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    • Deployment Use Cases for Big-Data-as-a-Service (BDaaS)
      Deployment Use Cases for Big-Data-as-a-Service (BDaaS) Nick Chang, Head of Customer Success, BlueData; Yaser Najafi, Big Data Solutions Engineer, BlueData Recorded: Mar 15 2018 5:00 pm UTC 55 mins
    • Watch this on-demand webinar to learn about use cases for Big-Data-as-a-Service (BDaaS) – to jumpstart your journey with Hadoop, Spark, and other Big Data tools.

      Enterprises in all industries are embracing digital transformation and data-driven insights for competitive advantage. But embarking on this Big Data journey is a complex undertaking and deployments tend to happen in fits and spurts. BDaaS can help simplify Big Data deployments and ensure faster time-to-value.

      In this webinar, you'll hear about a range of different BDaaS deployment use cases:

      -Sandbox: Provide data science teams with a sandbox for experimentation and prototyping, including on-demand clusters and easy access to existing data.

      -Staging: Accelerate Hadoop / Spark deployments, de-risk upgrades to new versions, and quickly set up testing and staging environments prior to rollout.

      -Multi-cluster: Run multiple clusters on shared infrastructure. Set quotas and resource guarantees, with logical separation and secure multi-tenancy.

      -Multi-cloud: Leverage the portability of Docker containers to deploy workloads on-premises, in the public cloud, or in hybrid and multi-cloud architectures.

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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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    • 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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    • 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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    • 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 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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    • So You’ve Got a Data Catalog...Now What?
      So You’ve Got a Data Catalog...Now What? Scott Gidley, Vice President of Product Upcoming: Oct 3 2018 6:00 pm UTC 120 mins
    • Achieving actionable insights from data is the goal of any organization. To help in this regard, data catalogs are being deployed to build an inventory of data assets that provides both business and IT users a way to discover, organize and describe enterprise data assets. This is a good first step that helps all types of users easily find relevant data to extract insights from.

      Increasingly, end users want to take the next step in provisioning or procuring this data into a sandbox or analytics environment for further use. Attend this session to see how organizations are looking to build actionable data catalogs via a data marketplace, that allow self-service access to data without sacrificing data governance and security policies.

      Learn how to provide governed access and visibility to the data lake while still staying on track and within budget. Join Scott Gidley, Zaloni’s Vice President of Product, as he discusses:
      - Architecting your data lake to support next-gen data catalogs
      - Rightsizing governance for self-service data
      - Where a data catalog falls short and how to address
      - Success use cases

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    • Modern Data Engineering and The Rise of Apache Airflow
      Modern Data Engineering and The Rise of Apache Airflow Prateek Shrivastava, Principal Product Manager, Qubole Recorded: Sep 11 2018 4:00 pm UTC 48 mins
    • Storage and compute are cheaper than ever. As a result, data engineering is undergoing a generational shift and is no longer defined by star-schema modeling techniques on data warehouses. Further, downstream operations are not just BI reporting and now include emerging use-cases such as data science. This means that modern day ETL tools should be dynamic, scalable, and extensible enough to handle complex business logic.

      Airflow provides that level of abstraction today’s Data Engineers need. The Qubole Data Platform provides single-click deployment of Apache Airflow, automates cluster and configuration management, and includes dashboards to visualize the Airflow Directed Acyclic Graphs (DAGs).

      In this webinar we will cover:
      - A brief Introduction to Apache Airflow and its optimal use cases
      - How to remove the complexity of spinning up and managing the Airflow cluster
      - How to Scale out horizontally with multi-node Airflow cluster
      - Real-world customer examples

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    • Building a multi-purpose Data Lake for Increased Business Agility
      Building a multi-purpose Data Lake for Increased Business Agility Alba Fernández-Arias, Sales Engineering at Denodo. Recorded: Mar 27 2018 9:00 am UTC 39 mins
    • The data contained in the data lake is too valuable to restrict its use to just data scientists. It would make the investment in a data lake more worthwhile if the target audience can be enlarged without hindering the original users. However, this is not the case today, most data lakes are single-purpose. Also, the physical nature of data lakes have potential disadvantages and limitations weakening the benefits and possibly even killing a data lake project entirely.

      A multi-purpose data lake allows a broader and greater use of the data lake investment without minimizing the potential value for data science or for making it a less flexible environment. Multi-purpose data lakes are data delivery environments architected to support a broad range of users, from traditional self-service BI users to sophisticated data scientists.

      Attend this session to learn:

      * The challenges of a physical data lake
      * How to create an architecture that makes a physical data lake more flexible
      * How to drive the adoption of the data lake by a larger audience

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