ApacheCon EU 2014

Apache Spark is growing to be the most active project in Apache Big Data ecosystem. It truly unlocks the ability to perform analytics in-memory & in an iterative fashion. In this talk I will highlight the several customer case studies where we used several aspects of Apache Spark from Streaming, Warehousing & ML. Furthermore I will show how the seamless integration of Streaming, ML & warehousing yields new opportunities for businesses to reach to their data faster.

Cascading is a Java-based application development framework for building Big Data applications on Apache Hadoop. This open source framework allows developers to leverage their existing skillsets such as Java, SQL, etc. to create enterprise=grade applications without having to think in MapReduce. This comprehensive framework separates business logic from integration logic so that developers can quickly build and test data applications locally on their laptop and then deploy them on Hadoop. While typical enterprise data applications must cross through multiple departments and frameworks, Cascading allows multiple departments to seamlessly integrate their application components into one single data processing application. In this presentation, developers will get an introduction to Cascading, how it works, and then dive into how one can build applications with Cascading

HBase has traditionally been a simple "byte-bucket", in strict homage to the BigTable paper. HBase 0.96 introduced a new API for making HBase "data type aware". This API provides necessary encodings that preserve serialized order and have first-class support for complex rowkeys. It's also user-extensible. This session will introduce the API to developers with examples, including how to implement your own data types for HBase.

The genesis of Hadoop was in analyzing massive amounts of data with a mapreduce framework. SQL­-on­Hadoop has followed shortly after that, paving a way to the whole schema-­on­-read notion. Discovering graph relationship in your data is the next logical step. Apache Giraph (modeled on Google’s Pregel) lets you apply the power of BSP approach to the unstructured data. In this talk we will focus on practical advice of how to get up and running with Apache Giraph, start analyzing simple data sets with built­-in algorithms and finally how to implement your own graph processing applications using the APIs provided by the project. We will then dive into how Giraph integrates with the Hadoop ecosystem (Hive, HBase, Accumulo, etc.) and will also provide a whirlwind tour of Giraph architecture.

In this talk, we'll take a look at a range of projects from the Apache Software Foundation, looking at those which complement the "headline projects" to build out your big data solution. While we can't cover every project at Apache (there are just too many these days!), we'll take a tour through some of the up-coming and lesser-known established projects out there, those that should prove very helpful to you in building your big data solution. We'll see that Apache is more than just the webserver, Hadoop and Lucene, and with any luck point you at projects that'll save you time and effort!