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Fast Data Processing & Analytics - Streamlio

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  • Building Data-Driven Data Pipelines Using a Streaming Approach
    Building Data-Driven Data Pipelines Using a Streaming Approach
    David Kjerrumgaard, Director of Solution Architecture, Streamlio Recorded: Oct 30 2018 59 mins
    Traditional batch-driven data pipelines were designed for a world where data was slow-moving and processing that data overnight was considered state-of-the-art. That’s a far cry from today’s environments, where enterprises are dealing with a growing number of data sources that generate data in an ongoing stream, and where applications and users expect to have immediate access to current data.

    Trying to take the same technologies and approaches that were used for batch data pipelines and just running them faster is not the answer. But stream processing has often required complicated, unfamiliar approaches and technologies and as a result created problems and headaches of its own.

    Join us for an educational webinar about practical ways to evolve your data pipelines to handle fast-moving and streaming data. You’ll learn:

    * Key considerations you need to know when planning for fast-moving data.
    * How modern streaming technologies can be applied in modern data pipelines.
    * How Apache Pulsar can be used as a key technology to support streaming data pipelines.
  • Integrating the Enterprise with a Streaming Data Approach
    Integrating the Enterprise with a Streaming Data Approach
    William McKnight, Analyst, Gigaom Research & Jon Bock, VP of Marketing, Streamlio Recorded: May 8 2018 56 mins
    Streaming and real-time data has high business value, but that value can rapidly decay if not processed quickly. If the value of the data is not realized in a certain window of time, its value is lost and the decision or action that was needed as a result never occurs. Streaming data – whether from sensors, devices, applications, or events – needs special attention because a sudden price change, a critical threshold met, a sensor reading changing rapidly, or a blip in a log file can all be of immense value, but only if the alert is in time.

    In this webinar, we will review the landscape of streaming data and message queueing technology and introduce and demonstrate a method for an organization to assess and benchmark—for their own current and future uses and workloads—the technologies currently available. We will also reveal the results of our own execution of the OpenMessaging benchmark on workloads for two of the platforms: Apache Kafka and Apache Pulsar.

    What Will Be Discussed:

    •The Evolution of Queuing, Messaging, and Streaming
    •Today’s Technology Landscape
    •Assessing Performance: The OpenMessaging Benchmark
    •Considerations for Your Evaluation

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