The tutorial will include a live demo of the full project on Cloudera's QuickStart VM. The code for the demo is available on GitHub. Download it here to follow along.
Implementing a scalable, low-latency architecture requires understanding a broad range of frameworks, such as Kafka, HBase, HDFS, Flume, Spark, Spark Streaming, and Impala, among many others. The good news is that there’s an abundance of resources—books, websites, conferences, etc.—for gaining a deep understanding of these related projects. The bad news is there’s still a scarcity of information on how to integrate these components to implement complete solutions.
Jonathan Seidman, Ted Malaska, Gwen Shapira, and Mark Grover walk participants through building a fraud-detection system, using an end-to-end case study to provide a concrete example of how to architect and implement real-time systems via Apache Hadoop components like Kafka, HBase, Impala, and Spark. They cover best practices and considerations for architecting real-time applications to give developers, architects, or project leads who are already knowledgeable about Hadoop or similar distributed data processing systems more insight into how they can be leveraged to implement real-world applications.
Jonathan Seidman is a software engineer on the cloud team at Cloudera. Previously, he was a lead engineer on the big data team at Orbitz Worldwide, helping to build out the Hadoop clusters supporting the data storage and analysis needs of one of the most heavily trafficked sites on the internet. Jonathan is a cofounder of the Chicago Hadoop User Group and the Chicago Big Data Meetup and a frequent speaker on Hadoop and big data at industry conferences such as Hadoop World, Strata, and OSCON. Jonathan is the coauthor of Hadoop Application Architectures from O’Reilly.
Ted Malaska is a group technical architect on the Battle.net team at Blizzard, helping support great titles like World of Warcraft, Overwatch, and HearthStone. Previously, Ted was a principal solutions architect at Cloudera, helping clients find success with the Hadoop ecosystem, and a lead architect at the Financial Industry Regulatory Authority (FINRA). He has also contributed code to Apache Flume, Apache Avro, Apache Yarn, Apache HDFS, Apache Spark, Apache Sqoop, and many more. Ted is a coauthor of Hadoop Application Architectures, a frequent speaker at many conferences, and a frequent blogger on data architectures.
Gwen Shapira is a system architect at Confluent, where she helps customers achieve success with their Apache Kafka implementations. She has 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. Gwen currently specializes in building real-time reliable data-processing pipelines using Apache Kafka. Gwen is an Oracle Ace Director, the coauthor of Hadoop Application Architectures, and a frequent presenter at industry conferences. She is also a committer on Apache Kafka and Apache Sqoop. When Gwen isn’t coding or building data pipelines, you can find her pedaling her bike, exploring the roads and trails of California and beyond.
Mark Grover is a product manager at Lyft. Mark is a committer on Apache Bigtop, a committer and PPMC member on Apache Spot (incubating), and a committer and PMC member on Apache Sentry. He has also contributed to a number of open source projects, including Apache Hadoop, Apache Hive, Apache Sqoop, and Apache Flume. He is a coauthor of Hadoop Application Architectures and wrote a section in Programming Hive. Mark is a sought-after speaker on topics related to big data. He occasionally blogs on topics related to technology.
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