We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Hortonworks - IBM Cognitive - The Future of Data ScienceThiago Santiago
The document discusses Hortonworks and IBM's partnership around data management and analytics. It highlights how their combined platforms can power the modern data architecture with solutions for data at rest and in motion. Examples are provided of how customers like Merck and JPMC have leveraged Hortonworks' technologies to gain insights from their data and drive business outcomes. Industries that are investing in data science are also listed.
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
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/
Risk listening: monitoring for profitable growthDataWorks Summit
Historically, insurers used 50-, 100-, and 500-year flood models for risk evaluation and pricing. The extreme weather events we have experienced in 2017 alone prove how dated these methods really are.
To better understand their customers and potential current/future liability claims, forward thinking insurers are monitoring, analyzing, and integrating external data sources in real time (weather feeds from USGS.gov, news and stock feeds, and satellite imagery, to name just a few). By integrating and injecting these new data sources into their risk models and underwriting, insurers are better able to identify their risk appetites and effectively price.
The session will include real-world case studies, including how a global P and C insurer is now quickly analyzing and monitoring 50,000 customers and targets, gaining new insights into the market. Another example is a global reinsurance and specialty company that now leverages digital news channels to monitor its risk portfolio for early warning claims indicators to help drive down loss costs. CINDY MAIKE, VP Industry Solutions, GM of Insurance, Hortonworks, Inc.
Enterprise Data Science at Scale Meetup - IBM and Hortonworks - Oct 2017 Hortonworks
The document discusses Hortonworks' Data Science Experience (DSX) platform. It describes challenges data scientists face around data access, tool usage, collaboration and model deployment. DSX aims to address these by providing tools for exploring, modeling and deploying data science projects on Hortonworks Data Platform (HDP) clusters at scale. It also announces an extension of IBM and Hortonworks' partnership to integrate DSX and other IBM data science tools with HDP.
Decide if PhoneGap is for you as your mobile platform selectionSalim M Bhonhariya
The document discusses strategies for developing a mobile application. It compares web applications, hybrid applications, and native applications. Hybrid applications like PhoneGap allow developing using HTML5/JavaScript while accessing device features, providing a compromise between web and native. The document suggests PhoneGap is best if performance and user experience are not primary concerns and a shorter timeline is needed, as it allows building once and releasing across platforms quickly. Otherwise, native may be preferable for the best performance, experience, and access to device features.
IIoT + Predictive Analytics: Solving for Disruption in Oil & Gas and Energy &...DataWorks Summit
The electric grid has evolved from linear generation and delivery to a complex mix of renewables, prosumer-generated electricity, and electric vehicles (EVs). Smart meters are generating loads of data. As a result, traditional forecasting models and technologies can no longer adequately predict supply and demand. Extreme weather, an aging infrastructure, and the burgeoning worldwide population are also contributing to increased outage frequency.
In oil and gas, commodity pricing pressures, resulting workforce reductions, and the need to reduce failures, automate workflows, and increase operational efficiencies are driving operators to shift analytics initiatives to advanced data-driven applications to complement physics-based tools.
While sensored equipment and legacy surveillance applications are generating massive amounts of data, just 2% is understood and being leveraged. Operationalizing it along with external datasets enables a shift from time-based to condition-based maintenance, better forecasting and dramatic reductions in unplanned downtime.
The session includes plenty of real-world anecdotes. For example, how an electric power holding company reduced the time it took to investigate energy theft from six months to less than one hour, producing theft leads in minutes and an expected multi-million dollar ROI. How a global offshore contract drilling services provider implemented an open source IIoT solution across its fleet of assets in less than a year, enabling remote monitoring, predictive analytics and maintenance.
Key takeaways:
• How are new processes for data collection, storage and democratization making it accessible and usable at scale?
• Beyond time series data, what other data types are important to assess?
• What advantage are open source technologies providing to enterprises deploying IIoT?
• Why is collaboration important across industrial verticals to increase IIoT open source adoption?
Speaker
Kenneth Smith, General Manager, Energy, Hortonworks
In this webinar, we will hear from Mark McKinney, Director – Enterprise Data Analytics at Sprint about the business drivers, key success factors, and challenges faced while undertaking Sprint’s data modernization journey. You will hear how Sprint set about establishing a Hadoop data lake, ingested data from multiple environments, and overcame key skill shortages. You will also hear from Diyotta and Hortonworks about best practices for modernizing your data architecture to support transformational business initiatives.
https://hortonworks.com/webinar/sprints-data-modernization-journey/
Insurance companies of all sizes are challenged to keep up with emerging technologies that deliver a competitive advantage. Recording: https://www.brighttalk.com/webcast/9573/192877
Big data holds the key to greater customer insight and stronger customer relationships. But risk of sensitive data exposure — and compliance violations — keeps many insurers from pursuing big data initiatives and reaping the rewards of business-driven analytics. Join Dataguise and Hortonworks for this live webinar to learn how you can free your organization from traditional information security constraints and unlock the power of your most valuable business assets.
• What do you need to know about PII/PHI privacy before embarking on big data initiatives?
• Why do so many big data initiatives fail before they’ve even begun—and what can you do about it?
• How can IT security organizations help data scientists extract more business value from their data?
• How are leading insurance companies leveraging big data to gain competitive advantage?
Global Data Management – a practical framework to rethinking enterprise, oper...DataWorks Summit
Global data management is not a newly coined term. However, what it stands for is actually widening in scope particularly around data-in-motion and data-at-rest. Significant technology trends such as IoT, cloud, AI/ML, blockchain, and streaming data have given rise to excessive data volumes and also innovative use cases. The scope for global data management now extends all the way from ingestion, processing, storage, governance, security to analysis. With a good number of endpoints served through the cloud and major application footprints remaining on-premisess, it is pertinent to have a global data management strategy that supports hybrid models and more specifically, a multi-cloud model.
Many modern businesses struggle to balance the demands of rapidly innovating through new technologies like machine learning with the need to keep data safe and secure, all while responding to a constantly changing regulatory landscape. This puts data stewards, data engineers, architects, data scientists, and analysts under intense pressure as they must contend with existing and new applications, multiple logical and physical data stores and sources, diverse data types, and data spread across several deployment environments.
Attend this session led by Matt Aslett, Research Director at 451 Research and Dinesh Chandrasekhar, Director, Hortonworks to learn more about creating a framework for your enterprise that offers guidance on how to think about global data management—priorities, responsibilities, key stakeholders, compliance, and growth.
Speakers
Dinesh Chandrasekhar, Hortonworks, Director Product Marketing
Matt Aslett, 451 Research, Research Director, Data platforms and Analytics
Powering the Intelligent Edge: HPE's Strategy and Direction for IoT & Big DataDataWorks Summit
Powering the Intelligent Edge is one of the three pillars of Hewlett Packard Enterprise's corporate strategy. The session will cover HPE’s strategic direction and approach in the areas of IoT and data analytics. Join the discussion and learn how HPE’s solutions can help businesses prepare for the big data era.
Real time trade surveillance in financial marketsHortonworks
Who’s winning the deep forensic analysis ‘arms race’ for compliance? Real-time trade surveillance in global financial markets has created a data tsunami. With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water? Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations. Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen. In-depth expert presentations by:
Shailesh Ambike, Executive Co-Chair of Compliance & Legal Section (CLS) Education Sub-Committee of the Investment Industry Regulatory Organization of Canada (IIROC)
Vamsi K Chemitiganti, GM – Financial Services at Hortonworks
This document provides an overview of Hortonworks and Hadoop. It discusses Hortonworks' customer momentum, the Hortonworks Data Platform (HDP), and Hortonworks' role as a partner for customer success. It also summarizes challenges with traditional data systems, how Hadoop emerged as a foundation for a new data architecture, and how HDP delivers a comprehensive data management platform.
Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality) and unplanned downtime costs plants approximately $50 billion per year (Deloitte).
The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors.
The speakers address the data management challenges facing today's manufacturers, including proprietary systems and silo'ed data sources, as well as an inability to make sensor-based data usable.
Integrating enterprise data from ERP, MES, maintenance systems and other sources with real time operations data from sensors, PLCs, SCADA systems and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action?
Join us for this educational session, which includes a rare view from one of our SWAT team experts into our roadmap for an open source industrial IoT data management platform.
Key Takeaways:
• How to choose an initial project from which to quickly demonstrate high value returns
• Understand the value of multivariate data sources, as opposed to a single sensor on a piece of equipment
• Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance
MICHAEL GER, General Manager, Manufacturing and Automotive, Hortonworks and RYAN TEMPLETON, Senior Solutions Engineer, Hortonworks
Using The Internet of Things for Population Health Management - StampedeCon 2016StampedeCon
The Internet of (Human) Things is just beginning to take shape. The human body is an inexhaustible source of data about personal health, and the healthcare industry is just beginning to scratch the surface of the potential insights and value that will come from that data. While much of healthcare traditionally focuses on the episodic delivery of services, the Affordable Care Act is pushing healthcare providers, payers, and self-funded employer groups to look at ways to proactively encourage healthy behaviors. Providing personal health devices as a way to promote individual health is one way that healthcare is beginning to take advantage of IoT technologies. This session provides insight into how IoT is being leveraged in population health management through a solution jointly delivered by Amitech Solutions and Big Cloud Analytics. Attendees will learn how Hadoop is being used to gather personal device from various vendors, integrate and analyze that information, differentiate trends across regional and cultural diversity, and provide personal recommendations and insights into health risks. This session presents one important way the healthcare industry is leveraging IoT.
What's new in Hortonworks DataFlow 3.0 by Andrew PsaltisData Con LA
Abstract:- Hortonworks DataFlow (HDF) is built with the vision of creating a platform that enables enterprises to build dataflow management and streaming analytics solutions that collect, curate, analyze and act on data in motion across the datacenter and cloud. Do you want to be able to provide a complete end-to-end streaming solution, from an IoT device all the way to a dashboard for your business users with no code? Come to this session to learn how this is now possible with HDF 3.0.
Reinvent Your Data Management Strategy for Successful Digital TransformationDenodo
Watch Dinesh's keynote presentation from Fast Data Strategy Virtual Summit here: https://goo.gl/3Pa8np
Leaders are re-inventing their data management strategies through the effective use of IoT, Big Data, and data science to boost their customer experience. Yet, they struggle to modernize their data architecture due to lack of global data management processes and technologies.
Attend this session to hear from the Big Data pioneer, Hortonworks:
• Why big data and data virtualization should be core technology components of your digital transformation.
• How to manage, govern, and secure your global data footprint across a hybrid multi-cloud landscape.
• Learn about key global data management strategies and use cases that drive leading digital enterprises.
The newly enacted GDPR regulations which become effective in 2018 require comprehensive protection of personal information of EU subjects. In this paper, we outline a solution that discovers and classifies personal data that is subject to GDPR in Hadoop ecosystem and uses such precise classification to automatically create a robust set of policies for authorization. The solution consists of using Dataguise’s DgSecure sensitive data detection to automatically classify sensitive data assets in Apache Atlas and author comprehensive and robust authorization policies via Apache Ranger. DgSecure is used to detect sensitive data in Hive databases and continuously update the classification in Apache Atlas via tags. Apache Atlas tags are used to create Apache Ranger policies that protect access to sensitive HDFS files, Hive tables, and Hive columns. We demonstrate a workflow where the components of the solution are automated requiring little or no manual intervention to provide protection of such sensitive data in Hadoop clusters.
Hortonworks Hybrid Cloud - Putting you back in control of your dataScott Clinton
The document discusses Hortonworks' solutions for managing data across hybrid cloud environments. It proposes getting all data under management, combating growing cloud data silos, and consistently securing and governing data across locations. Hortonworks offers the Hortonworks Data Platform, Hortonworks Dataflow, and Hortonworks DataPlane to provide a modern hybrid data architecture with cloud-native capabilities, security and governance, and the ability to extend to edge locations. The document also highlights Hortonworks' professional services and open source community initiatives around hybrid cloud data.
Running Enterprise Workloads with an open source Hybrid Cloud Data ArchitectureDataWorks Summit
The document discusses Hortonworks DataPlane Service (DPS), a platform that provides consistent security, governance, and management of data across hybrid cloud environments. Key capabilities of DPS include data lifecycle management using Data Lifecycle Manager (DLM), data discovery and profiling through Data Steward Studio (DSS), and self-service analytics with Data Analytics Studio (DAS). DPS provides a global data fabric to address challenges of securing, governing, and delivering data across multiple data sources and locations.
This document discusses various aspects of securing a Hadoop data lake, including authentication and perimeter security. It provides background on Kerberos and how it can be used for authentication in Hadoop. It describes how Ambari can be used to automate Kerberos configuration. It also discusses using Knox as a gateway to control access to Hadoop APIs and provide single sign-on capabilities.
This document discusses securing a Hadoop data lake with Kerberos authentication. It provides background on Kerberos and how it establishes identity for clients, hosts, and services. When used with Hortonworks Data Platform (HDP), Kerberos secures HDP components by managing Kerberos identities (principals) via keytabs on component hosts. The keytabs are generated and distributed by Ambari in an automated process that integrates with existing identity stores like Active Directory. Kerberos prevents impersonation and passwords from being sent in plain text, providing a secure authentication mechanism for the data lake.
IBM Cloud Paris meetup 20180213 - HortonworksIBM France Lab
This document discusses Hortonworks' Data Lake 3.0 architecture and Hadoop 3.0 capabilities. It summarizes Hortonworks' mission to make Hadoop an enterprise data platform that manages all data sources and types. It describes features of Data Lake 3.0 like scalability, containerized microservices, storage efficiency with erasure coding, and support for GPUs and AI/deep learning workloads. It also outlines capabilities in Hadoop 3.0 such as HDFS federation for linear scaling, Ozone for an object store, and more powerful YARN functionality including resource isolation and Docker support.
The document discusses Hortonworks' DataPlane Service (DPS) platform. DPS provides tools to manage data across hybrid cloud and on-premise environments, including data lifecycle management, data governance, analytics, and cluster deployment. Specifically, it introduces several services available on DPS: Data Lifecycle Manager (for replication and tiering), Data Steward Studio (for governance and lineage), Data Analytics Studio (for analytics), and Cloudbreak (for cloud cluster deployment). The goal of DPS is to provide a common platform to manage data across different environments and sources through extensible services.
Denodo DataFest 2017: Edge Computing: Collecting vs. Connecting to Streaming ...Denodo
This document discusses connected data and edge computing. It summarizes that connected devices, customers, vehicles, and assets are fueling new business models powered by streaming data, artificial intelligence, cloud computing, and the internet of things. It then describes Hortonworks' data platforms for managing both data at rest and in motion across cloud, on-premises and hybrid environments to enable analytics and power the modern data architecture.
This document provides an overview of Hortonworks DataFlow (HDF) 3.1 and its new and enhanced features. Key highlights include improved ease of use through integration with Atlas, SmartSense and Knox, enhanced flow management capabilities with Apache NiFi Registry for version control and migration of flows, and improved operational efficiency through features like test mode in Streaming Analytics Manager and group-based policy support in Ranger. The document also discusses use cases for data ingestion, stream processing, and enterprise data movement between data centers and cloud.
The document discusses Hortonworks' data science platform and solutions. It highlights key features such as running data science tools like Spark and Zeppelin on Hortonworks Data Platform (HDP) clusters, bringing predictive models into production, and delivering insights to business users. The document also provides an example use case of using the platform to predict customer churn and alert departments in real time.
Achieving a 360-degree view of manufacturing via open source industrial data ...DataWorks Summit
Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality), and unplanned downtime costs plants approximately $50 billion per year (Deloitte).
The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors.
The speakers address the data management challenges facing today's manufacturers, including proprietary systems and siloed data sources, as well as an inability to make sensor-based data usable.
Integrating enterprise data from ERP, MES, maintenance systems, and other sources with real-time operations data from sensors, PLCs, SCADA systems, and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action?
Join us for this educational session, which includes a view into a roadmap for an open source industrial IoT data management platform.
Key Takeaways:
• Understand key use cases commonly undertaken by manufacturing enterprises
• Understand the value of using multivariate manufacturing data sources, as opposed to a single sensor on a piece of equipment
• Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance
Speakers
Michael Ger, General Manager Manufacturing and Automotive, Hortonworks
Wade Salazar, Solutions Engineer, Hortonworks
Data in Motion - Data at Rest - Hortonworks a Modern ArchitectureMats Johansson
Presentation at Data Innovation Summit 2016 in Stockholm
How to build a modern data architecture supporting data in motion and data at rest with Hortonworks Data Flow and Data Platform.
Running Enterprise Workloads with an Open Source Hybrid Cloud Data ArchitectureDataWorks Summit
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Speaker: Alan Gates, Co-Founder, Hortonworks
Apache Spark in Cloud and Hybrid: Why Security and Governance Become More Imp...Spark Summit
An Increasing number of Apache Spark deployments are in Cloud and hybrid environments. This often means that Spark workloads are ephemeral but the data exists in a durable storage either in cloud and on-prem. The data also moves between cloud storage and on-prem. With this architecture in place, security and governance have become paramount to run Spark workloads across on-prem and cloud. In this keynote, we will walk through several issues and highlight a Spark workload running in an ephemeral cluster with security and governance across Cloud/On-Prem and how the same security and governance is shared with other workloads.
1. Sanjay Radia discusses designing data systems for data everywhere, including on-premises and in the cloud.
2. He advocates for applying a consistent set of tools, managing data in one place, and sharing security and governance across systems.
3. Radia also notes the importance of leveraging innovation from open source communities to continually improve the systems.
Discover Red Hat and Apache Hadoop for the Modern Data Architecture - Part 3Hortonworks
The document discusses using Hortonworks Data Platform (HDP) and Red Hat JBoss Data Virtualization to create a data lake solution and virtual data marts. It describes how a data lake enables storing all types of data in a single repository and accessing it through tools. Virtual data marts allow lines of business to access relevant data through self-service interfaces while maintaining governance and security over the central data lake. The presentation includes demonstrations of virtual data marts integrating data from Hadoop and other sources.
Running Enterprise Workloads with an open source Hybrid Cloud Data Architectu...DataWorks Summit
Cloud accelerates corporate IT landscapes with agility and flexibility. Today, discussion of cloud architecture dominates corporate IT. The cloud enables a number of temporary on-demand use cases that revolutionize analytical workload opportunities. But all of this involves the task of running corporate workloads safely and easily in the cloud.
With the convergence of cloud, IoT, and big data technology, enterprises are increasingly using multiple on-premises Data Lake and multiple Public on different geographies, for example due to regulations and compliance requirements restricting cross- It now distributes data to the cloud Data Lake store of the cloud vendor platform. Diffusion of data types and sources in this complex landscape makes the discovery process, provisioning, and getting insight by performing the appropriate workload on this data more complicated. In addition, to obtain business context, usage, and visibility of data trustworthiness worldwide, it is necessary to display all data and metadata, security management, data access, and monitoring in a centralized way .
All these problems create cracks during the creation of data insights to promote initial data capture and subsequent value creation. As a result, companies now look for compromises between appropriate rules and data control policies while providing a trusted environment that allows them to share data and partner with users responsibly to create value We need "Global Insight Fabric".
In this talk, how the Hortonworks DataPlane Service (DPS) analyzes the data in the data center to expand the storage, implement the open source hybrid architecture utilizing cloud flexibility and new use cases, global in Describes how site fabrics can help customers create. Securely migrate data from on-premises data centers to multiple public clouds, protect the data with replication, then apply consistent safety and governance policies to a wide variety of environments to ensure trustworthy data and inn We provide personal views on the challenges we face in providing the site to the business. I will explain how the DetaPlane service can be useful for traveling to this hybrid architecture and how the open source architecture enables the transformation of the entire enterprise.
Real-time Twitter Sentiment Analysis and Image Recognition with Apache NiFiTimothy Spann
A walk through of creating a dataflow for ingest of twitter data and analyzing the stream with NLTK Vader Python Sentiment Analysis and Inception v3 TensorFlow via Python in Apache NiFi. Storage in Hadoop HDFS.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
The document provides an overview of Apache Druid, an open-source distributed real-time analytics database. It discusses Druid's architecture including segments, indexing, and nodes like brokers, historians and coordinators. It also covers integrating Druid with Hortonworks Data Platform for unified querying and visualization of streaming and historical data.
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.
4 Essential Steps for Managing Sensitive DataHortonworks
Data is growing in data lakes, so are security and compliance risks. These risks stem from storing and processing sensitive data. In this webinar, we will go through a 4 step process to proactively discover and manage sensitive data within big data environments.
https://hortonworks.com/webinar/4-essential-steps-managing-sensitive-data-data-lake/
5 Steps to Create a Company Culture that Embraces the Power of DataHortonworks
A business culture that relies on gut checks and feelings for business decisions is a hard hurdle to overcome. Company culture is often the biggest barrier to moving a company toward data-driven decisions. There's a way to get there, when driven by company leaders. Here's how you do that:
1. Get comfortable with softer data sets
2. Must come from top-down
3. A structure where goals are clear
4. Right role for technology
5. Clear stewardship around data
Exploring the Heated-and Completely Unnecessary- Data Lake DebateHortonworks
When it comes to the data lakes and data warehouses, there’s no shortage of controversy: Is one better than the other? The real answer is, there’s no need for heated debate—a data lake actually complements the data warehouse.
Integrating a data lake with your EDW is really just an evolution of architecture that can provide you with a cross-environment that allows you to explore data creatively to yield great business insights. However, there’s a trick to making it work: EDW optimization.
https://hortonworks.com/webinar/exploring-heated-completely-unnecessary-data-lake-debate/
Modernize Your Existing EDW with IBM Big SQL & Hortonworks Data PlatformHortonworks
Find out how Hortonworks and IBM help you address these challenges to enable success to optimize your existing EDW environment.
https://hortonworks.com/webinar/modernize-existing-edw-ibm-big-sql-hortonworks-data-platform/
Benefits of Transferring Real-Time Data to Hadoop at ScaleHortonworks
Today’s Big Data teams demand solutions designed for Big Data that are optimized, secure, and adaptable to changing workload requirements. Working together, Hortonworks, IBM, and Attunity have designed an integrated solution that transfers large volumes of data to a platform that can handle rapid ingest, processing and analysis of data of all types from all sources, at scale.
https://hortonworks.com/webinar/benefits-transferring-real-time-data-hadoop-scale-ibm-hortonworks-attunity/
Streamline Apache Hadoop Operations with Apache Ambari and SmartSenseHortonworks
Apache Ambari 2.5 helps customers simplify the experience for provisioning, managing, monitoring, securing and troubleshooting Hadoop deployments. Find out how the combination of Ambari and SmartSense delivers a path to success to help IT get Hadoop up and running effectively. The end result – you get the full business impact management and benefits of Big Data for your organization.
https://hortonworks.com/webinar/streamline-apache-hadoop-operations-apache-ambari-smartsense/
How to Architect and Omnichannel Retail Solution to Achieve Real-Time Custome...Hortonworks
This document discusses an architecture for an omnichannel retail solution using SAS and Hortonworks technologies to achieve real-time customer insights. It outlines business challenges in retail like changing customer demographics and the impact of Amazon, and the need for a "market of one" approach. It then demonstrates a customer use case and shows the technology and architecture needed, including using Hortonworks Data Platform (HDP) for data management and analytics, and integrating SAS solutions both from and to HDP. Edge computing and data in motion capabilities are also discussed.
The Life of a Hadoop Administrator, with and without SmartSenseHortonworks
This cartoon shows how easy it is to troubleshoot and resolve support cases quickly, saving a Hadoop Administrator hours of time. By providing up-front access to diagnostic information needed to resolve issues, it helps reduce the back-and-forth nature of troubleshooting that consumes valuable time and resources.
Enterprise Data Warehouse Optimization: 7 Keys to SuccessHortonworks
You have a legacy system that no longer meet the demands of your current data needs, and replacing it isn’t an option. But don’t panic: Modernizing your traditional enterprise data warehouse is easier than you may think.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
Develop Secure Enterprise Solutions with iOS Mobile App Development ServicesDamco Solutions
The security of enterprise apps should not be overlooked by organizations. Since these apps handle confidential finance/user data and business operations, ensuring greater security is crucial. That’s why, businesses should hire dedicated iOS mobile application development services providers for creating super-secured enterprise apps. By incorporating sophisticated security mechanisms, these developers make enterprise apps resistant to a range of cyber threats.
Content source - https://www.bizbangboom.com/articles/enterprise-mobile-app-development-with-ios-augmenting-business-security
Read more - https://www.damcogroup.com/ios-application-development-services
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdfSelfMade bd
Imagine being able to generate high-quality traffic and leads effortlessly. Sounds like a dream, right? Well, it’s not. It’s called LeadMagnet IQ, and it’s here to revolutionize your marketing efforts.
(Note: Download the paper about this software. After that, click on [Click for Instant Access] inside the paper, and it will take you to the sales page of the product.)
Mastering OnlyFans Clone App Development: Key Strategies for SuccessDavid Wilson
Dive into the critical elements of OnlyFans clone app development, from understanding user needs and designing engaging platforms to implementing robust monetization strategies and ensuring scalability. Discover how RichestSoft can guide you through the development process, offering expert insights and proven strategies to help you succeed in the competitive market of content monetization.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
Challenges and Strategies of Digital Transformation.pptxwisdomfishlee
In an era where digital innovation is ubiquitous, executives from various corporations frequently seek insights into the tangible benefits that digital transformation can offer. This document outlines a comprehensive framework that elucidates the concept of digital transformation, highlighting its multifaceted dimensions and the pivotal roles it plays in enhancing business competitiveness.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Latest Tech Trends Series 2024 By EY IndiaEYIndia1
Stay ahead of the curve with our comprehensive Tech Trends Series! Explore the latest technology trends shaping the world today, from the 2024 Tech Trends report and top emerging technologies to their impact on business technology trends. This series delves into the most significant technological advancements, giving you insights into both established and emerging tech trends that will revolutionize various industries.