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Was ist angesagt?

Watch full webinar here: https://bit.ly/2EpHGyd Presented at Data Champions, Online Asia 2020 Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention. In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations: - Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time - Build a single engine for security that provides audit and control by geographies - Accelerate delivery of insights from your advanced analytics project

A Key to Real-time Insights in a Post-COVID World (ASEAN)

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Big data fabric combines essential big data capabilities in a single platform to automate the many facets of data discovery, preparation, curation, orchestration, and integration across a multitude of data sources. Attend this session to learn how Big Data Fabric enabled by data virtualization constitutes a recipe for: • Enabling new actionable insights with minimal effort • Securing big data end-to-end • Addressing big data skillset scarcity • Providing easy access to data without having to decipher various data formats Agenda: • Big Data with Data Virtualization • Product Demonstration • Summary & Next Steps • Q&A Watch webinar on demand here: https://goo.gl/EpmIBx This webinar is part of the Data Virtualization Packed Lunch Webinar Series: https://goo.gl/W1BeCb

Big Data Fabric: A Recipe for Big Data Initiatives

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Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptQMW7 What matters the most for analysts and decision makers is finding the right data within seconds. Data virtualization incorporates a rich metadata catalog and graphical interface for the self-service users In this session, you will learn: • How to discover, search, explore, curate and share trusted data assets in a governed manner • How to view and utilize the complete lineage of data assets • Ways to infer patterns in data and metadata This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6

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Watch: https://bit.ly/3iZUf2o Data Virtualization has gone beyond its initial promise and is now becoming a critical component of an adaptive, agile enterprise data fabric. But there are common misconceptions around Data Virtualization and how it works. In this session, we will examine these misconceptions and set the record straight. According to Gartner, organizations with data virtualization will spend 40% less on building and managing data integration processes for connecting distributed data assets, and 60% of all organizations on track to implement some sort of data virtualization by 2022. This solidifies data virtualization as a critical piece of technology for modern data architecture. Agenda: - Why Data Virtualization? And why now? - How Data Virtualization can turbocharge your enterprise data strategy - Typical use cases about Data Virtualization - Demystifying the misconceptions about Data Virtualization Our speakers bring years of experience launching data strategies, and they will share success stories and lessons learned. Learn how you can enhance the competitive edge for your business in this webinar hosted by Orion Innovation and Denodo. We look forward to seeing you online.

Demystifying Data Virtualization: Why it’s Now Critical for Your Data Strategy

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Watch full webinar here: https://buff.ly/2HMdbUp What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics. Attend this session to learn: • What data virtualization really is, • How it differs from other enterprise data integration technologies • Real-world examples of data virtualization in action from companies such as Logitech, Autodesk and Festo.

DAMA Webinar: Turn Grand Designs into a Reality with Data Virtualization

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Watch full webinar here: https://bit.ly/3dMN503 Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way. Watch this session to learn how companies can use data virtualization to: - Create a logical architecture to make all enterprise data available for advanced analytics exercise - Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice - Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc

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Watch full webinar here: https://bit.ly/32jYpiD Data fragmentation, multiple data sources and interoperability are a significant part of the challenges facing modern healthcare. We will focus our session on how to address these through a combination of a Universal Healthcare Data Fabric that leverages Denodo’s latest platform, as well as components that have been developed specifically for healthcare systems, based on the FHIR standard.

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Data Architecture for Machine Learning

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Today’s data-driven organisations are turning to Databricks for a cloud-based, open, unified platform for data and AI. Yet many companies struggle to unlock the value of the data they have in Databricks. To capitalise on the promise of a competitive edge through increased efficiency and insight, data scientists are turning to location to make sense of massive volumes of business data. Watch this on-demand to hear from The Spatial Distillery Co. and Databricks on how to leverage advanced location intelligence and enrichment solutions in Databricks to: - Simplify the complexity of location data and transform it into valuable insights - Enrich data with thousands of attributes for better, more accurate analytics, AI, and ML models - Leverage the power of Databricks to integrate geospatial data into business processes for real-time answers - Create more meaningful and timely customer interactions by streamlining customer-facing and operational tasks

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Data fabric architectures are used to simplify and integrate data management across business functions to accelerate digital transformation. Creating a data fabric is a way to develop a data-centric view of your business which results in an Enterprise 360 perspective based on trusted data. Industry analysts and vendors are increasingly finding that graph databases are a key enabling technology in support of Data Fabric architectures that deliver trusted data. During this on-demand webinar, we discuss how we help our customers implement a Data Fabric pattern using graph database technology in support of their key strategic objectives.

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Watch full webinar here: https://bit.ly/2Qpwqo9 Insurers’ globally are accelerating their digital journey, making rapid strides with their digitisation efforts, and adding key capabilities to adapt and innovate in the new normal. However, many insurance organisations find this transformation challenging as they rely on established systems that are often not only poorly integrated, but also highly resistant to modernisation without downtime. Hear how peers in your industry are leveraging data virtualization that facilitates digital transformation via a modern data integration / data delivery approach to gain greater agility, flexibility, and efficiency. In this session, you will learn: - Industry key trends and challenges driving the digital transformation mandate - What is data virtualization, use cases, and how it can enable insurers to develop critical capabilities - Lessons from success stories of insurers who already use data virtualization to differentiate themselves from the competition, have a single view of all their data and a way to establish security controls across the entire infrastructure

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As digital channels continue to grow, they drive greater diversity in our data landscape. At Yorkshire Building Society, our purpose is to provide real help with real life and this relies on data from a myriad of sources. This diversity creates a need for points of intersection, where data can unite to feed customer and business insights. How do we create these hubs of intersection and what can modern technology offer? Speaker: Mark Walters Lead Enterprise Data Architect for Data & Information Yorkshire Building Society

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CIT modernized its data architecture in response to intense regulatory scrutiny. In this presentation, they present how data virtualization is being used to drive standardization, enable cross-company data integration, and serve as a common provisioning point from which to access all authoritative sources of data. This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/CCqUeT.

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Watch: https://bit.ly/327z8UM While the impact of COVID-19 is uniform across organisations in the region, a lot of how the organisation can recover from the impact and strive in the market would depend on their resiliency and business agility. An organisation’s data management strategy holds the key, as they tackle the challenges of siloed data sources, optimising for operational stability, and ensuring real time delivery of consistent and reliable information, irrespective of the data source or format. Join this session to hear why large organisations are implementing Data Virtualization, a modern data integration approach in their data architecture to build resiliency, enhance business agility, and save costs. In this session, you will learn: - How to deliver clear strategy for agile data delivery across the enterprise without pains of traditional data integration - How to provide a robust yet simple architecture for data governance, master data, data trust, data privacy and data access security implementation - all from single unified framework - How to deploy digital transformation initiatives for Agile BI, Big Data, Enterprise Data Services & Data Governance

Building Resiliency and Agility with Data Virtualization for the New Normal

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Watch full webinar here: https://buff.ly/2GNO8PC What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses (DW) and data lakes are not easy to deal with for agile data access. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources. Attend this webinar to learn: * How easily new data sources can be made available for analytics and data science * How your organization can successfully migrate to a flexible LDW architecture in a step-by-step fashion * How LDWs help integrate self-service analytics with classic forms of business intelligence

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Was ist angesagt? (20)

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Building a Single Logical Data Lake: For Advanced Analytics, Data Science, an...

Big Data Fabric: A Recipe for Big Data Initiatives

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Watch Here: https://bit.ly/2NcqU6F We take on the 2nd myth about data virtualization and it’s one that suggests a BI tool can substitute a data virtualization software. You might be thinking: If I can have multi-source queries and define a logical model in my reporting tool, why would I need a data virtualization software? Reporting tools, no doubt important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data. Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on! Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.

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Watch full webinar here: https://bit.ly/3aWI8lt Self-service is a major goal of modern data strategists. A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. As data unification and data collaboration become key critical success factors for organisations, data catalogs play a key role as the perfect companion for a virtual layer to fully empower those self-service initiatives and build a self-service data marketplace requiring minimal IT intervention. Denodo’s Data Catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It provides business users with the tool to generate their own insights with proper security, governance, and guardrails. In this session we will cover: - The role of a virtual semantic layer in self-service initiatives - Key ingredients of a successful self-service data marketplace - Self-service (consumption) vs. inventory catalogs - Best practices and advanced tips for successful deployment - A Demonstration: Product Demo - Examples of customers using Denodo’s Data Catalog to enable self-service initiatives

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This session will detail best practices for architecting, building, operating and managing an Analytics Data Lake platform. Key topics will include: 1) Defining next-generation Data Lake architectures. The defacto standard has been commodity DAS servers with HDFS, but there are now multiple solutions aimed at separating compute and storage, virtualizing or containerizing Hadoop applications, and utilizing Hadoop compatible or embedded HDFS filesystems. This portion will explore the options available, and the pros and cons of each. 2) Data Ingest. There are many ways to load data into a Data Lake, including standardized Apache tools (Sqoop, Flume, Kafka, Storm, Spark, NiFi), standard file and object protocols (SFTP, NFS, Rest, WebHDFS), and proprietary tools (eg, Zaloni Bedrock, DataTorrent). This section will explore these options in the context of best fit to workflows; it will also look at key gaps and challenges, particularly in the areas of data formats and integration with metadata/cataloging tools. 3) Metadata & Cataloguing. One of the biggest inhibitors of successful Data Lake deployments is Data Governance, particularly in the areas of indexing, cataloguing and metadata management. It is nearly impossible to run analytics on top of a Data Lake and get meaningful & timely results without solving these problems. This portion will explore both emerging open standards (Apache Atlas, HCatalog) and proprietary tools (Cloudera Navigator, Zaloni Bedrock/Mica, Informatica Metadata Manager), and balance the pros, cons and gaps of each. 4) Security & Access Controls. Solving these challenges are key for adoption in regulatory driven industries like Healthcare & Financial Services. There are multiple Apache projects and proprietary tools to address this, but the challenge is making security and access controls consistent across the entire application and infrastructure stack, and over the data lifecycle, and being able to audit this in the face of legal challenges. This portion will explore available options and best practices. 5) Provisioning & Workflow Management. The real promise of the Data Lake is integrating Analytics workflows and tools on converged infrastructure-with shared data-and build “As A Service” oriented architectures that are oriented towards self-service data exploration and Analytics for end users. This is an emerging and immature area, but this session will explore some potential concepts, tools and options to achieve this. This will be a moderately technical session, with the above topics being illustrated by real world examples. Attendees should have basic familiarity with Hadoop and the associated Apache projects.

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