• Apache Spark and Data Science
    • Cloud and Applications
    • Data Processing and Warehousing
    • Enterprise Adoption
    • IoT and Streaming
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.
In this track you will hear from ISVs, and architects that have created applications, frameworks, and solutions that have been built to solve real business problems leveraging data as an asset. 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 span both technical and business audiences, discussing business justification and ROI to technical architecture. For a system to be “open for business”, it must be efficiently managed by system administrators. A critical component of a successful connected data architecture is a comprehensive dataflow and operations strategy. The track is focused on developing and deploying Modern Data Applications on the extended Apache Data ecosystem in the on-premise and cloud. Sessions will range from how to get started, and operating your cluster to cutting-edge best practices for large-scale deployments.
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.
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. Sample technologies: Apache Nifi, Apache Storm, Streaming Analytics Manager, Apache Flink, Apache Spark Streaming, Apache Beam, Apache Pulsar and Apache Kafka

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