QuasarDB is a scalable timeseries database that was designed to handle the extreme use cases one can find, for example, in market finance. In this talk we will see a couple of design and implementation decisions that were made to deliver the performance QuasarDB today delivers, especially regarding network communications, memory management and real-time aggregation. Part of Time Series Database... Read More

Trying to solve the data riddle purely through the lens of architecture is missing a vital point: The unifying factor across all data is a dependency on time. The ability to capture and factor in time is the key to unlocking real cost efficiencies. Whether it’s streaming sensor data, financial market data, chat logs, emails, SMS or the P&L, each... Read More

Alex Benik is a Partner at Battery Ventures. The talk promises to be buzzword- and jargon-fueled romp through current topics in venture capital. Alex will review some of the basics of the venture-capital industry and cover a number of areas Battery is focused on in its practice investing across enterprise IT and developer tools/platforms. He will also provide some advice... Read More

Analytic workloads in data management systems are dominated by joins, aggregations, scan and filtering costs. In-Memory columnar databases have significantly optimized scans using compressed data formats and SIMD vectorization techniques, but have made little impact to the rest of the query execution plan. The Oracle Database In-Memory (DBIM) Option introduced new SQL execution operators that accelerate a wide range of... Read More

Mainstream adaptively merges the video stream processing of concurrent applications sharing fixed edge resources to maximize aggregate result quality. Mainstream’s approach enables partial-DNN compute sharing among applications using DNNs (deep neural networks) that are fine-tuned from the same base model, decreasing aggregate per-frame compute time. Moreover, since the choice depends on the mix of applications running on an edge node,... Read More

Smooth is a distributed storage system for managing structured time series data at Two Sigma. Smooth's design emphasizes scale, both in terms of size and aggregate request bandwidth, reliability and storage efficiency. It is optimized for large parallel streaming read/write accesses over provided time ranges. Smooth has a clear separation between the metadata and data layers, and supports multiple pluggable... Read More

Andy will regale the team with a discussion of the various research projects that are currently ongoing this semester. He will then muse about various papers that he wants to write within the next year followed by a group discussion. Read More

Distributed consensus algorithms like Paxos and Raft provide an important building block for distributed systems, but there's a lot more that goes into a resilient and scalable distributed database. CockroachDB's key-value layer is built on many independent and overlapping Raft consensus groups. In this talk I'll explain why we built it this way, and some of the expected and unexpected... Read More

Several enterprises have been producing data not only at high volume but also at high velocity. Many daily business operations depend on real-time insights, therefore real-time processing of the data is gaining significance. Hence there is a need for a scalable infrastructure that can continuously process billions of events per day the instant the data is acquired. To achieve real... Read More

Stream processing has become the dominant processing model for monitoring and real-time analytics. Modern Parallel Stream Processing Engines (pSPEs) have made it feasible to increase the performance in both monitoring and analytical queries by parallelizing a query’s execution and distributing the load on multiple workers. A determining factor for the performance of a pSPE is the partitioning algorithm used to... Read More