Streaming: perform effective stream analysis by ingesting data in micro-batches
Cassandra: understand how this distributed database works by examining use cases where scaling and uptime are critical
Cassandra Query Language (CQL): use this language to navigate your data models, from creating tables to inserting and selecting data
Spark and Cassandra: combine these powerful tools to perform expressive analytics over large volumes of data
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Prior to DataStax, he was Chief Architect at
Hobsons, an education services company.
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