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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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    • Have Your Search and BI Too: Big Data Analytics as Easy as Google Search
      Have Your Search and BI Too: Big Data Analytics as Easy as Google Search Matt Aslett, 451 Research & John Thuma, Arcadia Data Upcoming: Oct 17 2018 11:00 am UTC 60 mins
    • For decades people have been talking about self-service BI and analytics to enable business users to make better decisions in organizations. Yet, the big data era (Hadoop, Spark, data lakes, and more) has seemingly pushed us in the direction of automation, machine learning, and AI. Are your business users left out? Do they still ask for more control over reporting and analysis? What if you could provide the simplicity of Internet search for ALL users on ALL of your data in the organization? What if you could do all of this and provide security and privacy with the full power of visual analytics?

      Join industry thought leaders from 451 Research and Arcadia Data on October 17th at 12 p.m. BST, 1 p.m. EET to learn about recent trends in big data analytics around natural language search, self-service, and AI-driven insights. In this webinar, you will learn:
      •Why modern analytical environments need to focus more on business users.
      •Why traditional BI approaches are falling short.
      •How new innovations like search-based BI are redefining self-service BI.

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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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    • 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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    • 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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    • Logtrust Real-time Big Data Analytics
      Logtrust Real-time Big Data Analytics Logtrust Big Data Analytics Recorded: Jul 7 2017 3:30 pm UTC 4 mins
    • No Code, Low Code Big Data Analytics from Simple Search to Complex Event Processing.

      Logtrust is designed for fast data exploration and interaction with real-time visualizations on complex data streams and historical data at rest such as:

      - Machine behavior during attacks
      - Network traffic flow analytics
      - Firewall events
      - Application performance metrics
      - Real-time threat hunting and cyber security
      - IoT analytics

      Explore Petabytes of data with Logtrust without worrying about storage costs or indexers, analyze billions of events per day with ultra-low latency queries, and experience unique real-time performance on trillions of events with over +150,000 ingest EPS per core, +1,000,000 search EPS per core, and +65,000 complex event processing EPS per core.

      Live Data Exploration
      Logtrust data is always fresh with real-time data updates in their native formats. Slice and dice subsets of data at any point in time for exploration and deep forensics on real-time data streams.

      Powerful Data Exploration & Analytics
      Accelerate time-to-insights and rich visualizations with simple point and click. Empower your team to quickly harness insights and make faster, smarter decisions. Optionally, use a single compact expressive SQL language (LINQ) and create reusable callable queries for more complex event processing operations.

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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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    • What's Ahead in Big Data and Analytics
      What's Ahead in Big Data and Analytics Paul Nelson, Leena Joshi, and Balaji Mohanam Recorded: Dec 12 2017 11:20 pm UTC 61 mins
    • We have come a long way since the term "Big Data" swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop, NoSQL and Spark have become members of the enterprise IT landscape, data lakes have evolved as a real strategy and migration to the cloud has accelerated across service and deployment models.

      On the road ahead, the demand for real-time analytics will continue to skyrocket alongside growth in IoT, machine learning, and cognitive applications. Meeting the speed and scalability requirements of these types of workloads requires more flexible and efficient data management processes – both on-premises and in the cloud. Flexible deployment and integration options will become a must-have for projects.

      Finally, the need for data governance and security is intensifying as businesses adopt new approaches to expand their data storage and access via data lakes and self-service analytics programs. As data, along with its sources and users, continues to proliferate, so do the risks and responsibilities of ensuring its quality and protection.

      Join us to watch the replay of "What's Ahead in Big Data and Analytics" to get real direction and practical advice on the challenges and opportunities to tackle in 2018.

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    • 5 Ways to Fuel Your Big Data Analytics in 2018
      5 Ways to Fuel Your Big Data Analytics in 2018 John Morrell, Senior Director Product Marketing, Datameer Recorded: Dec 14 2017 7:00 pm UTC 49 mins
    • The big data analytics market has undergone continuous transformation since its’ inception and continued in 2017 with new innovations and a strong move to the cloud. But from the view of a customer, the world should be getting simpler, not more complex, and they expect products to make deployments faster and easier.

      Instead of complex, “piece together your own architecture” approaches, 2018 will be a year in which customers can really focus on what’s important – the data and analytics – and not the underlying technologies that support them, whether on-premise, in the cloud, or hybrid.

      In this session, John will explore five ways in which modern big data platforms will enable to you:

      -Accelerate your big data initiatives
      -Get more value from your data lakes
      -Drive faster, more innovative analytics

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    • Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
      Powering Real-Time Big Data Analytics with a Next-Gen GPU Database Matt Aslett, Research Director, Data Platforms & Analytics at 451 Research, Dipti Borkar, VP Product Marketing at Kinetica Recorded: Nov 1 2017 5:00 pm UTC 52 mins
    • Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.

      Join 451 Research and Kinetica to learn:
      •An overview of the business and technical trends driving widespread interest in real-time analytics
      •Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
      •How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
      •Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.

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