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    • Under the Hood of Denodo’s Query Optimizer
      Under the Hood of Denodo’s Query Optimizer Pablo Álvarez, Principal Technical Account Manager, Denodo Recorded: Mar 10 2017 9:35 am UTC 57 mins
    • The optimizer is one of the most complex parts of any data engine; it must ensure that the execution engine is performing at its best, so understanding how it works, is crucial.

      This webinar deep dives into the various performance optimization techniques employed by Denodo’s Dynamic Query Optimizer and illustrates these techniques via a demonstration.

      View this session to ensure that you achieve the maximum value from your data processing infrastructure:

      Various components of query optimization engine
      Different stages leveraged within the optimizer and the techniques applied.


      When, how and why to use each option available in the query optimizer
      Don’t miss the chance to discover Denodo’s Query Optimizer capabilities.

      Read more >
    • Accessing Data Using Custom Connectors
      Accessing Data Using Custom Connectors Ryan Haber Recorded: Sep 11 2017 3:20 pm UTC 8 mins
    • This video explores how applications connect to data sources and what that means to an embedded application.

      Database query languages like Structured Query Language (SQL) and the Open Database Connectivity Protocol (ODBC) have been around a long time. SQL since the early 1970s and ODBC since 1986. And for as long as people have been querying data, reducing the length of time it took to get answers back -- query latency -- has been a problem. As databases have changed and new types emerged, solving the problem has become even more complex. Custom data source connectors are a solution.

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    • Choosing the Right Embedding Technology
      Choosing the Right Embedding Technology Ian Fyfe, Senior Director of Product Marketing, Zoomdata Recorded: Sep 2 2017 2:35 pm UTC 3 mins
    • This video will give you an idea of what features you should look for when choosing an embedded analytics technology.

      First, of course, make sure it’s designed to be embedded. A lot of BI vendors out there peddle legacy technologies that have been retooled for embedding analytics -- with very mixed results.

      Some other important capabilities include speed. Your embedded analytics technology should be fast. Users don’t want to watch a spinning hourglass while they wait minutes for query results. It should also natively accommodate modern data sources and make it easy to add new sources.

      When your embedded technology has these and a few other essentials, it can keep up with the analytical demands of big data.

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    • What Are The Big Data Access Methods for Spark and Hive?
      What Are The Big Data Access Methods for Spark and Hive? Howard Dresner, President, Dresner Advisory Services Recorded: Sep 2 2017 3:25 pm UTC 3 mins
    • Check out this video for insight into big data access methods.

      Spark SQL, Hive, and Hive QL offer different approaches to accessing data stored in Hadoop. The Hive alternatives are preferred by those accustomed to typical query languages. Amazon Redshift and Google BigQuery also have adherents in this corner of the big data space. Organization’s show data access preferences based on size and industry. Tech and financial services often adopt Spark SQL, but size plays into this dynamic. Smaller organizations tend to lean more towards Spark SQL, while larger organizations lean towards Hive and Hive QL even though they also show use of Spark SQL.



      Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study; Copyright 2017 -- Dresner Advisory Services

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    • 3 Big Data Challenges Your AI Project Will Face
      3 Big Data Challenges Your AI Project Will Face Kiyoto Tamura, VP of Marketing, Treasure Data Recorded: Aug 23 2017 6:00 pm UTC 37 mins
    • If you are working on an AI project, drop it. Chances are good that you don’t have AI problems (yet) but instead have data challenges. In this webinar, we will share what we learned from running a large data platform ingesting 2 million data points/sec and running 7 million queries for hundreds of enterprises. We will focus on the problems that you can tackle on Monday so that you are best positioned for next year and beyond.

      About the presenter:

      Kiyoto is VP of Marketing at Treasure Data. Previously, he was a Software Engineer at TrialPay and Trader at DRW Trading. He is a self-proclaimed hacker at heart: “creativity and do-ocracy over passivity and bureaucracy any day.”

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    • Meeting the Challenges of Big Data: Zoomdata 2.5
      Meeting the Challenges of Big Data: Zoomdata 2.5 Ian Fyfe and Ruhollah Farchtchi Recorded: May 11 2017 5:00 pm UTC 39 mins
    • What does it take to meet the challenges of ever growing big, fast data and modern data sources? Join us on May 11 at 10AM PST to find out!

      Zoomdata’s Senior Director of Product Marketing Ian Fyfe and CTO Ruhollah Farchtchi will present the new capabilities of Zoomdata 2.5 and how they enable speed-of-thought visual analytics on massive data stores. Ian and Ruhollah will discuss Zoomdata’s:

      - New data sharpening algorithm for large and long-running queries
      - Enhanced memory management for very large result sets
      - Drill to detail at scale capability
      - Delegated security access

      A demo featuring Zoomdata’s performance on a 10-billion row data set will follow the discussion. Register today.

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    • How Databricks and Machine Learning is Powering the Future of Genomics
      How Databricks and Machine Learning is Powering the Future of Genomics Frank Austin Nothaft, Genomics Data Engineer at Databricks Recorded: May 25 2017 5:00 pm UTC 59 mins
    • With the drastic drop in the cost of sequencing a single genome, many organizations across biotechnology, pharmaceuticals, biomedical research, and agriculture have begun to make use of genome sequencing. While the sequence of a single genome may provide insight about the individual who was sequenced, to derive maximal insight from the genomic data, the ultimate goal is to query across a cohort of many hundreds to thousands of individuals.

      Join this webinar to learn how Databricks — powered by Apache Spark — enables queries across a database of genomics in interactive time and simplifies the application of machine learning models and statistical tests to genomics data across patients, to derive more insight into the biological processes driven by genomic alterations.

      In this webinar, we will:

      - Demonstrate how Databricks can rapidly query annotated variants across a cohort of 1,000 samples.
      - Look at a case study using Databricks to improve the performance of running an expression quantitative trait loci (eQTL) test across samples from the GEUVADIS project.
      - Show how we can parallelize conventional genomics tools using Databricks.

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    • Making Microservices Event-Driven
      Making Microservices Event-Driven Jonathan Schabowsky Recorded: Sep 7 2017 2:00 pm UTC 52 mins
    • Microservices may be small in size, but they have huge potential, especially when you pair them with the idea of event-driven architecture (EDA). Today developers are using microservices as the building blocks behind all kinds of cloud and enterprise applications, but most still think microservices = REST, which limits you to polling/reactive interactions.

      Join Jonathan Schabowsky of Solace’s Office of the CTO at Solace as he describes how combining EDA and microservices can help you improve all kinds of data flows. He’ll talk about query and command interactions, synchronous and asynchronous communications, and you’ll learn how to make microservices event-driven.

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    • HSBC GIF Asia ex Japan Equity Smaller Companies - Quarterly Update Q3 2017
      HSBC GIF Asia ex Japan Equity Smaller Companies - Quarterly Update Q3 2017 Elina Fung Recorded: Aug 3 2017 9:00 am UTC 33 mins
    • The HSBC GIF Asia ex Japan Equity Smaller Companies fund continued its strong run as it delivered a total return of 23% for the first six months of the year, and remained in the first quartile among its competitors for 3-month, 6-month, 1-year and 3-year periods.

      In her second quarterly update webcast, Elina Fung, lead manager of the fund, will share the latest strategy and positioning of the fund. On this call, Elina will also answer questions from clients, so please free to email your queries to your local sales representative or you can also ask the questions live during the webcast.

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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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    • How to manage Hortonworks HDB resources with YARN
      How to manage Hortonworks HDB resources with YARN Hortonworks; Pivotal Recorded: Oct 19 2016 5:00 pm UTC 49 mins
    • Part three in a five-part series, this webcast will be a demonstration of the integration of Hortonworks HDB and Apache Hadoop YARN. YARN provides the global resource management for HDB for cluster-level hardware efficiency, while the in-database resource queues and operators provide the database and query-level resource management for workload prioritization and query optimization. This webinar will focus on demonstrating the installation process as well as discuss the various YARN and HDB parameters and best practice settings.

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    • Secure Coding Practices: Avoiding SQL Injection Attacks
      Secure Coding Practices: Avoiding SQL Injection Attacks Zach Jones, Sr. Manager - TRC Static Code Analysis, WhiteHat Security Recorded: Jul 25 2017 5:00 pm UTC 57 mins
    • SQL injection attacks enable attackers to tamper with, delete or steal sensitive data from corporate databases. In this webinar, Zach Jones, senior manager for static code analysis from WhiteHat Security’s Threat Research Center, will discuss SQL injection attacks and how to best defend against them.

      In this webinar, we will:
      - Provide examples of vulnerable code
      - Discuss data boundary concepts between input and target interpreters
      - Explain the differences and advantages of using parameterized queries versus custom stored procedures
      - Discuss the pitfalls of using selective parameterization or trying to sanitize inputs by escaping or encoding them manually

      WhiteHat Security has extensive experience working with customers to identify and fix the latest web application vulnerabilities. Join us to gain a deeper understanding of common web application vulnerabilities, get expert technical advice on defensive tactics, and learn best practices to safeguard your apps from being exploited.

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    • Deep Dive: Amazon Redshift for Big Data Analytics
      Deep Dive: Amazon Redshift for Big Data Analytics Ian Meyers, Sr. Mgr Solutions Architecture, AWS & Zach Christopherson, Database Engineer, AWS Recorded: 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

      Other sessions on the AWS Big Data Webinar Day - 28 September:

      10:00 - 11:00 GMT | Getting Started with Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221047?utm_campaign=Brighttalk

      11:15 - 12:15 GMT | Architectural Patterns for Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221063?utm_campaign=Brighttalk

      12:30 - 13:30 GMT | Building Big Data Solutions with Amazon EMR and Amazon Redshift
      Register » https://www.brighttalk.com/webcast/9019/221145?utm_campaign=Brighttalk

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    • Power BI Data Analytics and Visualization
      Power BI Data Analytics and Visualization Priyanka Mane, Technology Consultant at Saviant Consulting Recorded: May 8 2017 8:00 am UTC 47 mins
    • Join this webinar to learn:

      1. What is Power BI?
      a. More power to Business Inteligence

      2. Why Power BI?
      a. Analytics
      b. Visualization

      3. How?
      a. Which data sources we can connect and analyse with Power BI?
      b. How to connect those data sources to Power BI?
      c. What is the role of R as a data source?
      d. How to visualise and Analyse?
      i. Design skills
      ii. Tips and tricks
      iii. Analytics patterns
      1. Custom visuals
      2. R Custom visuals (Predictive Analytics)

      4. What about Backend Management?
      a. Excel / Azure SQL / Azure Storage or Other
      b. Custom Tables / Columns / Measures
      i. What / Why / How?

      5. Power BI Updates
      a.Monthly Updates
      b. Power BI Community for queries and request/issues updates
      i. How to use this?

      Read more >