Artificial Intelligence is transforming every vertical. Several popular tools and projects have contributed to this accelerated transformation: Apache Spark for large-scale Machine Learning, TensorFlow for Deep Learning, and Apache Zeppelin and Jupyter notebooks enabling Data Scientist to quickly prototype, test, and deploy advanced ML models. This track covers introductory to advanced sessions on algorithms, tools, applications, and emerging research topics that extend the Hadoop ecosystem for data science. Sessions will include examples of innovative analytics applications and systems, data visualization, statistics, and machine learning, deep learning and artificial intelligence. You will hear from leading data scientists, analysts and practitioners who are driving innovation by extracting valuable insights from data at rest as well as data in motion.
For a system to be “open for business,” system administrators must be able to efficiently manage and operate it. That requires a comprehensive dataflow and operations strategy. This track provides best practices for deploying and operating data lakes, streaming systems, and the extended Apache data ecosystem on premises and in the cloud. Sessions cover the full deployment lifecycle including installation, configuration, initial production deployment, upgrading, patching, loading, moving, backup, and recovery.
You’ll discover how to get started and how to operate your cluster. Speakers will show how to set up and manage high-availability configurations and how DevOps practices can help speed solutions into production. They’ll explain how to manage data across the edge, the data center, and the cloud. And they’ll offer cutting-edge best practices for large-scale deployments.
Apache Ambari, Cloudbreak, HDInsight, HDCloud, Data Plane Service, AWS, Azure, and Apache Oozie
Apache Hadoop – YARN has transformed Hadoop into a multi-tenant data platform. It is the foundation for a wide range of processing engines that empowers businesses to interact with the same data in multiple ways simultaneously. This means applications can interact with the data in the most appropriate way: from batch to interactive SQL or low latency access with NoSQL, and the interaction of legacy data stores and big data. There is a vast ecosystem of SQL engines and tools that are enabling richer Data Warehousing on Hadoop with capabilities for ACID, interactive queries, OLAP and data transformation. You will have the opportunity to hear from the rock stars of the Apache community and learn how these innovators are building applications.
Enterprise business leaders and innovators are using data to transform their businesses. These modern data applications are augmenting traditional architectures and extending the reach for insights from the edge to the data center. Sessions in this track will discuss business justification and ROI for modern data architectures.
You’ll hear from ISVs and architects who have created applications, frameworks, and solutions that leverage data as an asset to solve real business problems. Speakers from companies and organizations across industries and geographies will describe their data architectures, the business benefits they’ve experienced, their challenges, secrets to their successes, use cases, and the hard-fought lessons learned in their journeys.
Your data lake contains a growing volume of diverse enterprise data, so a breach could be catastrophic. Privacy violations and regulatory infractions can damage your corporate image and long-term shareholder value. Government and industry regulations demand you properly secure and govern your data to assure compliance and mitigate risks. But as Hadoop and streaming applications emerge as a critical foundation of a modern data architecture, enterprises face new requirements for protection and governance.
In this track, you’ll learn about the key enterprise requirements for governance and security of the extended data plane. You’ll hear best practices, tips, tricks, and war stories on how to secure and govern your big data infrastructure.
Sample technologies: Apache Ranger, Apache Sentry, Apache Atlas, and Apache Knox
The rapid proliferation of sensors and connected devices is fueling an explosion in data. Streaming data allows algorithms to dynamically adapt to new patterns in data, which is critical in applications like fraud detection and stock price prediction. Deploying real-time machine learning models in data streams enables insights and interactions not previously possible.
In this track you’ll learn how to apply machine learning to capture perishable insights from streaming data sources and how to manage devices at the “jagged edge.” Sessions present new strategies and best practices for data ingestion and analysis. Presenters will show how to use these technologies to develop IoT solutions and how to combine historical with streaming data to build dynamic, evolving, real-time predictive systems for actionable insights.
Apache Nifi, Apache Storm, Streaming Analytics Manager, Apache Flink, Apache Spark Streaming, Apache Beam, Apache Pulsar and Apache Kafka