Artificial Intelligence (AI) is transforming every industry. Data science and machine learning are opening new doors in process automation, predictive analytics, and decision optimization. This track offers sessions spanning the entire data science lifecycle: development, test, and production.
You’ll see examples of innovative analytics applications and systems for data visualization, statistics, machine learning, cognitive systems, and deep learning. We’ll show you how to use modern open source workbenches to develop, test, and evaluate advanced AI models before deploying them. You’ll hear from leading researchers, data scientists, analysts, and practitioners who are driving innovation in AI and data science.
Apache Spark, R, Apache Livy, Apache Zeppelin, Jupyter, scikit-learn, Keras, TensorFlow, DeepLearning4J, Chainer, Lasagne/Blocks/Theano, CaffeOnSpark, Apache MXNet, and PyTorch/Torch
Apache Hadoop continues to drive data management innovation at a rapid pace. Hadoop 3.0 adds container management to YARN, an object store to HDFS, and more. This track presents these advances and describes projects in incubation and the industry initiatives driving innovation in and around the Hadoop platform.
You’ll learn about key projects like HDFS, YARN, and related technologies. You’ll interact with technical leads, committers, and experts who are driving the roadmaps, key features, and advanced technology research around what is coming next and the extended open source big compute and storage ecosystem.
Sample technologies: Apache Hadoop (YARN, HDFS, Ozone), Apache Kudu, Kubernetes, Apache BookKeeper
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
The speed and scale of recent ransomware attacks and cybersecurity breaches have taught us that threat detection and mitigation are the key to security operations in data-driven businesses. Creating cybersecurity machine learning models and deploying these models in streaming systems is becoming critical to defending and managing these growing threats.
In this track, you’ll learn how to leverage big data and stream processing to improve your cybersecurity. Experts will explain how to scale with analytics on more data and react in real time.
Apache Metron, Apache Spot
Apache Hadoop YARN has transformed Hadoop into a multi-tenant data platform that enables the interaction of legacy data stores and big data. It is the foundation for multiple processing engines that let applications interact with data in the most appropriate way from batch to interactive SQL to low latency access with NoSQL.
Sessions will cover the vast ecosystem of SQL engines and tools that enable richer enterprise data warehousing (EDW) on Hadoop. You’ll learn how NoSQL stores like Apache HBase are adding transactional capability that brings traditional operational data store (ODS) workloads to Hadoop and why data preparation is a key workload. You’ll meet Apache community rock stars and learn how these innovators are building the applications of the future.
Apache Hive, Apache Tez, Apache ORC, Druid, Apache Parquet, Apache HBase, Apache Phoenix, Apache Accumulo, Apache Drill, Presto, Apache Pig, JanusGraph, Apache Impala
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