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    • Deep Dive: Amazon Redshift for Big Data Analytics Deep Dive: Amazon Redshift for Big Data Analytics TBC Upcoming: Sep 28 2016 12:45 pm UTC 60 mins
    • Analysing big data quickly and efficiently requires a data warehouse optimised to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyse big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas and tune query and database performance.

      Learning Objectives:
      • Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
      • Learn how to migrate from existing data warehouses, optimise schemas and load data efficiently
      • Learn best practices for managing workload, tuning your queries and using Amazon Redshift's interleaved sorting features

      Who Should Attend:
      • Data Warehouse Developers, Big Data Architects, BI Managers and Data Engineers

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    • Turbo-Charge BI on Hadoop Turbo-Charge BI on Hadoop Josh Klahr, VP of Product, AtScale Recorded: Aug 1 2016 9:10 pm UTC 45 mins
    • Turbo-Charge BI on Hadoop: The Time is Now

      Want to turn your Hadoop cluster into a super-powerful, analytics data warehouse? Need to run BI queries on Hadoop at top speed?

      Watch this recording of a live best practice session. You'll see how leading companies are super-charging their BI on Hadoop by combining the power of Tableau with the scale of Impala, and accelerating it all with AtScale. In this session, leaders from Cloudera and Tableau share a real-world perspective on

      How to get super-fast performance from BI queries on Hadoop
      Deliver powerful self-service visualization directly on Hadoop
      Leverage existing BI and Hadoop investments to deliver more value to more users

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    • GraphFrames: DataFrame-based graphs for Apache® Spark™ GraphFrames: DataFrame-based graphs for Apache® Spark™ Joseph Bradley Recorded: Apr 14 2016 5:00 pm UTC 53 mins
    • GraphFrames bring the power of Apache Spark DataFrames to interactive analytics on graphs.

      Expressive motif queries simplify pattern search in graphs, and DataFrame integration allows seamlessly mixing graph queries with Spark SQL and ML. By leveraging Catalyst and Tungsten, GraphFrames provide scalability and performance. Uniform language APIs expose the full functionality of GraphX to Java and Python users for the first time.

      In this talk, the developers of the GraphFrames package will give an overview, a live demo, and a discussion of design decisions and future plans. This talk will be generally accessible, covering major improvements from GraphX and providing resources for getting started. A running example of analyzing flight delays will be used to explain the range of GraphFrame functionality: simple SQL and graph queries, motif finding, and powerful graph algorithms.

      For experts, this talk will also include a few technical details on design decisions, the current implementation, and ongoing work on speed and performance optimizations.

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    • Zoomdata Overview with Justin Langseth Zoomdata Overview with Justin Langseth Justin Langseth Recorded: Mar 14 2016 7:30 pm UTC 2 mins
    • Zoomdata, developers of the world’s fastest big data exploration, visualization & analytics platform, lets business users see and interact with data in all new ways.

      Designed mobile and touch first, its patented micro-query architecture delivers results on billions of records in seconds and gives users a single plane of access for bridging old data and new data.

      Zoomdata is backed by Accel Partners, B7, Columbus Nova Technology Partners, NEA and Razors Edge Ventures.

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    • DB2 Tech Talk: Temporal Data Management Features of DB2 10 DB2 Tech Talk: Temporal Data Management Features of DB2 10 Serge Rielau (host), Matthias Nicola Recorded: Jun 21 2012 4:30 pm UTC 90 mins
    • This Tech Talk continues the "deep dive" on all all new IBM DB2 10 and IBM Infosphere Warehouse 10 features. Matthias Nicola from IBM labs explains the new Time Travel Query feature, which is a collection of bitemporal data management capabilities. These capabilities include temporal tables, temporal queries and updates, temporal constraints, and other functionality to manage data as of past or future points in time. Time Travel Query helps improve data consistency and quality across the enterprise and provides a cost-effective means to address auditing and compliance issues. As a result, organizations can reduce their risk of noncompliance and achieve greater business accuracy.

      The presentation will discuss:
      · How to create and manage temporal tables in DB2 10
      · How insert, update, delete, and query data for different points in the past, present, or future
      · How to use DB2 as a time machine

      Please note that this webcast is conducted at 12:30 PM ET. You may see this time translated into your local time zone.

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    • Interactive Visualization of Streaming Data Powered by Spark Interactive Visualization of Streaming Data Powered by Spark Chief Technologist, Ruhollah Farchtchi Recorded: Mar 31 2016 1:50 pm UTC 29 mins
    • Chief Technologist, Ruhollah Farchtchi gives a presentation at Spark Summit East, 2016 on the Interactive Visualization of Streaming Data Powered by Spark.

      Much of the discussion on real-time data today focuses on the machine processing of that data. But helping humans visualize real-time streams is just as important. Visualizing real-time data introduces new UX and usability challenges for any developer embedding analytics into applications, especially when the target end users are business users and not data scientists. Self-service, interactive, subsecond response time to ad hoc queries — these are the new UX requirements for any enterprise visualizing real-time data. Streaming data also lends itself to new paradigms of interaction with the stream itself, like being able to pause, rewind and replay a stream. This talk is a case study in how and why Zoomdata built a “Data DVR” capability using Spark and Spark Streaming. We will describe the required user experience, the overall architecture and the specific use of Spark and Spark Streaming. We will describe the design considerations that led us to choose Spark Streaming over alternatives like Storm. We will show how end users configure the real-time increment and a historical retention window without writing any code themselves. We will also show how pause, rewind, replay is implemented in Spark and how the solution supports both real-time and historical analysis in the same architecture. Attendees will walk away with knowledge of Spark Streaming and how users can interactively work with streaming data. They will develop familiarity with the challenges of a lambda architecture and providing a consistent analytic experience over streaming and historical data.

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    • Active Hunting Revealed: How vSOC Identifies Threats in Your Environment Active Hunting Revealed: How vSOC Identifies Threats in Your Environment Stephen Jones, Dir. of Managed Services, GuidePoint Security & Kris Merritt, Sr. Director, Hunting Operations, CrowdStrike Recorded: Aug 24 2016 6:00 pm UTC 43 mins
    • The GuidePoint Virtual Security Operations Center (vSOC) was designed to address many of the common complaints and issues customers experience with other managed service providers. We use the cloud to provide dynamic scalability and cost savings. vSOC analysts provide validated security incidents that allow you to focus on what’s really important: remediation.

      vSOC Detect now integrates with CrowdStrike Falcon by leveraging the Falcon Connect API to ingest Falcon host data in to the vSOC Detect monitoring platform. This integration enables vSOC Detect to leverage the CrowdStrike platform for endpoint monitoring and allows analysts to correlate endpoint data against SIEM security logs. This added correlation within our SIEM enables active hunting by vSOC Detect analysts to discover new and emerging threats in customer environments.

      Join us to explore “Hunting with CrowdStrike”— and how our Integrations make CrowdStrike Falcon Versatile and Effective.

      Topics will include:
      - Using the CrowdStrike Integration vSOC Detect
      - Learning how analysts can:
      - Perform ad-hoc searches and queries
      - Quickly Conduct comprehensive investigations
      - Identify insider threat activity
      - Create dashboards and reports

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    • The Ruby Behind Chef The Ruby Behind Chef Franklin Webber, Training Team Lead, Chef Upcoming: Sep 14 2016 5:00 pm UTC 60 mins
    • Chef is built in Ruby - a conscious choice for its great flexibility and developer friendliness. For some people, learning the language can feel difficult because most examples lack your perspective as a Chef practitioner. In this interactive webinar, we invite you to follow along in your favorite editor as we dive through the source code to teach you core Ruby concepts.

      Join us to learn:
      - Fundamental Ruby concepts and how they create the Recipe Domain Specific Language and the tools that power Chef
      - Pry’s ability to navigate and query source code

      Come with the Chef Development Kit installed

      Who should attend:
      Chefs with a basic understanding of writing recipes and cookbooks that want to gain a better understanding of the cookbooks they author and the tools that they employ each day.

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    • Solving the "Middle Data" problem Solving the "Middle Data" problem Natalino Busa, Head of Applied Data Science at Teradata Recorded: Jun 9 2016 2:00 pm UTC 34 mins
    • We are very well aware that companies like Facebook, Twitter, Whatsapp deal with datasets in the range of 100's of Petabytes and more. However not all datasets are that big. Did you know that all english pages of Wikipedia amount to just 49 GB uncompressed text data? Likewise, there are a large amount of datasets ranging from customers data to events and transactions which do not exceed the low Terabyte range.

      In this webinar we will discuss how to process data in this range both for interactive queries as well for batch processing. We will look at what tradeoffs can be made by tuning the architecture with SSD and RAM. And which distributed computing paradigm work best for this datasets and their typical workloads. We will revision the concepts of data locality, data replication and parallel computing for this specific class of datasets.

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    • Leveraging Compression for SQL Server Performance Leveraging Compression for SQL Server Performance Sumeet Bansal & Neil Hambly Recorded: Jun 23 2016 11:10 pm UTC 62 mins
    • ndexing is king when it comes to achieving highest levels of performance in SQL Server. When indexing is correctly combined with compression (Enterprise only feature), you can gain significant advantages. During this session, we will demo how to query dynamic management views (DMVs) to identify the right objects on which to implement compression, ways to measure performance and identify impact on memory and other resources. We will cover advanced scripts that provide details on fragmentation, memory caching & compression levels. We will also look at how someone who does not have the Enterprise Edition of SQL Server or latest edition of SQL Server can still leverage compression and gain the performance advantage using the right Flash Storage solution

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    • Be a Data Science Hero on SAP BW Be a Data Science Hero on SAP BW Imran Hussain, Principal Data Scientist, Agilexi Recorded: Jul 13 2016 1:00 pm UTC 38 mins
    • Leading organisations are now using data science to unleash profitability, efficiency and agility across all business functions. Join this webinar to understand how any SAP BW developer, business analyst or data geek working with the SAP Business Warehouse can instantly enable themselves with data science capabilities.

      In particular, see how the total BEx integration of TIBCO Spotfire unlocks data science on SAP BW data with unprecedented ease, speed and value.

      This webinar will cover:
      - The practical considerations for analysing SAP BW data using predictive analytics
      - How in-house SAP BW developers and business analysts can build invaluable data science skills at minimal expense and disruption
      - How existing BW BEx queries are the natural starting point to deliver value through predictive analytics
      - How a rich library of over 8000 open source R-packages, used by millions of developers and data scientists, can be used to address practically any business problem
      - How the industry standard predictive modelling language, R, can run 10-100x faster on live SAP BW data using TIBCO Spotfire
      - The power of total BEx integration using TIBCO Spotfire
      - How TIBCO Spotfire provides a single ecosystem for the needs of a data scientist

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