DataStax Accelerate On-Demand

Explore the trends, strategies, and technologies driving application modernization and data management that will help you move your business into the future. Learn about the future of Apache Cassandra™, DataStax Constellation, a cloud-native data platform, and 70+ technical sessions designed to help you build game-changing applications in the cloud, on premises, or in a hybrid cloud environment.

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Streaming Cassandra into Kafka

Yelp has built a robust stream processing ecosystem called Data Pipeline. As part of this system we created a Cassandra Source Connector, which streams data updates made to Cassandra into Kafka in real time. We use Cassandra CDC and leverage the stateful stream processing of Apache Flink to produce a Kafka stream containing the full content of each modified row, as well as its previous value.

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DataStax Enterprise 6 and Beyond

DataStax Enterprise already offers a plethora of solid capabilities to make your distributed database dreams become more real than the Neverending Story. But are you aware of all of the crazy quality of life updates and new features added in DataStax Enterprise 6? This includes: significantly improved performance; anti-entropy improvements with NodeSync; quality updates for Search, Graph, Analytics, OpsCenter, security, and Studio; metrics collection; and Kafka and Docker integrations. There is just so much good stuff here! We’ll take a look at all of it along with a sneak peak at some of the foundational changes coming in DataStax Enterprise 6.8 that will rock your world.

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Extending Gremlin with Foundational Steps

Gremlin provides the most technology agnostic way to develop graph applications. Gremlin is the graph query language of Apache TinkerPop, which is the open source graph computing framework used by much of the graph database community, including DataStax Enterprise Graph. This presentation aims to demystify the more intermediate to advanced aspects of Gremlin and allow users of any level to become more effective when writing graph queries. It will explain Gremlin’s functional, data-flow style, and demonstrate how it can be extended to a Domain Specific Language (DSL) to encapsulate foundational and domain specific logic for better code organization and reusability.

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Lighting a Spark with Machine Learning and DataStax Enterprise Analytics

Machine learning glitters as the latest skill in tech for developers. This session will combine lecture and live code that will utilize DSE Analytics and review five different Apache Spark MLlib functions. Real-world examples will be used so the audience leaves understanding how to incorporate these functions into their applications. The audience will leave this session feeling like they can be a star in their next machine learning project using DSE Analytics.

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Implementation of a Big Data Architecture for Real-Time Analytics with DataStax Enterprise Graph, Analytics, and Search

In the world of traditional databases Real-Time Analytics provides no architecture. Why? It would inevitably degrade the transactional service of the database and with it the user experience. Another important factor is the scalability and being able to maintain a always online service. If we think of a traditional BI and DW architecture, several steps are involved: we usually have ETLs working to extract the information, populate the Data Warehouse and load the OLAP model, with, in the majority of use cases, a one day delay. My talk will cover a Big Data architecture that allows Real-Time Analytics to be done in an efficient way. I will be exposing DSE with its different workloads: Analytics, Search, Graph and AlwaysOn SQL feature that will allow SQL queries and also connect a Business Intelligence tool like PowerBI. I will also show how DataStax Studio allows to interact graphically with the data by performing SQL and Gremlin queries.

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Building a Recommendation System with DataStax Enterprise Graph

This talk covers fundamentals of recommendation systems and how to build one using DataStax Enterprise Graph. We discuss content-based, collaborative filtering and hybrid approaches to predictive modeling as well as possible design patterns for a real-time recommender service. Using DataStax Studio, we will walk through several examples of Gremlin recommender traversals, demonstrate how they work, and discuss their advantages and limitations.

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To Spark or Not to Spark?

Heard about the exciting new world of distributed Analytics with Spark but not sure if it's appropriate for your use case? In this talk, we'll walk through the basic use cases for Spark and DataStax Enterprise outlining the potential uses for any organization, even those not requiring generic analytics capabilities. Learn about how we can use Spark to load data, modify tables, and move data from cluster to cluster. Discover more advanced use cases, like working with streaming services and messaging queues. Find out about all the exciting things you can do with DataStax Enterprise and Spark!

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DSE Graph.Next: The Cloud-Native Distributed Graph Database for Solving Graph Problems

Graph databases are everywhere right now. The explosive growth in the graph market coupled with the hype of solving graph problems is causing both excitement and confusion. From labeled property graphs to RDF to pure graph analytics to multi-model databases, the breadth of graph offerings is staggering. The good news? DataStax has been listening—and building. In this session, we'll show you how DataStax delivers valuable solutions for the world’s largest distributed graph problems in today’s cloud architectures. We'll highlight the fundamental philosophy driving DSE Graph and unveil a sneak peak at where we are going with DSE Graph.Next. Hint: it does things no other graph databases can do. We’re very excited about what's next.

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Predicting Restaurant Inspection Failures with DataStax Enterprise Analytics

How does a data scientist go about understanding the data and determining how to build a model? What are the problems, and how can a model be misleading? What are the tools available at scale, and how easily can existing knowledge be leveraged? We’ll explore a data scientist's methodology for predicting restaurant failures using Chicago's Open Data Portal and DSE. What data sources would make sense and how does intuition come into play? What are we trying to achieve, and how do we measure accuracy? While perfect for those new to data science, we’ll also dive more deeply into the thought process, exploration, and analysis with code and visualization tools (Jupyter, ML, DSE, Cassandra, Spark). This project was one involved in graduate studies at Harvard University, but the topic was explored and implemented by the City of Chicago. Participants will come away with an understanding of how and why a platform for exploration makes life better and how various tools work. Feel free to clone the GitHub repo and maybe even come up with a better model!

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