Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Apache Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark, that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Mike Percy, Software Engineer at Cloudera
Mike Percy is software engineer currently working at Cloudera on Kudu, a native columnar database for the Hadoop ecosystem. He is also a committer and PMC member on Apache Flume. Prior to joining Cloudera, Mike worked at Yahoo! building a content recommendation system on top of Hadoop and HBase. Mike holds an MS in Computer Science from Stanford University and a BS in Computer Science from the University of California, Santa Cruz.
6:00 - 6:45pm: Registration, Networking
6:45 - 7:00pm: Introductions
7:00 - 7:45pm: Presentation
7:45 - 8:00pm: Q&A session
8:00 - 8:30pm: Networking