11 Jul 2014
In this session, you'll learn about how Apache Cassandra is used with Python in the NY Times ⨍aбrik messaging platform. Michael will start his talk off by diving into an overview of the NYT⨍aбrik global message bus platform and its "memory" features and then discuss their use of the open source Apache Cassandra Python driver by DataStax. Progressive benchmark to test features/performance will be presented: from naive and synchronous to asynchronous with multiple IO loops; these benchmarks tailored to usage at the NY Times. Code snippets, followed by beer, for those who survive. All code available on Github!
- 1 participant
- 32 minutes
11 Jul 2014
This talk will cover how to load test your Cassandra cluster for your applications schema and other best practices to gain confidence in your Cassandra deployment before you run in production.
Jake Luciani is an Apache Cassandra developer at DataStax, as well as a committer on Apache Cassandra and Apache Thrift. His previous employer was Blue Mountain Capital in NYC, building a next generation market data database on Cassandra.
Jake Luciani is an Apache Cassandra developer at DataStax, as well as a committer on Apache Cassandra and Apache Thrift. His previous employer was Blue Mountain Capital in NYC, building a next generation market data database on Cassandra.
- 1 participant
- 34 minutes
11 Jul 2014
Apache Cassandra is singular in its ability to provide a single logical database dispersed over multi-region datacenters. Cassandra databases can lose entire datacenters due to network outages or natural disasters and the database will remain available for application users. This talk will discuss how Cassandra is able to achieve this feat, and dive deep into the programming realities of distributed systems and consistency models. It turns out that the consistency model is the critical feature of successful distributed systems. When an application and a database can have an honest conversation about the consistency needs of each, a powerful system is born. This honesty is at the core of Cassandra's dominant position in the world of distributed databases.
Matt Kennedy is an architect at DataStax. Matt has been a Cassandra user and occasional contributor since version 0.7 and is a co-organizer of the Cassandra meetup in the Washinton DC area. At the European Cassandra Summit in October of 2013, he was recognized by DataStax as a Cassandra MVP. Matt has been working with distributed systems his entire career and kinda wishes he was one. Because while Cassandra is partition tolerant, Matt is not.
Matt Kennedy is an architect at DataStax. Matt has been a Cassandra user and occasional contributor since version 0.7 and is a co-organizer of the Cassandra meetup in the Washinton DC area. At the European Cassandra Summit in October of 2013, he was recognized by DataStax as a Cassandra MVP. Matt has been working with distributed systems his entire career and kinda wishes he was one. Because while Cassandra is partition tolerant, Matt is not.
- 1 participant
- 30 minutes
11 Jul 2014
So you've grabbed the latest 2.0 beta of DataStax C# driver from NuGet. Now what? In this talk, Luke will walk you through some of the basics of the C# driver--how to bootstrap the driver and connect to a cluster, execute statements, and retrieve result sets. Wondering what the difference between a PreparedStatement and a SimpleStatement is? Not sure what the appropriate lifetime for a Cluster or a Session object is and whether you should reuse one (from multiple threads)? What about ADO.NET and LINQ support? We'll cover this and more, so that you can get on with building applications on top of Cassandra and .NET.
- 2 participants
- 37 minutes
11 Jul 2014
1.) With each device comes an implicit contract with the end user: you give us the data, we give you the results. Now. Not tomorrow. Not even fifteen minutes from now.
2.) The flip side of getting data in real time is that users expect results in real time.
3.) In return for being wired into the internet 24x7 customers demand a similar level of responsiveness and even better availability.
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Spark Streaming and Cassandra form the ideal combination of high velocity CEP and analytics with a high velocity and always on database.
Today's solutions don't scale to the Internet of tomorrow. The always-on nature of the emerging Internet of Things space means you need to process information at previously unseen scale and, more difficult, make sense out of that data.
Cassandra is the leader in large scale, high velocity, time series data workloads. While the Hadoop world has been stuck with legacy "batch analytics" technology, Cassandra users have been increasingly focused on the "now". Fast answers to easy questions about your data, at any velocity, and any scale. But Cassandra has always been weak on the "complex questions" problem. DataStax integrated with Hadoop to overcome this limitation, but it was always an awkward fit. Slow batch analytics on top of fast moving data really doesn't do you much good.
But Spark, and in this case, Spark Streaming, make high velocity streaming analytics at scale easier than ever, similar to how Cassandra pioneered high-velocity data management at scale.
Hadoop is the right choice for batch analytics. Until recently, nobody really knew what the right solution is for real-time processing. We believe that Spark and Cassandra are the clear answer.
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Tupshin has been helping Cassandra users and DataStax customers build large scale, high velocity applications for years. As both a Solutions Architect as well as the lead Field Strategist for DataStax, he has seen deployments of every scale and in every sector. Recently specializing in the financial services space, he has worked with numerous banking industry customers to build and refine their mission critical, enterprise scale, operational data stores based on Cassandra and DataStax Enterprise. After 18 years in the Bay Area start-up scene, he recently packed up and moved to New York City.
Al is a father, technologist, musician, and open source advocate working for DataStax. While attending Central Michigan University as a music major, Al got into MUDs, C, and Linux, eventually ending up with a career as a sysadmin. Over the last 15 years, Al has worked on everything from kernel changes to modern web applications, mostly from inside operations teams. These days he goes by the title Open Source Mechanic, which means he tries to do interesting things with Cassandra and other open source software.
2.) The flip side of getting data in real time is that users expect results in real time.
3.) In return for being wired into the internet 24x7 customers demand a similar level of responsiveness and even better availability.
__
Spark Streaming and Cassandra form the ideal combination of high velocity CEP and analytics with a high velocity and always on database.
Today's solutions don't scale to the Internet of tomorrow. The always-on nature of the emerging Internet of Things space means you need to process information at previously unseen scale and, more difficult, make sense out of that data.
Cassandra is the leader in large scale, high velocity, time series data workloads. While the Hadoop world has been stuck with legacy "batch analytics" technology, Cassandra users have been increasingly focused on the "now". Fast answers to easy questions about your data, at any velocity, and any scale. But Cassandra has always been weak on the "complex questions" problem. DataStax integrated with Hadoop to overcome this limitation, but it was always an awkward fit. Slow batch analytics on top of fast moving data really doesn't do you much good.
But Spark, and in this case, Spark Streaming, make high velocity streaming analytics at scale easier than ever, similar to how Cassandra pioneered high-velocity data management at scale.
Hadoop is the right choice for batch analytics. Until recently, nobody really knew what the right solution is for real-time processing. We believe that Spark and Cassandra are the clear answer.
---
Tupshin has been helping Cassandra users and DataStax customers build large scale, high velocity applications for years. As both a Solutions Architect as well as the lead Field Strategist for DataStax, he has seen deployments of every scale and in every sector. Recently specializing in the financial services space, he has worked with numerous banking industry customers to build and refine their mission critical, enterprise scale, operational data stores based on Cassandra and DataStax Enterprise. After 18 years in the Bay Area start-up scene, he recently packed up and moved to New York City.
Al is a father, technologist, musician, and open source advocate working for DataStax. While attending Central Michigan University as a music major, Al got into MUDs, C, and Linux, eventually ending up with a career as a sysadmin. Over the last 15 years, Al has worked on everything from kernel changes to modern web applications, mostly from inside operations teams. These days he goes by the title Open Source Mechanic, which means he tries to do interesting things with Cassandra and other open source software.
- 2 participants
- 34 minutes
11 Jul 2014
Cassandra has been an integral part of Ultravisual's infrastructure since its launch, allowing us to rapidly prototype and build new features that further enhance user experience. Over the course of this discussion we will cover three key topics. How Cassandra came to be used at Ultravisual and the key problem it solved. How the usage of Cassandra as part of our stack has evolved alongside the product. And finally, some of the experiences we've had with deploying and running Cassandra in a production environment.
- 1 participant
- 23 minutes
