![]() Participants in this tutorial will be led through the process of securing a Hadoop cluster. Why do many Hadoop clusters lack basic security controls? In part because some security features are relatively new and Hadoop security can be complex and daunting. Juliet Hougland, Senior Data Scientist, ClouderaĪ Practitioner’s Guide to Securing Your Hadoop Cluster It will cover the use of Spark's DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. Introduction to Machine Learning on Apache Spark MLlib Eva Andreasson, Director Product Management, Cloudera In this session, attendees will learn about the new analytics capabilities in Apache Solr that integrate full-text search, faceted search, statistics, and grouping to provide a powerful engine for enabling next-generation big data analytics applications. Text-based search recently has become a critical part of the Hadoop stack, and has emerged as one of the highest-performing solutions for big data analytics. Intuitive Real-Time Analytics with Search Eddie Garcia, Chief Security Architect, Cloudera Attendees will leave with a greater understanding of how effective INFOSEC relies on an enterprise big data governance and risk management approach. In addition, the presenter will cover strategies to orchestrate data security, encryption, and compliance, and will explain the Cloudera Security Maturity Model for Hadoop. In this session, participants will hear a comprehensive introduction to Hadoop Security, including the “three A’s” for secure operating environments: Authentication, Authorization, and Audit. Protecting enterprise data is an increasingly complex challenge given the diversity and sophistication of threat actors and their cyber-tactics. Risk Management for Data: Secured and Governed Todd Lipcon, Software Engineer, Cloudera / Kudu Founder The session also will cover Kudu (currently in beta), the new addition to the open source Hadoop ecosystem with outof-the-box integration with Apache Spark and Apache Impala (incubating), that achieves fast scans and fast random access from a single API. In this session, the presenter will describe these gaps and discuss the tradeoffs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. ![]() However, gaps remain in the storage layer that complicate the transition to Hadoop-based architectures. The Hadoop ecosystem has improved real-time access capabilities recently, narrowing the gap with relational database technologies. Doug Cutting, Chief Architect, ClouderaĪpache Kudu (Incubating): New Hadoop Storage for Fast Analytics on Fast Data In this keynote, Doug Cutting will explain how Apache Spark provides a second-generation processing engine that greatly improves on MapReduce, and why this transition provides an example of an evolutionary pattern in the data ecosystem that gives it long-term strength. In the decade since Hadoop was introduced, many other projects have been created around the Hadoop Distributed File System (HDFS) storage layer and its MapReduce processing engine, forming a rich software ecosystem. Hadoop was the first software to permit affordable use of petabytes. Keynote - From MapReduce to Spark: An Ecosystem Evolves
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