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. 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 Apache 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 Apache Kudu (incubating), a new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark and Apache Impala (incubating), that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.