Production Ready Cassandra (Beginner)

Like many startups, Coursera began its data storage journey with MySQL, a familiar and industry-proven database. As Coursera's user base grew from several thousand to many millions, we found that MySQL provided limited availability and restricted our ability to scale easily. New product initiatives and requirements provided a perfect opportunity to revisit our choice of core workhorse database. After evaluating several NoSQL databases, including MongoDB, DynamoDB and HBase, we elected to transition to Cassandra . Cassandra's relative maturity, masterless architecture (for availability), tunable consistency, and stable low-latency performance made it a clear winner for our needs. Learn more about what it takes to transition from SQL to Cassandra in this talk.

New features in 3.0

Introduction to .Net Driver

.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about: - Cassandra’s internals and how it works - CQL (the SQL-like query language for Cassandra) - Data Modeling like a pro - Tools available for developers - Writing .NET code that talks to Cassandra If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.

Spark Cassandra Connector: Past, Present and Furure

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Lessons Learned with Cassandra and Spark at the US Patent and Trademark Office

This case study concerns moving large amounts of patent data from Cassandra to Solr. How we approached the problem, the introduction of Spark as a solution, and how to optimize Spark jobs. I will cover: * Understanding the parts of a Spark Job. Which components run where and common issues. * Adding metrics to show where pain points are in your code. * Comparing various methods in the API to achieve more performant code. * How we saved time and made a repeatable process with Spark.

Using Event-Driven Architectures with Cassandra

Many architects in companies ranging from small startups to publicly traded companies are turning to event-driven architectures to solve mission-critical scalability problems, often ones that carry real-time processing requirements. In this talk we'll demonstrate how you can use Apache Cassandra to build powerful event-driven systems in combination with technologies like Akka, RabbitMQ, and others. These concepts will help you radically simplify the design of complex systems and give you the ability to remain available and responsive even in the face of bursty workloads. If your organization does any sort of stream processing or real-time aggregation work with C*, then this talk is for you.

One is the loneliest number Much, much worse than two Many of PagerDuty’s mission-critical services are based on Cassandra, and as a result we have built up a lot of operational experience over the past few years. Unfortunately, some of our best learnings have come from sizeable failures in production. One of those failures stemmed from having multiple services share the same Cassandra cluster, which was a major factor in PagerDuty’s largest outage of 2014. This talk will relive that outage, sort through the wreckage, and explain why isolating your Cassandra clusters is a best practice you should adopt

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Analytics with Spark and Cassandra

Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark. Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. It’s exactly what a Cassandra cluster needs to deliver real-time, ad-hoc querying of operational data at scale. In this talk, we’ll explore Spark and see how it works together with Cassandra to deliver a powerful open-source big data analytic solution.

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Cassandra Data Maintenance with Spark

Client Drivers and Cassandra, the Right Way

Cassandra is pretty awesome, sure I am biased, but it rocks. Always on, tuneable consistency and multi-master architecture? Let’s get our web scale on and build a highly available app that never goes down! Hold on a second. There is one key piece of the puzzle that has a massive impact on your applications availability: the client driver. In this talk we will go through the how to best configure your clients to make the most of failure handling and tuneable consistency in Cassandra.

Make 2016 your year of SMACK talk

You’ve heard all of the hype, but how can SMACK work for you? In this all-star lineup, you will learn how to create a reactive, scaling, resilient and performant data processing powerhouse. Bringing Akka, Kafka and Mesos together provides a foundation to develop and operate an elastically scalable actor system. We will go through the basics of Akka, Kafka and Mesos and then deep dive into putting them together in an end2end (and back again) distrubuted transaction. Distributed transactions mean producers waiting for one or more of consumers to respond. We'll also go through automated ways to failure induce these systems (using LinkedIn Simoorg) and trace them from start to stop through each component (using Twitters Zipkin). Finally, you will see how Apache Cassandra and Spark can be combined to add the incredibly scaling storage and data analysis needed in fast data pipelines. With these technologies as a foundation, you have the assurance that scale is never a problem and uptime is default.

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Advanced Operations

This sessions covers diagnosing and solving common problems encountered in production, using performance profiling tools. We’ll also give a crash course to basic JVM garbage collection tuning. Attendees will leave with a better understanding of what they should look for when they encounter problems with their in-production Cassandra cluster. This talk is intended for people with a general understanding of Cassandra, but it not required to have experience running it in production.

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Feeding Cassandra with Spark-Streaming and Kafka

In this session we will examine a sample application that simulates an IoT stream that is handled through Kafka, Spark Streaming, and into Cassandra. The session will discuss the implementation details including the Kafka design considerations, Spark Steaming functionality including working with windowing to achieve analytics and finally Cassandra Time series data model considerations. The example is based on OSS Kafka and Integrated Spark and Cassandra in DSE.

Cassandra Summit 2014: A Train of Thoughts About Growing and Scalability — Bu...

Presenter: Eiti Kimura, Senior Software Engineer at Movile Apache Cassandra was adopted by Movile in 2009, and became a fundamental piece within the robust and scalable architecture to support more than 50 products, impacted by over 200MM users in Latin America. In this case we present the architecture of our ring, configuration details, detailed tuning, hardware used to be able to achieve our performance requirements (order of a few milliseconds), information storage strategies for network and disk space optimization, and best practices, in addition to showing the evolution of the architecture of simple systems to become scalable and distributed platforms. We introduced our cluster with a relatively low number of nodes (6) using commodity hardware to support critical high-performance applications. After this talk, you'll understand how Apache Cassandra was essential to evolve our systems and leverage the growth of our business. Movile is the leading mobile content company in Latin America. Movile’s products include mobile content, mobile TV, mobile learning, mobile games, mobile payment, mobile marketing and mobile commerce. Every month, it publishes content and services to more than 20 million mobile costumers. It has grown substantially over the last few years (with a more than 25-fold increase in its revenue over the last five years) both organically and through an aggressive M&A strategy, including five acquisitions in the last five years. Movile is positioning itself as a kind of Silicon Valley company based in Brazil. For the last two years, Movile has been named in the “Great Place to Work” list for technology companies in Brazil. The company shareholders include the founders of the company plus Naspers, a South-African media conglomerate.

Cassandra Summit 2014: Social Media Security Company Nexgate Relies on Cassan...

Presenter: Harold Nguyen, Senior Data Scientist at Nexgate In this talk, we focus on a use case by showing how Cassandra can detect spam and spammers on social media. We also show how we use Cassandra to train our 100+ social-media-security classifiers. The accuracy of any security product is directly tied to the breadth of the corpus of data upon which it is built. For Nexgate, this means that the success of our products is inextricably tied to our ability to save everything we've ever scanned, but in a way that is still readily accessible. In the days before NoSQL, this was hard. This talk is about how Datastax and Cassandra make it easy.

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Presenter: Claudiu Barbura, Senior Director of Engineering at Atigeo xPatterns is a big data analytics platform-as-a-service that enables rapid development of enterprise-grade analytical applications. It provides tools, API sets and a management console for building an ELT pipeline with data monitoring and quality gates, a data warehouse for ad-hoc and scheduled querying, analysis, model building and experimentation, tools for exporting data to Cassandra and solrCloud clusters for real-time access through low-latency/high-throughput (automatically generated) apis as well as dashboard and visualization api/tools leveraging the available data and models. In this talk I'll share some of the hard lessons we've learned in the past three years while leveraging Cassandra (and Hector) in large-scale enterprise-grade deployments. We will focus on three specific areas, in which we identified consistent best practices & design patterns: data model optimization as a result of exporting data from HDFS/Hive/Shark into Cassandra through Spark/Hadoop MR jobs under Mesos with throttling, instrumentation and resilience features, automatically publishing geo-replicated, instrumented and monitored REST API's on top of the exported Cassandra data, and lessons learned from running Cassandra at scale from 0.6 to 2.0.6, including performance tuning, and tips and tricks. You will see live demos of our Publish to NoSql tools (Spark/Shark, Mesos, Hive, Cassandra ), a dashboard application built on top of generated data apis (D3.js, Cassandra) and xPatterns' monitoring and instrumentation consoles (Graphite, Ganglia, Nagios).

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Cassandra Summit 2014: META — An Efficient Distributed Data Hub with Batch an...

Presenter: Alvaro Agea, Big Data Architect at Stratio Big Data analysis is commonly associated with batch processing of data stored in distributed file systems. The advent of streaming data is exposing the shortcomings of the traditional data analysis. Users aiming to combine both worlds - batch processing and streaming - had to turn to unreliable in-house developments. We propose Stratio META to meet this new need. META is a technology based on a structured NoSQL datastore with advanced indexing capabilities. META includes an efficient query planner designed from scratch. The planner determines which is the optimal path to execute a query and which components should be involved.

Introduction to Dating Modeling for Cassandra

A good data model is key to getting the best performance from Apache Cassandra. The Log Structured Storage Engine and it's distributed architecture mean we cannot rely on a paradigm such as Normal Form to evaluate a model. Instead we need to design data models that support the read path of the application. In this talk Aaron Morton will walk through the key principles and patterns of a good CQL3 data model using simple examples.

Cassandra Summit 2014: Apache Cassandra at Telefonica CBS

Presenter: Antonio Alcocer, Big Data Architect at Stratio Telefonica is the incumbent telecommunications network operator in Spain and the fourth one in capitalisation in the world. Cyber security is one of our most successful businesses worldwide. We provide monitoring and protecting clients from attacks. We analyze millions of data from multiple sources including social media, DNS records, and underground internet, to generate alerts and security reports for our clients. This use case required a Big Data component capable of processing the data and extract its information in real-time; warnings and alerts are time-sensitive in order to deal efficiently with security attacks. Our original architecture was the typical one used for data fusion systems. It included several collectors, a processing layer based on legacy systems, and a data store. The initial setup included a MongoDB database and an ad-hoc application. This solution however proved to be unfit for the specific purpose of dispatching alerts. We proposed to use Cassandra and Spark instead. This approach did manage to fulfill our original specifications as intended. Our talk will explain the reasons why we migrated the architecture and how the adopted solution based on Spark and Cassandra solved our problem.

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2018 Women in Analytics Conference https://www.womeninanalytics.org/ Over the last year I’ve become obsessed with learning how to be a better "cloud computing evangelist to data scientists" - specifically to the R community. I’ve learned that this isn’t often an easy undertaking. Most people (data scientists or not) are skeptical of changing up the tools and workflows they’ve come to rely on when those systems seem to be working. Resistance to change increases even further with barriers to quick adoption, such as having to teach yourself a completely new technology or framework. I’d like to give a talk about how working in the cloud changes data science and how exploring these tools can lead to a world of new possibilities within the intersection of DevOps and Data Analytics. Topics to discuss: - Working through functionality/engineering challenges with R in a cloud environment - Opportunities to customize and craft your ideal version of R/RStudio - Making and embracing a decision on what is “real" about your analysis or daily work (Chapter 6 in R for Data Science) - Running multiple R instances in the cloud (why would you want to do this?) - Becoming an R/Data Science Collaboration wizard: Building APIs with Plumber in the Cloud

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