 Live from San Francisco, it's theCUBE, covering Spark Summit 2017, brought to you by Databricks. Well what an exciting day we've had here at theCUBE. We've been at Spark Summit 2017 talking to partners, to customers, to founders, technologists, data scientists. It's been a load of information, right? Yeah, overload of information. Well George, you've been here in the studio with me talking with a lot of the guests. I'm going to ask you to maybe recap some of the top things you've heard today for our guests. Okay, so, well Databricks laid down sort of three themes that they wanted folks to take away. Deep learning, structured streaming, and serverless. Now, deep learning is not entirely new to Spark, but they've dramatically improved their support for it. I think going beyond the frameworks that were written specifically for Spark, like Deep Learning 4J and BigDL by Intel, and now like TensorFlow, which is the open source framework from Google, has gotten much better support. Structured streaming, it was not clear like sort of how much more news we were going to get because it's been sort of talked about for 18 months. And they really, really surprised a lot of people, including me, where they took essentially the processing time for an event or a small batch of events down to one millisecond, whereas before it was in the hundreds, if not higher. And that changes the type of apps you can build. And also the Databricks guys had coined the term continuous apps, which means they operate on the never ending stream of data, which is different from what we've had in the past where it's batch or with a user interface request response. So they definitely turned up the volume on what they can do with continuous apps. And serverless, they'll talk about more tomorrow and Jim I think is going to weigh in, but it basically greatly simplifies the ability to run this infrastructure because you don't think of it as a cluster of resources. You just know that it's sort of out there and you ask requests of it and it figures out how to fulfill it. I will say the other big surprise for me was when we had Matej, who's the creator of Spark and the chief technologist at Databricks, come on the show and say when we asked him about how Spark was going to deal with essentially more advanced storage of data so that you could update things so that you could get queries back so you could do analytics and not just the stuff that's stored in Spark, but stuff that Spark stores essentially below it. And he said, you know Databricks, you can expect to see come out with or partner with a database to do these advanced scenarios. And I got the distinct impression and I have to listen to the tape again that he was talking about for Apache Spark which is separate from Databricks that they would do some sort of key value store. So in other words, when you look at competitors or quasi-competitors like Confluence Kafka or a data artisan's flink, they're not perfect competitors, they overlap some. Now Spark is pushing its way more into overlapping with some of those solutions. All right, well Jim can be a list and thank you for that George. You've been mingling with the masses today and you've been here all day as well. Educated masses, yeah, who are really engaged in this stuff, yes. Well great, maybe give us some of your top takeaways after all the conversations you've had today. You're not all that dissimilar from George's. What Databricks, Databricks of course being the center, the developer, the primary committer in the Spark open source community. They've done a number of very important things in terms of the announcements today at this event that push Spark, Spark ecosystem, where it needs to go to expand the range of capabilities and their deployability into production environments. The deep learning announcement in terms of the deep learning pipeline API, very, very important. Now as George indicated, Spark has been used in a fair number of deep learning development environments but not as a modeling tool so much as a training tool, tool for in-memory distributed training of deep learning models that we've developed in TensorFlow and Caffe and other frameworks. Now this announcement is essentially bringing support for deep learning directly into the Spark modeling pipeline, the machine learning modeling pipeline, being able to call out to deep learning, TensorFlow and so forth from within ML, LIB. That's very important. That means that Spark developers, of which there are many, far more than there are TensorFlow developers, will now have an easy path to bring more deep learning into their projects. That's critically important to democratize deep learning. Hope from what I've seen, what Databricks has indicated, they have support currently in API, reaching out to both TensorFlow and Keras, that they have plans to bring in API support for access to other leading DL toolkits, such as Caffe, Caffe2, which is a Facebook developed, such as MixNet, which is Amazon developed and so forth. That's very encouraging. Structure streaming is very important in terms of what they announced, which is an API to enable access to faster or higher throughput structured streaming in their cloud environment. And they also announced that they have gone beyond, in terms of the code that they've built, the micro-batch architecture of structure streaming, to enable it to evolve into a more true streaming environment, to be able to contend credibly with the likes of Flink. Because I think the Spark community has sort of had their back against the wall with structured streaming that they couldn't fully provide a true sub millisecond, end-to-end latency environment here to forward. It sounds like with its R&D that Databricks is addressing that, and that's critically important for the Spark community to continue to evolve in terms of continuous computation. And then the serverless apps announcement is also very important because I see it as really big, it's a fully managed multi-tenant Spark development environment as an enabler for continuous build, deploy, and testing DevOps within a Spark machine learning and now deep learning context. The Spark community as it evolves and matures needs robust DevOps tools to productionize these machine learning and deep learning models. Because really in many ways, many customers, many developers are now using, are developing Spark applications that are real 24 by seven enterprise application artifacts that need a robust DevOps environment. And I think that Databricks has indicated they know where this market needs to go and they're pushing it with their R&D. And I'm encouraged by all those signs. Okay, well thank you, Jim. And I hope both of you gentlemen are looking forward to tomorrow. I certainly am. And to you out there, tune in again around 10 a.m. Pacific time, we're going to be broadcasting live here from Spark Summit 2017. I'm David Goad with Jim and George saying goodbye for now and we'll see you in the morning.