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Zeppelin has become a popular way to unlock the value of data lake due to its user interface and appeal to business users. These business users ask their IT department for access to Zeppelin. Enterprise IT department want to help their business users but they have several enterprise concerns such as enterprise security, integration with their corporate LDAP/AD, scalability and multi-user environment, integration with Ranger and Kerberos. This session will walk through enterprise concerns and how these concerns can be handled with Zeppelin.

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Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.

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Financial services companies can reap tremendous benefits from 'Big Data' and they have moved quickly to deploy it. But these companies also place heavy demands on 'Big Data' infrastructure for flexibility, reliability and performance. In this webinar, Hortonworks joins WANDisco to look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn: How to leverage data from across an entire global enterprise How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions What industry leaders have put in place

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Customers are preparing themselves to analyze and manage an increasing quantity of structured and unstructured data. Business leaders introduce new analytical workloads faster than what IT departments can handle. Legacy IT infrastructure needs to evolve to deliver operational improvements and cost containment, while increasing flexibility to meet future requirements. By providing HDP on IBM Power Systems, Hortonworks and IBM are giving customers have more choice in selecting the appropriate architectural platform that is right for them. In this webinar, we’ll discuss some of the challenges with deploying big data platforms, and how choosing solutions built with HDP on IBM Power Systems can offer tangible benefits and flexibility to accommodate changing needs.

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Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.

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Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges. Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.

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Apache Solr is the open source platform for searching data stored in Hadoop. Solr powers search on many of the world's largest Internet sites, enabling powerful full-text search and near real-time indexing. Whether users search for tabular, text, geo-location or sensor data in Hadoop, they find it quickly with Apache Solr. Hortonworks Data Platform 2.1 includes Apache Solr. In this deck from their 30-minute webinar, Rohit Bakhshi, Hortonworks product manager, and Paul Codding, Hortonworks solution engineer describe how Solr works within HDP's YARN-based architecture.

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As more and more enterprises are adapting to utilize hadoop, integrating the enterprise users into the hadoop environment and enabling these users to access hadoop services and clusters becomes critical. Typically, enterprises manage the users and groups through directory servers and Hadoop services need to integrate with these enterprise directory servers (like Active Directory, LDAP etc...). This process presents its own unique set of challenges. In this session, the speakers review the background of user and group management for Hadoop as well as covers various scenarios supported in Apache Ranger while interacting with AD or LDAP servers. This session also examines various use cases and some of the common challenges faced in the enterprise environments while interacting with Active Directory or LDAP server including filtering out users and/or groups to be brought into Hadoop from AD or LDAP servers, restricting access to a set of users and/or groups from AD, etc…

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As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different, What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them. We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you. This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution. Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks

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Hadoop has become a backbone of many enterprises. While it can do wonders for businesses, it sometimes can be overwhelming for its operators and users. Amateurs as well as seasoned operators of Hadoop are caught unaware by common pitfalls of deploying, tuning and operating a Hadoop cluster. Having spent 5+ years working with 100s of Hadoop users, running clusters with 1000s of nodes, managing 10s of petabytes of data and running 100s of 1000s of tasks per day, we have seen people's unintentional acts, suboptimal configurations and common mistakes have resulted into downtimes, SLA violations, many hours of recovery operations and in some cases even data loss! Most of these traumas could have been easily avoided by applying easy to follow best practices that would protect data and optimize performance. In this talk we present real life stories, common pitfalls and most importantly, strategies on how to correctly deploy and manage Hadoop clusters. The talk will empower users and help make their Hadoop journey more fulfilling and rewarding. We will also discuss SmartSense. SmartSense can identify latent problems in a cluster and provide recommendations so that an operator can fix them before they manifest as a service degradation or outage.

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Mon-Fong Mike Jiang, Kuan-Yu Hubert Fan-Chiang and Tienyu Rebecca Lin 自2011年起，我們就開始使用HBase作為結構化大數據的儲存工具，主要是做為半導體製造設備參數的分析。為了有效進行數據查詢，我們開發Standard Query Language(SQL)的整合介面，最早的方式是(1)自行開發GUI操作介面及(2)透過自行定義SQL語法的方式進行，但是這樣會衍生出很多額外的工作，特別是SQL Parser與對應的HBase API的連結。為了解決此問題，我們解析了Hive QL Parser作為主要的核心，將此部分的原始碼整合進HareDB HBase Client之中，另外，也整合了HBase Coprocessor，可以加速查詢的進行，這個架構我們實際使用在數個半導體製造廠的大數據系統中，也展現了高查詢效率。除此之外，透過整合Kafka來處理串流數據的匯入，同時對於數據分析的呈現也加上Cube建立工具，這些都是實際開發大數據系統時陸續面對的問題與解決方法，我們將分享這一連串的系統開發過程。 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

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HBASE is the leading NoSQL database. Tightly integrated with Hadoop ecosystem, it offers random, real-time read/write capabilities on billions of rows and millions of columns. Apache Phoenix offers a SQL interface to HBASE, opening HBase to large community of SQL developers and enabling inter-operability with SQL compliant applications. The session will cover the essentials of HBASE and provide an in-depth insight into Apache Phoenix. Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community. Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=de6d0c435c0761adedf3114a100e7483%20

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With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers. With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer. https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/

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The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance. Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/

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Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI. Presented at Gartner Data and Analytics Summit Speaker: Dinesh Chandrasekhar Director of Product Marketing, Hortonworks

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