A large number of distributed applications requires continuous and timely processing of
information as it flows from the periphery to the center of the system. Examples include …

As the data management field has diversified to consider settings in which queries are
increasingly complex, statistics are less available, or data is stored remotely, there has been …

Distributed stream processing systems need to support stateful processing, recover quickly
from failures to resume such processing, and reprocess an entire data stream quickly. We …

Many" big data" applications must act on data in real time. Running these applications at
ever-larger scales requires parallel platforms that automatically handle faults and stragglers …

MillWheel is a framework for building low-latency data-processing applications that is widely
used at Google. Users specify a directed computation graph and application code for …

Many important “big data” applications need to process data arriving in real time. However,
current programming models for distributed stream processing are relatively low-level, often …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

As users of" big data" applications expect fresh results, we witness a new breed of stream
processing systems (SPS) that are designed to scale to large numbers of cloud-hosted …

Borealis is a second-generation distributed stream processing engine that is being
developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream …

The last 25 years of commercial DBMS development can be summed up in a single phrase:"
One size fits all". This phrase refers to the fact that the traditional DBMS architecture …