Cloud Data Services: A Brand New Ballgame for Business

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In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.

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Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.

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Presentation by Ashish Thusoo, Co-Founder & CEO at Qubole, on exploring the big data industry trends in moving from data warehouses to cloud-based data lakes.This presentation will cover how companies today are seeing a significant rise in the success of their big data projects by moving to the cloud to iteratively build more cost-effective data pipelines and new products with ML and AI. Uncovering how services like AWS, Google, Oracle, and Microsoft Azure provide the storage and compute infrastructure to build self-service data platforms that can enable all teams and new products to scale iteratively.

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So you built your Hadoop cluster. How do you get data from hundreds of database tables, streaming Kafka sources, and data shared by 20-year-old COBOL programs, all in there and working together quickly, efficiently and securely? With many customers asking this same question, Hortonworks recently expanded its partnership with Syncsort to provide optimized ETL onboarding for Hadoop. During this talk, we'll discuss how a next-generation ETL tool, built on contributions to the open source community and natively integrated in Hadoop, can drive lasting value for your organization. 1) Seamlessly onboard data from all your enterprise sources – batch and streaming -- into Hadoop for fast and easy analytics. 2) Stay agile and simplify your environment with a "design once, deploy anywhere" approach that minimizes disruption and risk in the face of a rapidly evolving big data ecosystem. 3) Secure, govern and manage your data with full integration with Apache Ambari, Apache Ranger, and more. These benefits come to life with real customer case studies. Learn how a national insurance company and global hotel chain are using Hortonworks HDP and Syncsort DMX-h to get bigger insights from their enterprise data, securely, efficiently, and cost-effectively, without spending hundreds of man-hours.

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In this webinar, we talk about the shift in data gravity as more and more business applications are moving to the cloud, and how the ability to deliver analytics in the cloud has evolved from idea to enterprise reality with new solutions being announced constantly that appeal to the need for speed, simplicity and access to insight on demand. Joining us in this webinar is David Glueck, Sr. Director of Data Science and Engineering at Bonobos. To learn more, visit: www.SnapLogic.com/salesforce-analytics

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DataWorks Summit 2017 - Sydney Keynote Scott Gnau, Chieft Technology Officer, Hortonworks Data has become the most valuable asset for every enterprise. As businesses undergo data transformation, leading organizations are turning to data science and machine learning to drive more business value out of their data. In this talk, Scott will examine the trends and the key requirements needed to evolve to next-generation analytics and operations.

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At Sams Club we have a long history of using Apache Spark and Hadoop. Projects from all parts of the company use Apache Spark, from fraud detection to product recommendations. Because of the scale of our business with billions of transactions and trillions of events it is often essential to use big data technologies. Until recently all of this work has run on several large on-premise Hadoop clusters. As part of our transition to public cloud we needed to build out an enterprise scale data platform. Azure Databricks is a key component of this platform giving our data scientist, engineers, and business users the ability to easily work with the companies data. We will discuss our architecture considerations that lead to using multiple Databricks workspaces and external Azure blob storage. We will also discuss how we move massive amounts of data to Azure on a daily basis with Airflow. Further we will discuss the self-service tools that we created to help users get their data to Azure and for us to manage the platform. Finally we will discuss our security considerations and how that played out in our architecture. Authors: Andrew Ray, Craig Covey

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Industry 4.0 IoT applications promise vast gains in productivity from reduced downtime, higher product quality and higher efficiency. Modern industrial robots integrate hundreds of sensors of all kinds, generating tremendous volumes of data rich in valuable information. However, the reality is that some of the most advanced industrial makers in the world are barely getting started making use of this data, with relatively rudimentary, bespoke monitoring systems built at tremendous cost. We believe that it is now possible, using a well-chosen selection of enterprise open source big data projects, to successfully deploy Industry 4.0 pilot use cases in a matter of months, at a small fraction of the cost of equivalent projects at leading high-tech makers. We propose to show a working prototype of just such a system, and explain in some detail how it was made. Our presentation describes a working real-time ML-based anomaly detection system. We show a working industrial robot-analog installed with a wireless movement sensor. Our system scores the data in a cloud-based cluster. For added realism, the system we demonstrate live includes a working augmented-reality headset that can show the real-time status overlaid on the working robot. This talk is about demonstrating a concrete example of a real-time predictive maintenance system, built as a series of microservices connected by Kafka streams and powered by the excellent H2O distributed Machine Learning tool. Our goal is for our attendees to get a feel for what can be realistically achieved by a few non-genius-level engineers in a few months of effort using the best in open source technology for real-time streams (Kafka) and Machine learning (H2O). Where appropriate, we’ll mention how our choice of using the MapR Converged Data Platform made the development easier thanks to some of its unique features. Speaker Cao Yi, MapR

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The Briefing Room with Eric Kavanagh and Phasic Systems Live Webcast Mar. 26, 2013 The complexity of today's information architectures creates a wide range of challenges for executives trying to get a strategic view of their current operations. The data and context locked in operational systems often get diluted during the normalization processes of data warehousing and other types of analytic solutions. And the ultimate goal of seeing the big picture gets derailed by a basic inability to reconcile disparate organizational views of key information assets and rules. Register for this episode of The Briefing Room to learn from Bloor Group CEO Eric Kavanagh, who will explain how a tightly controlled methodology can be combined with modern NoSQL technology to resolve both process and system complexities, thus enabling a much richer, more interconnected information landscape. Kavanagh will be briefed by Geoffrey Malafsky of Phasic Systems who will share his company's tested methodology for capturing and managing the business and process logic that run today's data-driven organizations. He'll demonstrate how a “don't say no” approach to entity definitions can dissolve previously intractable disagreements, opening the door to clear, verifiable operational intelligence. Visit: http://www.insideanalysis.com

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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