Rapid advancements are causing a dramatic evolution in both the storage and processing capabilities in the open source big data software ecosystem. These advancements include projects like:
Along with the Apache Hadoop platform, these storage and processing systems provide a powerful platform to implement data processing applications on batch and streaming data. While these advancements are exciting, they also add a new array of tools that architects and developers need to understand when architecting solutions with Hadoop.
Using Customer 360 and the IoT as examples, Jonathan Seidman, Mark Grover, and Gwen Shapira explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Impala, Kudu, Spark Streaming, and Spark SQL with Hadoop to enable new forms of data processing and analytics. Along the way, they discuss considerations and best practices for utilizing these components to implement solutions, cover common challenges and how to address them, and provide practical advice for building your own modern, real-time big data architectures.
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, 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.
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’s a committer on Apache Bigtop, a committer and PPMCmember on Apache Spot (incubating), and a committer and PMC member on Apache Sentry. He’s also contributed to a number of open source projects, including Apache Hadoop, Apache Hive, Apache Sqoop, and Apache Flume. He’s 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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