Eric and Kostas from The Data Stack Show chat with Pete Goddard, the CEO of Deephaven, Arjun Narayan, co-founder and CEO of Materialize, Jeff Chao, a staff engineer at Stripe, and Ashley Jeffs, a software engineer at Benthos. Together they discuss batch versus streaming, transitioning from traditional data methods, and define “streaming ETL” as they push for simplicity across the board.
Conquering real-time data is the future of data science - but when it comes to AI, there are several roadblocks to overcome before you get it right. In his article published in Venture Beat, Chip Kent outlines strategies that put you on the easy path towards real-time AI.
If you're a data scientist, and you're not using real-time data yet, you will be soon. In this episode of the Data Science at Home podcast, "Streaming data with ease," Chip Kent and Francesco Gadaleta talk about the imperative of working with real-time data and the challenges that go along with it. A real-time stream by definition is constantly growing - there is no end to your data. Chip and Francesco discuss useful tools and best practices for data scientists and engineers to help navigate this new territory.
In this article from Inside Big Data, our CEO, Pete Goddard, talks about Deephaven's Python Package MVP tournament, what the match-ups say about the Python community's preferences, and the popularity of Python in data science in general.
Tammy Xu for Built In magazine interviewed several developers and technical writers, including our own dev-rel engineer Amanda Martin, to discuss the challenges of writing useful software documentation. In particular, Martin highlights the need to cater to users of different levels as well as continually maintaining working examples so users never feel frustrated.
Listen to the Contributor podcast, where Eric Anderson sits down with our CEO Pete Goddard to talk about Deephaven's open-core query engine built for real-time streams and batch data. Hear our origin story: for several years, Deephaven Enterprise has provided a necessary real-time data infrastructure in the finance world. Realizing how useful this technology could be in a wider variety of verticals, Deephaven Community Core was born.
Tune in to the Data Engineering Podcast as they interview our CEO, Pete Goddard, about how Deephaven's real-time query engine is engineered to allow users to effortlessly work across streaming and static data in their preferred language.
In this article from Inside Big Data, Pete Goddard, CEO and co-founder of Deephaven Data Labs, paints a new picture of data: one of a synchronized group effort rather than a relay race of individual, siloed teams. He explains how the “baton pass” method of working with data – inherently full of risks and slow-downs – has evolved but not quite met the needs of modern data teams.
Pete then outlines how de-compartmentalizing data use cases can lead to a new understanding that data is changing and exists on a continuum of time. When all data problems are viewed through the lens of how data needs to meet software, and diverse teams are brought together to work in tandem on these interesting problems, we can create one frictionless data world free from the current limits of intermediation.
Analytics Insight has engaged in an exclusive interview with Pete Goddard, CEO of Deephaven Data Labs.
Data analytics software tools enable businesses to analyze vast stores of data for great competitive advantage. These platforms can mine data that tracks a diverse array of business activity from current sales to historic inventory, based on data scientists’ queries. Deephaven, a data software company, developed its initial version of its engine as an in-house product at a quantitative hedge fund. Its newest iteration, Community Core, provides the same easy-to-use tools and high-performance in an open product.
Deephaven Data Labs has built a platform used by financial services organizations to operationalize real-time data for queries and historical reporting of financial and client data.
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