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Unravel Data

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  • Moving Big Data Pipelines to the Cloud: Plan, Migrate, Validate & Manage
    Moving Big Data Pipelines to the Cloud: Plan, Migrate, Validate & Manage
    Dave Berry, Senior Solutions Engineer - International, Unravel Data Recorded: Jun 25 2019 42 mins
    The movement to utilize data to drive more effective business outcomes continues to accelerate. But with this acceleration comes an explosion of complex platforms to collect, process, store, and analyze this data. Ensuring these platforms are utilized optimally is a tremendous challenge for businesses.

    Join Dave Berry, Senior Solution Engineer at Unraveldata, as he takes you through an AI/ML based approach to Application Performance Management applied to data applications on any infrastructure - whether it be cloud, on-premise, or a combination of the two.
  • Drive Value from Modern Data Applications with Unravel
    Drive Value from Modern Data Applications with Unravel
    Unravel Data Recorded: Jun 19 2019 2 mins
    Drive Value from Modern Data Applications with Unravel
  • Automation of Root Cause Analysis for Big Data Stack Applications
    Automation of Root Cause Analysis for Big Data Stack Applications
    Alkis Simitsis, Chief Scientist, Unravel Data; Shivnath Babu, CTO, Unravel Data Recorded: Jun 19 2019 36 mins
    Alkis Simitsis and Shivnath Babu share an automated technique for root cause analysis (RCA) for big data stack applications using deep learning techniques, using Spark and Impala. The concepts they discuss apply generally to the big data stack.
  • How to Optimize and Tune your Spark Data Pipelines
    How to Optimize and Tune your Spark Data Pipelines
    Aengus Rooney, Head of Solution Engineering - International, Unravel Data Recorded: Jun 19 2019 25 mins
    The first step to understanding and maintaining optimal application performance is to create a holistic, end-to-end perspective on your Spark data pipelines and platform integrations. With modern data pipelines composed of numerous processing stages, data engineers and data scientists can lose time focusing on part of the ecosystem as they do not have access to the end to end flow. Developing an end-to-end view requires collecting and correlating application metadata and identify poor performance failures at the application and operational level.

    Join Unravel expert Aengus Rooney to develop an understanding of the performance dynamics of modern data pipelines and applications. In this session, you will learn about uncovering and understanding the key datasets, metrics, and best practices needed to develop mastery with Spark performance management.
  • Putting AI to Work on Apache Spark
    Putting AI to Work on Apache Spark
    Shivnath Babu, CTO & Co-Founder at Unravel Data Recorded: Jun 13 2019 40 mins
    Apache Spark simplifies AI, but why not use AI to simplify Spark performance and operations management? An AI-driven approach can drastically reduce the time Spark application developers and operations teams spend troubleshooting problems.

    This talk will discuss algorithms that run real-time streaming pipelines as well as build ML models in batch to enable Spark users to automatically solve problems like: (i) fixing a failed Spark application, (ii) auto tuning SLA-bound Spark streaming pipelines, (iii) identifying the best broadcast joins and caching for SparkSQL queries and tables, (iv) picking cost-effective machine types and container sizes to run Spark workloads on the AWS, Azure, and Google cloud; and more.
  • Using Machine Learning to Understand Kafka Runtime Behavior
    Using Machine Learning to Understand Kafka Runtime Behavior
    Shivanath Babu, Unravel Data & Nate Snapp, Palo Alto Networks Recorded: May 13 2019 40 mins
    Apache Kafka is now nearly ubiquitous in modern data pipelines and use cases. While the Kafka development model is elegantly simple, operating Kafka clusters in production environments is a challenge. It’s hard to troubleshoot misbehaving Kafka clusters, especially when there are potentially hundreds or thousands of topics, producers and consumers and billions of messages.

    The root cause of why real-time applications is lag may be due to an application problem – like poor data partitioning or load imbalance – or due to a Kafka problem – like resource exhaustion or suboptimal configuration. Therefore getting the best performance, predictability, and reliability for Kafka-based applications can be difficult. In the end, the operation of your Kafka powered analytics pipelines could themselves benefit from machine learning (ML).
  • Unravel Chargeback demo
    Unravel Chargeback demo
    Abha Jain, Director of Products, Unravel Data Recorded: May 13 2019 5 mins
    Unravel Director of Product Abha Jain provides a demo of Unravel chargeback reporting - by user, department, by queue, et al.
  • Intelligent Cloud Migration with Unravel Data
    Intelligent Cloud Migration with Unravel Data
    Alejandro Fernandez, Lead Developer, Unravel Data Recorded: May 6 2019 15 mins
    Unravel lead developer Alejandro Fernandez demonstrates the intelligent cloud migration features of the Unravel platform.
  • Unravel's Data Insights features
    Unravel's Data Insights features
    Abha Jain, Director of Products, Unravel Data Recorded: May 6 2019 6 mins
    Abha Jain, demonstrates Unravel's data insight features in this 5 minute video.
  • Unravel on Azure HDInsight (Demo)
    Unravel on Azure HDInsight (Demo)
    Unravel Data Recorded: Apr 28 2019 8 mins
    Join this demo to see how Unravel works on Azure HDI

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