Partitioned tables can help parallelize queries across multiple threads and generally improve performance when used in the right context. Learn more: https://t.co/eSajQHqWNB #Deephaven #LiveDataframes #PartitionedTables #RealTimeData

Static dataframes are DEAD! You can enjoy spooky fast analytics with Deephaven's LIVE dataframes. Enjoy working with a familiar dataframe format that has come to to life! https://t.co/2h9PdOFlnb #Deephaven #LiveDataframes

Deephaven to serves as the only necessary tool for all layers in a lambda architecture deployment, solving many of the challenges inherent in what is normally considered a complex design. Learn More: https://t.co/lgQybfttK2 #Deephaven #LambdaArchitecture #RealTimeData

Dataframes brought to life! Deephaven’s ‘LIVE’ Dataframes track changes (the "deltas”) and you inherit updates a bit magically. https://t.co/F8LCi0NtEk #Deephaven #LivingDataframes #LiveDataframes

Composability allows you to break big use cases into smaller ones and snap building blocks together. Analytics as Legos. Learn More: https://t.co/foj1cmcdjn #Deephaven #Composability #LiveDataframes

Deephaven is designed to handle big ticking data. The web-client-ui front-end is no exception. We want to interact with as large a ticking data set as possible without compromising the user experience. Learn more: https://t.co/yjH1T25qID #BigData #Deephaven #StreamingData

The Benchmark framework provides support for gathering performance measurements and statistics for operations on tabular data. Try it: https://t.co/PnblB0zzvb #Deephaven #LiveDataframes #Benchmarks

Deephaven tables are implemented using data structures that lend themselves to efficient sharing and incremental updating. Learn more: https://t.co/OAYHFvezFK #Deephaven #LiveDataframes #TickingTables #IncrementalUpdateModel

This Barrage project is the wire-format extension of arrow-flight to support ticking data sets. Learn more: https://t.co/wAuDFYz0Li #Deephaven #Barrage #Github #OpenSource

Deephaven offers A turnkey approach to create, use, and share LIVE dashboards! learn more: https://t.co/nMbMz6dQHw #Deephaven #LiveDashboards #LiveDataframes

The Deephaven CSV Library is a high-performance, column-oriented, type inferencing CSV parser. Organize data into columns rather than rows, which allows for more efficient storage and retrieval. Learn more: https://t.co/PSl4ogvSay #Deephaven #CSV #Github

** Lambda Architecture Without Compromise ** Deephaven serves as the the ONLY necessary tool for all layers in a lambda architecture deployment! Learn more: https://t.co/ZzLYCgSFA1 #Deephaven #LambdaArchitecture #DataProcessing #DataStack

The Deephaven user interface (GUI) offers a comprehensive suite of advanced tools to manipulate and transform data and tables with ease. Learn miore: https://t.co/5osGkdQqQe #Deephaven #UI #LiveDataframes

Deephaven Community Core is a real-time, time-series, column-oriented analytics engine with relational database features. Try it now: https://t.co/JAqEt58vg7 #Deephaven #DeephavenCore #OpenSource #GitHub

We have designed and implemented a unified table API that offers the same functionality and semantics for both static and dynamic data sources, albeit with additional correctness considerations in the dynamic case. https://t.co/Zcjrut9vLX #Deephaven #LiveDataframes

Deephaven Community Core is a real-time, time-series, column-oriented analytics engine with relational database features. Queries can seamlessly operate upon both historical and real-time data. https://t.co/pgWrkvPFvw #Deephaven #LiveDataframes #Github #OpenSource

Using the R client API via R Studio provides a familiar interface for interacting with Deephaven as a live (real-time) backend or a compute cluster for pre-processing historical data. Learn more: https://t.co/bVi2ZV3dBr #Deephaven #LiveDataframes #RStudio

The fundamental principle of the Deephaven experience is this: “Deephaven queries are unaware and indifferent to whether the underlying data source is static or streaming.” Learn more: https://t.co/fp0DXKk5LV #Deephaven #LiveDataframes

Deephaven Live Dataframes integrate with Plotly Express library! Providing ticking plots via simple Python sscripts. https://t.co/J9U1baEWH8 #Deephaven #LiveDataframes #PlotlyExpress

Data in the real world is constantly in flux. Deephaven’s query engine provides a scalable solution to some of the hardest problems in this area, freeing compute and engineering resources to address domain-specific issues. https://t.co/XuNASpM8fu #Deephaven #LiveDataframes