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This is a crash course introduction to Cassandra. You'll step away understanding how it's possible to to utilize this distributed database to achieve high availability across multiple data centers, scale out as your needs grow, and not be woken up at 3am just because a server failed. We'll cover the basics of data modeling with CQL, and understand how that data is stored on disk. We'll wrap things up by setting up Cassandra locally, so bring your laptops.

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I don't think it's hyperbole when I say that Facebook, Instagram, Twitter & Netflix now define the dimensions of our social & entertainment universe. But what kind of technology engines purr under the hoods of these social media machines? Here is a tech student's perspective on making the paradigm shift to "Big Data" using innovative models: alphabet blocks, nesting dolls, & LEGOs! Get info on: - What is Cassandra (C*)? - Installing C* Community Version on Amazon Web Services EC2 - Data Modelling & Database Design in C* using CQL3 - Industry Use Cases

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Feeling the need to contribute something to Apache Cassandra? Maybe you want to help guide the future of your favorite database? Get off the sidelines and get in the game! It's easy to say but how do you even get started? I will outline some of the ways you can help contribute to Apache Cassandra from minor to major. If you don't have the time or ability to submit code, there are a lot of ways you can participate. What if you do want to write some code? I can walk you through the process of creating a patch and submitting for final approval. Got a great idea? I'll show you propose that to the community at large. Take it from me, participating is so much more fun than just watching the project from a distance. Time to jump in! About the Speaker Patrick McFadin Chief Evangelist, DataStax Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.

Spectator to Participant. Contributing to Cassandra (Patrick McFadin, DataSta...

In this talk we describe the features of Cassandra that set it above the pack, and how to get the most out of them, depending on your application. In particular, we'll describe de-normalization, and detail how the algorithms behind Cassandra leverage awesome write speed to accelerate reads; and we'll explain how Cassandra achieves multi-datacenter support, tunable consistency and no single point of failure, to give a great solution for highly available systems.

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Highly available, scalable and secure data with Cassandra and DataStax Enterp...

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Whether it's statistics, weather forecasting, astronomy, finance, or network management, time series data plays a critical role in analytics and forecasting. Unfortunately, while many tools exist for time series storage and analysis, few are able to scale past memory limits, or provide rich query and analytics capabilities outside what is necessary to produce simple plots; For those challenged by large volumes of data, there is much room for improvement. Apache Cassandra is a fully distributed second-generation database. Cassandra stores data in key-sorted order making it ideal for time series, and its high throughput and linear scalability make it well suited to very large data sets. This talk will cover some of the requirements and challenges of large scale time series storage and analysis. Cassandra data and query modeling for this use-case will be discussed, and Newts, an open source Cassandra-based time series store under development at The OpenNMS Group will be introduced.

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Come learn about how Zonar Systems uses Cassandra for logistics use cases such as tracking fleets of school buses and other fleet management services. Zonar uses Cassandra because because of its ability to scale horizontally, its continuous availability and operational ease. This talk will cover details about the implementation and our 3 year journey that got us here, including the challenges along the way.

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Building and managing cloud applications is not easy. Delivering one with an amazing customer experience is even harder. Join us for “The Performance Challenge: Providing an Amazing Customer Experience No Matter What” webinar where we will deep dive into the challenges customers face with providing a consistent experience no matter where customers are, providing real-time access to data and how DataStax Enterprise can help. Link to recording: https://youtu.be/qBGsyNulCOs View past DataStax webinars: http://www.datastax.com/resources/webinars

Webinar: The Performance Challenge: Providing an Amazing Customer Experience ...

Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.

En vedette (20)

Cassandra NoSQL Tutorial

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In Cassandra Lunch #74, Technical Marketing Manager at ScyllaDB, Peter Corless, presents on ScyllaDB and some of the advantages of using ScyllaDB over open-source Cassandra. Accompanying Blog: Coming Soon! Accompanying YouTube: https://youtu.be/9s83yDMGcbI Sign Up For Our Newsletter: http://eepurl.com/grdMkn Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/ Cassandra.Link: https://cassandra.link/ Follow Us and Reach Us At: Anant: https://www.anant.us/ Awesome Cassandra: https://github.com/Anant/awesome-cassandra Cassandra.Lunch: https://github.com/Anant/Cassandra.Lunch Email: solutions@anant.us LinkedIn: https://www.linkedin.com/company/anant/ Twitter: https://twitter.com/anantcorp Eventbrite: https://www.eventbrite.com/o/anant-1072927283 Facebook: https://www.facebook.com/AnantCorp/ Join The Anant Team: https://www.careers.anant.us

Apache Cassandra Lunch #74: ScyllaDB - Peter Corless

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Apache Cassandra is rock-solid and widely deployed for OLTP and real-time applications, but it is typically not thought of as an OLAP database for analytical queries. This talk will show architectures and techniques for combining Apache Cassandra and Spark to yield a 10-1000x improvement in OLAP analytical performance. We will then introduce a new open-source project that combines the above performance improvements with the ease of use of Apache Cassandra, and compare it to implementations based on Hadoop and Parquet. First, the existing Cassandra Spark connector allows one to easily load data from Cassandra to Spark. We'll cover how to accelerate queries through different caching options in Spark, and the tradeoffs and limitations around performance, memory, and updating data in real time. We then dive into the use of columnar storage layout and efficient coding techniques that dramatically speed up I/O for OLAP use cases. Cassandra features like triggers and custom secondary indexes allow for easy data ingestion into columnar format. Next, we explore how to integrate this new storage with Spark SQL and its pluggable data storage API. Future developments will enable extreme analytical database performance, including smart caching of column projections, a columnar version of Spark's Catalyst execution planner, and how vectorization makes for fast cache- and GPU-friendly calculations - see Spark's Project Tungsten. FiloDB is a new open-source database using the above techniques to combine very fast Spark SQL analytical queries with the ease of use of Cassandra. We will briefly cover interesting use cases, such as: * Easy exactly-once ingestion from Kafka for streaming and IoT applications * Incremental computed columns and geospatial annotations. We'll discuss how FiloDB improves aggregations needed for choropleth maps over standard PostGIS solutions.

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