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    • Data Preparation Done Right
      Data Preparation Done Right Davinder Mundy, Specialist Big Data Technologies, Informatica Recorded: May 9 2018 9:00 am UTC 45 mins
    • How do you avoid your enterprise data lake turning into a so-called data swamp? The explosion of structured, unstructured and streaming data can be overwhelming for data lake users, and make it unmanageable for IT. Without scalable, repeatable, and intelligent mechanisms for cataloguing and curating data, the advantages of data lakes diminish. The key to solving the problem of data swamps is Informatica’s metadata driven approach which leverages intelligent methods to automatically discover, profile and infer relationships about data assets. Enabling business analysts and citizen integrators to quickly find, understand and prepare the data they are looking for.

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    • Succeeding with Big Data Analytics and Machine Learning in The Cloud
      Succeeding with Big Data Analytics and Machine Learning in The Cloud James E. Curtis Senior Analyst, Data Platforms & Analytics, 451 Research Upcoming: Oct 10 2018 5:00 pm UTC 60 mins
    • The cloud has the potential to deliver on the promise of big data processing for machine learning and analytics to help organizations become more data-driven, however, it presents its own set of challenges.

      This webinar covers best practices in areas such as.

      - Using automation in the cloud to derive more value from big data by delivering self-service access to data lakes for machine learning and analytics
      - Enabling collaboration among data engineers, data scientists, and analysts for end-to-end data processing
      - Implementing financial governance to ensure a sustainable program
      - Managing security and compliance
      - Realizing business value through more users and use cases

      In addition, this webinar provides an overview of Qubole’s cloud-native data platform’s capabilities in areas described above.

      About Our Speaker:

      James Curtis is a Senior Analyst for the Data, AI & Analytics Channel at 451 Research. He has had experience covering the BI reporting and analytics sector and currently covers Hadoop, NoSQL and related analytic and operational database technologies.

      James has over 20 years' experience in the IT and technology industry, serving in a number of senior roles in marketing and communications, touching a broad range of technologies. At iQor, he served as a VP for an upstart analytics group, overseeing marketing for custom, advanced analytic solutions. He also worked at Netezza and later at IBM, where he was a senior product marketing manager with responsibility for Hadoop and big data products. In addition, James has worked at Hewlett-Packard managing global programs and as a case editor at Harvard Business School.

      James holds a bachelor's degree in English from Utah State University, a master's degree in writing from Northeastern University in Boston, and an MBA from Texas A&M University.

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    • 5 Traps to Avoid and 5 Ways to Succeed with Big Data Analytics
      5 Traps to Avoid and 5 Ways to Succeed with Big Data Analytics Hal Lavender, Chief Architect, Cognizant, Thomas Dinsmore, BI & Big Data Expert, Josh Klahr, VP Product Management, AtScale D Recorded: Dec 20 2017 6:00 pm UTC 59 mins
    • When it comes to Big Data Analytics, do you know if you are on the right track to succeed in 2017?

      Is Hadoop where you should place your bet? Is Big Data in the Cloud a viable choice? Can you leverage your traditional Big Data investment, and dip your toe in modern Data Lakes too? How are peer and competitor enterprises thinking about BI on Big Data?

      Come learn 5 traps to avoid and 5 best practices to adopt, that leading enterprises use for their Big Data strategy that drive real, measurable business value.

      In this session you’ll hear from Hal Lavender, Chief Architetect of Cognizant Technologies, Thomas Dinsmore, Big Data Analytics expert and author of ‘Disruptive Analytics: Charting Your Strategy for Next-Generation Business Analytics, along with Josh Klahr, VP of Product, as they share real world approaches and achievements from innovative enterprises across the globe.

      Join this session to learn…

      - Why leading enterprises are choosing Cloud for Big Data in 2017
      - What 75% of enterprises plan to drive value out of their Big Data
      - How you can deliver business user access along with security and governance controls

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    • Big Data Analytics: What is Changing and How Do You Prepare?
      Big Data Analytics: What is Changing and How Do You Prepare? Ivan Jibaja, Tech Lead, Pure Storage; Joshua Robinson, Founding Engineer, FlashBlade, Pure Storage Upcoming: Oct 25 2018 5:00 pm UTC 46 mins
    • Learn the origin of big data applications, how new data pipelines require a new infrastructure toolset and why both containers and shared storage are the fundamental infrastructure building blocks for future data pipelines.

      We will first discuss the factors driving changes in the big-data ecosystem: ever-greater increases in the three Vs of data volume, velocity, and variety. The data lake concept was originally conceived as a single location for all data, but the reality is that multiple pipelines and storage systems quickly lead to complex data silos. We then contrast the legacy Hadoop applications, which are built only for volume, and the next generation of applications, like Spark and Kafka, which solves for all three Vs. Finally, we end with how to build infrastructure to support this new generation of applications, as well as applications not yet in existence.

      About the Speakers:

      Ivan Jibaja, Tech Lead, Pure Storage Ivan Jibaja is currently a tech lead for the Big Data Analytics team inside Pure Engineering. Prior to this, he was a part of the core development team that built the FlashBlade from the ground-up. Ivan graduated with a PhD in Computer Science from the University of Texas at Austin, with a focus on systems and compilers.

      Joshua Robinson, Founding Engineer, FlashBlade, Pure Storage Joshua builds Pure's expertise in big-data, advanced analytics, and AI. His focus is on organizing a cross-functional team, technical validation, performance benchmarking, solution architectures, collecting customer feedback, customer consultations, and company-wide trainings. Joshua specializes in several data analytics tools, including Hadoop, Spark, ElasticSearch, Kafka, and TensorFlow.

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    • Are you killing the benefits of your data lake? (North America)
      Are you killing the benefits of your data lake? (North America) Rick van der Lans, Independent Business Intelligence Analyst and Lakshmi Randall, Director of Product Marketing, Denodo Recorded: Jun 27 2018 6:00 pm UTC 48 mins
    • Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
      So, what’s the solution?

      A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment.

      Attend this session to learn:

      • Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
      • Why a multi-purpose data lake is essential in building a universal data delivery system.
      • How to build a logical multi-purpose data lake using data virtualization.

      Do not miss this opportunity to make your data lake project successful and beneficial.

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    • How to Bring BI into the Big Data World
      How to Bring BI into the Big Data World Claudia Imhoff, renowned analyst & Founder - Boulder BI Brain Trust and Ajay Anand, VP Products & Marketing - Kyvos Insights Recorded: May 25 2018 5:00 pm UTC 63 mins
    • Business intelligence (BI) has been at the forefront of business decision-making for more than two decades. Then along came Big Data and it was thought that traditional BI technologies could never handle the volumes and performance issues associated with this unusual source of data.

      So what do you do? Cast aside this critical form of analysis? Hardly a good answer. The better answer is to look for BI technologies that can keep up with Big Data, provide the same level of performance regardless of the volume or velocity of the data being analyzed, yet give the BI-savvy business users the familiar interface and multi-dimensionality they have come to know and love.

      This webinar will present the findings from a recent survey of Big Data and the challenges and value many organizations have received from their implementations. In addition, the survey will supply a fascinating look into what Big Data technologies are most commonly used, the types of workloads supported, the most important capabilities for these platforms, the value and operational insights derived from the analytics performed in the environment, and the common use cases.

      Attendees will also learn about a new BI technology built to handle Big Data queries with superior levels of scalability, performance and support for concurrent users. BI on Big Data platforms enables organizations to provide self-service and interactive on big data for all of their users across the enterprise.

      Yes, now you CAN have BI on Big Data platforms!

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    • The Data Lake for Agile Ingest, Discovery, & Analytics in Big Data Environments
      The Data Lake for Agile Ingest, Discovery, & Analytics in Big Data Environments Kirk Borne, Principal Data Scientist, Booz Allen Hamilton Recorded: Mar 27 2018 9:00 pm UTC 58 mins
    • As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.

      Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.

      The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.

      Another huge advantage of the data lake approach is the ability to annotate data sets and data granules with intelligent, searchable, reusable, flexible, user-generated, semantic, and contextual metatags. This tag layer makes your data "smart" -- and that makes your agile big data environment smart also!

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    • Big Data Lake : comment l’exploiter en temps réel pour le bien des métiers ?
      Big Data Lake : comment l’exploiter en temps réel pour le bien des métiers ? Edouard Guerin et Francois Lacas Recorded: Sep 11 2018 3:00 pm UTC 51 mins
    • Tout devient temps réel : recommandations d’achat, détection de fraude, maintenance prédictive … vous devez garantir à vos interlocuteurs analytiques métiers l’accès partagé, simple, à toutes les sources de data de confiance, et en temps réel. Pas facile avec votre existant, non ?

      A l'issue de ce webinar, vous saurez comment : 
      Alimenter en masse sans coding votre data lake big data on-prem, cloud, ou cluster éphémère Azure / AWS
      Découvrir et cartographier exhaustivement vos datasets
      Préserver le temps vos Data Scientists pour les focaliser sur leur cœur de métier
      Ouvrir cette richesse aux analystes métiers en les guidant par des recommandations automatisées de qualité de données
      Déployer leurs préparations en streaming à l’échelle de votre organisation

      A l’issue de ce webinar de 45-min, vous saurez pourquoi et comment passer au Big Data Lake en mode streaming.

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    • Speed to Value: How To Justify Your Big Data Investments
      Speed to Value: How To Justify Your Big Data Investments Amit Duvedi, VP of Business Value Engineering, Qubole Recorded: Aug 15 2018 4:00 pm UTC 55 mins
    • Every investment in big data, whether people or technology, should be measured by how quickly it generates value for the business. While big data uses cases may vary, the need to prioritize investments, control costs and measure impact is universal.

      Like most CTOs, CIOs, VPs or Directors overseeing big data projects, you’re likely somewhere in between putting out fires and demonstrating how your big data projects are driving growth. If your focus, for example, is improving your users’ experience you need to be able to demonstrate a clear ROI in the form of higher customer retention or lifetime value.

      However, in addition to driving growth, you’re also responsible for managing costs. Here’s the rub-- if you’re successful in driving growth, your big data costs will only go up. That’s the consequence of successful big data use cases. How then, when you have success, do you limit and manage rising cloud costs?

      In this webinar, you’ll learn:

      - How to measure business value from big data use cases
      - Typical bottlenecks that delay time to value and ways to address them
      ​- Strategies for managing rising cloud and people costs
      - How best-in-class companies are generating value from big data use cases while also managing their costs

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    • The Ideal Architecture for BI on Big Data
      The Ideal Architecture for BI on Big Data Scott Gidley Zaloni - VP of Product Management, Josh Klahr AtScale - VP of Product Recorded: Dec 14 2017 4:00 pm UTC 46 mins
    • Watch this online session and learn how to reconcile the changing analytic needs of your business with the explosive pressures of modern big data.

      Leading enterprises are taking a "BI with Big Data" approach, architecting data lakes to act as analytics data warehouses. In this session Scott Gidley, Head of Product at Zaloni is joined by Josh Klahr, Head of Product at AtScale. They share proven insights and action plans on how to define the ideal architecture for BI on Big Data.

      In this webinar you will learn how to

      - Make data consumption-ready and take advantage of a schema-on-read approach
      - Leverage data warehouse and ETL investments and skillsets for BI on Big Data
      - Deliver rapid-fire access to data in Hadoop, with governance and control

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    • Cloud Data Synergy, from Data Lakes and Data Warehouse to ML and AI
      Cloud Data Synergy, from Data Lakes and Data Warehouse to ML and AI GigaOm, Qubole & Snowflake Recorded: Mar 19 2018 10:35 pm UTC 63 mins
    • This 1-hour webinar from GigaOm Research brings together leading minds in cloud data analytics, featuring GigaOm analyst Andrew Brust, joined by guests from cloud big data platform pioneer Qubole and cloud data warehouse juggernaut Snowflake Computing. The roundtable discussion will focus on enabling Enterprise ML and AI by bringing together data from different platforms, with efficiency and common sense.

      In this 1-hour webinar, you will discover:

      - How the elasticity and storage economics of the cloud have made AI, ML and data analytics on high-volume data feasible, using a variety of technologies.
      - That the key to success in this new world of analytics is integrating platforms, so they can work together and share data
      - How this enables building accurate, business-critical machine leaning models and produces the data-driven insights that customers need and the industry has promised
      - How to make the lake, the warehouse, ML and AI technologies and the cloud work together, technically and strategically.

      Register now to join GigaOm Research, Qubole and Snowflake for this free expert webinar.

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    • Adopting an Enterprise-Wide Shared Data Lake to Accelerate Business Insights
      Adopting an Enterprise-Wide Shared Data Lake to Accelerate Business Insights Ben Sharma, CEO at Zaloni; Carlos Matos, CTO Big Data at AIG Recorded: Sep 21 2017 8:50 pm UTC 68 mins
    • Today's enterprises need broader access to data for a wider array of use cases to derive more value from data and get to business insights faster. However, it is critical that companies also ensure the proper controls are in place to safeguard data privacy and comply with regulatory requirements.

      What does this look like? What are best practices to create a modern, scalable data infrastructure that can support this business challenge?

      Zaloni partnered with industry-leading insurance company AIG to implement a data lake to tackle this very problem successfully. During this webcast, AIG's VP of Global Data Platforms, Carlos Matos, and Zaloni CEO, Ben Sharma will share insights from their real-world experience and discuss:

      - Best practices for architecture, technology, data management and governance to enable centralized data services
      - How to address lineage, data quality and privacy and security, and data lifecycle management
      - Strategies for developing an enterprise-wide data lake service for advanced analytics that can bridge the gaps between different lines of business, financial systems and drive shared data insights across the organization

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