ApacheCon EU 2014

FINN.no is the leading classifieds website in Norway and the country's busiest website.

This session will go through various product development where Cassandra has shown to be the best choice. Focusing on the primary use-case: a tracking solution that's collects raw time-series data in c* and aggregates it near real-time using Hadoop into various new datasets from advert-centric statistics to user-centric behavioural analysis.

Mick will cover the final technical design chosen after three years of development iterations, touching on technologies: scribe, thrift, kafka, hadoop, pig, mahout; the hurdles faced along the way, integration improvements done between cassandra and hadoop, and the throughput and performance of today's systems.

During this talk, you'll be given a high level introduction to Cassandra and to the database mechanism. Summary of topics discussed:

•    Architecture
•    Data model
•    Replication
•    Consistency model
•    Read/Write path
•    Failure handling

Whether you are building a mobile app or a web app, Apache Usergrid (incubating) can provide you with a complete backend that supports authentication, persistence and social features like activities and followers all via a comprehensive REST API — and backed by Cassandra, giving you linear scalability. All that, and Usergrid is open source too.

This session will explain how you can use Usergrid to provide a back-end for your application. We’ll start with an overview of Usergrid features, then explore in depth how to authenticate users, store data and query data with the REST API provided by a Usergrid server. We’ll develop a simple HTML5 app and package it as a native mobile app via Apache Cordova. We'll also cover how to run Usergrid locally for development and testing.

Whether it's statistics, astronomy, finance, or network management, time series data plays a critical role in analytics and forecasting. Yet, while many tools exist, few are able to scale past memory limits; 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.

This talk will focus on learning curve and complex data models and queries. This is a real life example and something we do daily at Execom. It will also cover data migration and development cycles using Cassandra as database.

This talk starts with the tradition data modeling rules (and the reasons behind them) then shows how new requirement have brought new modeling approaches, including star schema modeling for DWs, and Data Vault modeling  The final part of talk discuss the requirements of the modern NOSQL database systems and serve as an introduction to NOSQL/Cassandra data modeling.

This talk is a good fit for people who has some background in SQL databases but are new to NOSQL databases and Cassandra.  

We'll be looking at how SoundCloud uses Cassandra, particularly looking how it supports SoundCloud's new stats product, which provides analytics data for people with sounds on SoundCloud. There are two main systems behind it, one doing real-time counts and timelines, and another doing batch top-k lists. We'll talk about the different problems inherent in both systems, and how we've attempted to overcome them.

Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We'll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.