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High-Performance, Cost-Effective Move to Azure

Cloud migration may be the biggest challenge, and the biggest opportunity, facing IT departments today - especially if you use big data and streaming data technologies, such as Cloudera, Hadoop, Spark, and Kafka. In this 55-minute webinar, Unravel Data product marketer Floyd Smith and Solutions Engineering Director Chris Santiago describe how to move workloads to Azure HDInsights, Databricks, and other destinations on Azure, fast and at the lowest possible cost
Recorded Jan 28 2021 61 mins
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Presented by
Mick Nolen, Senior Solutions Engineer, & Floyd Smith, Product Marketing Director Unravel Data
Presentation preview: High-Performance, Cost-Effective Move to Azure

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  • Unravel "Optimize" Webinar Series | Managing Cost & Resources Usage for Spark Oct 21 2021 5:00 pm UTC 60 mins
    Chris Santiago, Director of Solutions Engineering, Unravel Data
    Are you looking to optimize costs and resource usage for your Spark jobs? Then this is the webinar for you. For Spark jobs running on-premises, optimizing resource usage is key. For Spark jobs running in the cloud, on Amazon EMR or Databricks, adding resources is a click away - but it’s an expensive click, so cost management is critical.

    Unravel Data is our AI-enabled observability platform for Spark jobs and other Big Data technologies. Unravel helps you right-size memory allocations, choose the right number of workers, and map your cluster needs to available servers and instance types. Unravel’s troubleshooting capabilities mean you can fix problems the right way. You may never have to overallocate memory and other resources again!

    Join Chris Santiago, Director of Solutions Engineering at Unravel Data, as he offers tricks and tips to help you the most from your Spark environment, on-premises or in the cloud, while reducing resource requirements and cost. You’ll learn:

    -How Unravel cuts resource requirements and costs by an average of 30-40%.
    -How Unravel cuts time to solve problems (MTTR) by an average of 50%.
    -How to auto-tune and fix jobs to speed them up, eliminate errors, and meet SLAs.
    -How to screen jobs with Unravel before they go into production, ensuring a smooth launch and happy users.
    -How to quickly and easily optimize your on-premises Spark jobs before you move them to the cloud, saving money and resources every step of the way.
    -How Unravel’s AI-powered recommendations, AutoActions, and TopX reports save you time, money, and stress.
  • Unravel "Optimize" Webinar Series | Troubleshooting Amazon EMR Oct 5 2021 5:00 pm UTC 60 mins
    Mike Wong, Solutions Engineer, Unravel Data
    Amazon EMR is growing in popularity, and is emerging as the leading platform for big data processing on AWS. EMR is the preferred platform for “lift and shift” migration of existing Hadoop and Spark workloads to the cloud, with minimal refactoring. You get better control, enhanced flexibility, and greater responsiveness.

    However, as the importance of EMR grows, so does the importance of reliability for EMR jobs - especially big data jobs such as Spark workloads. Information you need for troubleshooting is scattered across multiple, voluminous log files. The right log files can be hard to find, and even harder to understand. There are other tools, each providing part of the picture, leaving it to you to try to assemble the jigsaw puzzle yourself.

    Would your organization benefit from rapid troubleshooting for your EMR workloads? If you’re running significant workloads on EMR, then you may be looking for ways to find and fix problems faster and better - and to find new approaches that steadily reduce your problems over time. You will want to find equivalents to the approaches you used on-premises, plus cloud-specific ways to fix jobs, faster.

    Join Mike Wong, Solutions Engineer at Unravel Data, on Tuesday, October 5th at 10AM PT. See how Unravel can deliver:
    - Enhanced observability through the use of additional sensors, placed in the JVM, plus intelligent curation and presentation of existing log and other data
    - End-to-end monitoring, measurement, and troubleshooting of apps using Spark and related technologies.
    - AI-powered recommendations and automated actions to enable pre-emptive fixes of problems with your big data pipelines and applications.
    - Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Spark environment.

    Don’t wait. Register today for this informative and actionable webinar.
  • Unravel Optimize Webinar Series | Troubleshooting Databricks Recorded: Sep 16 2021 43 mins
    Patrick Mawyer, Senior Solutions Engineer, Unravel Data
    The popularity of Databricks is rocketing skyward, and it is now the leading multi-cloud platform for Spark and analytics workloads, offering fully managed Spark clusters in the cloud. Databricks is fast and organizations generally refactor their applications when moving them to Databricks. The result is strong performance. However, as usage of Databricks grows, so does the importance of reliability for Databricks jobs - especially big data jobs such as Spark workloads. But information you need for troubleshooting is scattered across multiple, voluminous log files. The right log files can be hard to find, and even harder to understand. There are other tools, each providing part of the picture, leaving it to you to try to assemble the jigsaw puzzle yourself.

    Join Patrick Mawyer, Senior Solutions Engineer at Unravel Data, on September 16th @ 10:00am PT. See how Unravel can deliver:

    - Enhanced observability through the use of additional sensors, placed in the JVM, plus intelligent curation and presentation of existing log and other data
    - End-to-end monitoring, measurement, and troubleshooting of apps using Spark and related technologies.
    - AI-powered recommendations and automated actions to enable pre-emptive fixes of problems with your Big Data pipelines and applications.
    - Detailed insights; clear, AI-powered recommendations; and user-specified AutoActions to help you make the most of your Spark environment.

    Don’t wait. Register today for this informative and actionable webinar.
  • Unravel "Optimize" Webinar Series | Troubleshooting Apache Spark Recorded: Sep 9 2021 46 mins
    Chris Santiago, Director of Solutions Engineering, Unravel
    Apache Spark is the leading technology for big data processing, on-premises and in the cloud. Spark powers advanced analytics, AI, machine learning, and more. Spark provides a unified infrastructure for all kinds of professionals to work together to achieve outstanding results. Technologies such as Cloudera’s offerings, Amazon EMR, and Databricks are largely used to run Spark jobs. However, as Spark’s importance grows, so does the importance of Spark reliability - and troubleshooting Spark problems is hard. Information you need for troubleshooting is scattered across multiple, voluminous log files. The right log files can be hard to find, and even harder to understand. There are other tools, each providing part of the picture, leaving it to you to try to assemble the jigsaw puzzle yourself.

    Would your organization benefit from rapid troubleshooting for your Spark workloads? If you’re running significant workloads on Spark, then you may be looking for ways to find and fix problems faster and better - and to find new approaches that steadily reduce your problems over time. See how Unravel can deliver:

    - Enhanced observability through the use of additional sensors, placed in the JVM, plus intelligent curation and presentation of existing log and other data
    - End-to-end monitoring, measurement, and troubleshooting of apps using Spark, Hadoop, Kafka, and related technologies.
    - AI-powered recommendations and automated actions to enable pre-emptive fixes of problems with your big data pipelines and applications.
    - Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Spark environment.

    Don’t wait. Register today for this informative and actionable webinar.
  • Unravel Optimize Webinar Series | Accelerate Amazon EMR for Spark & More! Recorded: Aug 19 2021 56 mins
    Chris Santiago, Director of Solutions Engineering, Unravel Data
    Amazon EMR is growing in popularity, and is emerging as the leading platform for big data processing on AWS. EMR is the preferred platform for “lift and shift” migration of existing Hadoop and Spark workloads to the cloud, with minimal refactoring. You get better control, enhanced flexibility, and greater responsiveness.

    Would your organization benefit from rapid troubleshooting and performance optimization for your Amazon EMR workloads? If you’re running significant workloads on Amazon EMR then you may be looking for ways to get faster performance, and meet SLAs, without excessive resource use and cost. You will want to find the equivalents to the approaches you used on-premises, plus cloud-specific ways to get the job(s) done, faster.

    Join Chris Santiago, Director of Solutions Engineering at Unravel Data, on August 19th to see how Unravel can deliver:

    - AI-powered recommendations and automated actions to enable intelligent optimization of your big data pipelines and applications.
    - End-to-end monitoring, measurement, and troubleshooting of apps using Spark, Hadoop, Kafka, and related technologies.
    - Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Amazon EMR environment.

    Don’t wait. Register today for this informative and actionable webinar.
  • Unravel "Optimize" Webinar Series | Accelerate Performance for Databricks Recorded: Jul 15 2021 39 mins
    Patrick Mawyer, Solutions Engineer, Unravel
    Databricks is the leading multi-cloud platform for Spark and analytics workloads with fully managed Spark clusters in the cloud. Would your organization benefit from rapid troubleshooting and performance optimization for your Databricks workloads? If you’re running significant workloads on Databricks, you’ve undoubtedly looked for ways to free up more DBUs to address more use cases and business challenges. This can result in identifying ways to run jobs faster while maintaining or decreasing cloud spend. Join Patrick Mawyer, Solutions Engineer at Unravel Data, on July 15th. See how Unravel can deliver:

    - AI-powered recommendations and automated actions to enable intelligent optimization of your big data pipelines and applications.
    - End-to-end monitoring, measurement, and troubleshooting of apps using Spark and related technologies.
    - Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Databricks environment.

    Don’t wait. Register today for this informative and actionable webinar.
  • Unravel "Optimize" Webinar Series | Accelerate Performance for Spark Recorded: Jun 24 2021 58 mins
    Mike Wong, Solutions Engineer, Unravel Data
    Are you looking for a radically simple way to monitor, troubleshoot, and optimize Spark performance and reliability for Apache Spark? If so, then this webinar is for you. Join Mike Wong, Solutions Engineer at Unravel Data as he offers tricks and tips invaluable to getting the most from your Sparks environment – on-premises or in the cloud. Learn how Unravel Data’s built-in AI engine provides insights, recommendations, and auto-tuning for Spark applications and pipelines. You’ll benefit from:

    - Automated root cause analysis (RCA) for failures and delays, with detailed explanations telling you what happened and why.
    - Recommendations and tweaks to get your Spark jobs running at optimal levels.
    - Auto-tuning and fixes to speed up jobs, get rid of errors, and guarantee SLAs are met.
    - Using Unravel to screen jobs before putting them into production, so your job is optimized, efficient, and reliable from Day 1.
    - Insights into misconfiguration, parallelism, partitioning, garbage collection, RDD caching, resource contention, container resource utilization, and more.

    Register today for this informative and actionable webinar, the first of the series.
  • Effective Cost and Performance Management for Amazon EMR Recorded: May 20 2021 49 mins
    Chris Santiago, Director of Solutions Engineering at Unravel Data
    Amazon EMR is a go-to platform for those who want all the power of Hadoop and Spark in the cloud. However, cost and performance trade-offs can reduce the advantages of EMR over alternatives. Lack of visibility into the root cause of problems, right-sizing options, and cost allocation can add confusion and frustration for EMR users. Unravel Data gives you visibility into the minute-to-minute operations of your workloads on EMR. Get root cause analysis (RCA) of workload breakdowns and slowdowns; AI-powered recommendations; and proactive fixes for many problems. With Unravel Data, you can meet and beat your SLAs, saving thousands - even millions - of dollars per year in the process.

    Chris Santiago is Director of Solutions Engineering at Unravel Data, and is a true master of cost and performance issues in the cloud. Join him as he demonstrates best practices for running Hadoop and Spark on Amazon EMR, including:

    ● Tracking, managing, and allocating costs, minute by minute
    ● Optimizing performance and costs while meeting SLAs
    ● Using the free Unravel Data two-week trial to get a flying start on optimizing your Amazon EMR environment

    There's a reason AWS partners provides many of its customers free access to Unravel Data for moving workloads to the cloud, and why many of those customers stay with Unravel Data for cloud cost and performance management. Learn more in this free webinar.
  • Operationalize Your Insights - The Self-Service Data Roadmap, Session 4 of 4 Recorded: May 13 2021 56 mins
    Sandeep Uttamchandani, CDO & VP Engineering, Unravel Data
    In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the fourth and final step for any large, data-driven project: the Operationalize phase. You've found your data (Discover phase), readied it for processing (Prep phase), and built out your processing logic and machine learning model(s) (Build phase). Now you need to Operationalize all your work to data as a live project, in production.

    Sandeep Uttamchandani is a leader in the fields of data, AI, and machine learning. This webinar is the third talk from his new O'Reilly animal book, The Self-Service Data Roadmap. The book shows how to start, implement, and complete large data science projects, up to and including the creation of a complete, self-service data science platform for your organization.
  • CDO Sessions: Transforming DataOps in Banking Recorded: May 6 2021 48 mins
    Sandeep Uttamchandani, CDO & VP Engineering, Unravel Data & Matteo Pelati, Executive Director, DBS
    Join Unravel’s CDO & VP of Engineering, Sandeep Uttamchandani and Matteo Pelati, Executive Director, Head of Technology - Data Platform at DBS Bank as they discuss:

    How their tactical/strategic focus areas are evolving in these challenging times
    Cloud big data migration strategy, do's and don'ts
    Practical advice they can share for other leaders in the big data community
    How Unravel has helped DBS optimize their big data
  • Reasons Why Big Data Cloud Migrations Fail - and Ways to Succeed Recorded: Apr 29 2021 55 mins
    Chris Santiago, Global Solution Engineering Director, Unravel Data
    Organizations are moving big data from on-premises to the cloud, using best-of-breed technologies like Databricks, Amazon EMR, Azure HDI, and Cloudera, to name a few. However, many cloud migrations fail. Why? And, how can you overcome the barriers and succeed? Join Chris Santiago, Director of Solution Engineering, as he describes the biggest pain points and how you can avoid them, and make your move to the cloud a success. He will cover:

    The elements you must include in a successful cloud migration plan
    How to find the right strategy for your cloud migration
    Successful models for big data deployments in the cloud
    How Unravel customers are making solid plans, meeting their goals, and saving time and money
  • DataOps Unleashed - Building Checkpoints in Your DataOps Recorded: Apr 20 2021 33 mins
    Sandeep Uttamchandani, CDO & VP of Engineering at Unravel Data
    Behind every successful insight (BI analytics or ML model) is a reliable data pipeline! These pipelines are planned, implemented, deployed, and monitored in an ongoing fashion referred to as the DataOps infinity loop (similar to CI/CD for traditional software). This talk covers battle scars in managing DataOps at scale, and how building checkpoints in the DataOps loop can reduce missed SLAs, cost outages, escalation from data users, and most importantly avoid data pipeline surprises!
  • DataOps Unleashed - DataOps for the New Data Stack Recorded: Apr 20 2021 29 mins
    Shivnath Babu, Co-Founder and CTO at Unravel Data
    DataOps Unleashed is the first-ever event for the global DataOps community. We came together on Wednesday, March 17th, 2021 for DataOps Unleashed – a gathering of DataOps, CloudOps, AIOps, MLOps, and data-oriented DevOps professionals, including all data team members and their management, up to the CDO level. We shared the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

    This talk demystifies the new data stack that thousands of companies are deploying to convert data into insights continuously and with high agility. This stack continues to evolve with the emergence of new data roles like analytics engineers and ML engineers as well as new data technologies like lake houses and data validation.
    A new wave of operational challenges has emerged with this stack that, unless addressed from day one, will derail its success. Session presented by Shivnath Babu, Co-Founder and CTO, at Unravel Data discusses these DataOps challenges and the best practices to address them. The talk is accompanied by a brief demonstration.
  • DataOps Unleashed - How 84.51 Slashed Costs & Improved DataOps Efficiency Recorded: Apr 19 2021 30 mins
    Jeff Lambert, VP of Data Solutions at Kroger/84.51˚ and Suresh Devarakonda, Lead Database Engineer at Kroger/84.51˚
    DataOps Unleashed is the first-ever event for the global DataOps community. We came together on Wednesday, March 17th, 2021 for DataOps Unleashed – a gathering of DataOps, CloudOps, AIOps, MLOps, and data-oriented DevOps professionals, including all data team members and their management, up to the CDO level. We shared the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads. Sessions included talks by DataOps professionals at leading organizations, detailing how they’re establishing data predictability, increasing reliability, and reducing costs.

    Hear from Jeff Lambert, Vice President of Data Solutions at Kroger/84.51˚ and Suresh Devarakonda, Lead Database Engineer at Kroger/84.51˚ as they give a 30,000 ft view into their management of Yarn and Impala. They share how they solved challenges associated with small files and used a centralized DataOps approach to troubleshoot issues with their big data pipelines. 84.51° also take from their executive dashboards and share key learnings in helping your business improve efficiency and reduce operational costs.
  • DataOps Unleashed - A Journey to the Cloud for Adobe’s Corporate Data Platform Recorded: Apr 17 2021 25 mins
    Kevin Davis, Application Engineering, Adobe
    DataOps Unleashed is the first-ever event for the global DataOps community. We came together on Wednesday, March 17th, 2021 for DataOps Unleashed – a gathering of DataOps, CloudOps, AIOps, MLOps, and data-oriented DevOps professionals, including all data team members and their management, up to the CDO level. We shared the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads. Sessions included talks by DataOps professionals at leading organizations, detailing how they’re establishing data predictability, increasing reliability, and reducing costs.

    Adobe has just embarked on a multi-year journey to transition their on-premise Hadoop data platform to the cloud. With thousands of users, petabytes of data, and millions of monthly job executions, transitioning to the cloud will be a tremendously challenging task. Join Kevin Davis as he shares the catalysts that started Adobe on this journey, the processes being employed to ensure key customer challenges are addressed in the new environment, and other tools and strategies that are helping along the way.

    If your organization is contemplating a move to the cloud, this session will provide key insights into the early stages of Adobe’s transition that will help you plan your initiative.
  • DataOps Unleashed - The Evolution of a Data Platform Recorded: Apr 15 2021 32 mins
    James Felder, Senior Data Engineer, Cox Automotive
    DataOps Unleashed is the first-ever event for the global DataOps community. We came together on Wednesday, March 17th, 2021 for DataOps Unleashed – a gathering of DataOps, CloudOps, AIOps, MLOps, and data-oriented DevOps professionals, including all data team members and their management, up to the CDO level. We shared the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

    James Fielder, Senior Data Engineer at Cox Automotive, shows how a small data team manages DataOps for his organization’s global footprint, highlighting their use of Databricks on Microsoft Azure. Designing a data platform is no easy task, particularly when there are new technologies, techniques, and approaches appearing every week. At Cox Auto UK we have been on a journey from manually deployed Hadoop clusters to a full platform as a service setup using Azure Databricks. This journey hasn’t always been smooth however and we’ve learned some things along the way! In this talk, we examine how we have made design choices while evolving our platform, our decision to open source some of our work, and what our past, present, and future look like.
  • Build Your Insights & ML Models - The Self-Service Data Roadmap, Session 3 of 4 Recorded: Apr 15 2021 58 mins
    Sandeep Uttamchandani, CDO & VP Engineering, Unravel Data
    In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the third step for any large, data-driven project: the Build phase. You've found your data, in the Discover phase, and readied it for processing, in the Prep phase. Now you need to Build the logic that will actually process the data and the machine learning models that the data will be run through.

    Sandeep Uttamchandani is a leader in the fields of data, AI, and machine learning. This webinar is the third talk from his new O'Reilly animal book, The Self-Service Data Roadmap. The book shows how to start, implement, and complete large data science projects, up to and including the creation of a complete, self-service data science platform for your organization.
  • Mastering Databricks Environments with Unravel Recorded: Apr 1 2021 38 mins
    Chris Santiago, Global Solution Engineering Director, Unravel Data
    Databricks is a great solution for customers looking to unlock the powerful use cases that Spark enables, with the high performance of Databricks and the convenience of a managed service. Databricks is available in AWS, Microsoft Azure, and GCP clouds.
    If you are already a Databricks customer, you want to get the most out of your investment - and if you're considering Databricks, you'll be wondering how hard it is to move to the platform, and how to optimize your investment once you get there.
    Unravel has a powerful platform that supports Spark on-premises, cloud migration, and Databricks operations in the cloud. Please join Chris Santiago, Global Director of Solutions Engineering at Unravel Data, to learn how to maximize the opportunities that Databricks can bring you.
  • Prepare Your Data - The Self-Service Data Roadmap, Session 2 of 4 Recorded: Mar 23 2021 60 mins
    Sandeep Uttamchandani, CDO & VP Engineering, Unravel Data
    In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the second step for any large, data-driven project: the Prep phase. Having found the data you need in the Discover phase, it's time to get your data ready. You must structure, clean, enrich, and validate static data, and ensure that "live," updated or streamed data events are continually ready for processing.

    Sandeep Uttamchandani is a leader in the fields of data, AI, and machine learning. This webinar is the second talk from his new O'Reilly animal book, The Self-Service Data Roadmap. The book shows how to start, implement, and complete large data science projects, up to and including the creation of a complete, self-service data science platform for your organization.
  • Getting the Best Performance & Reliability Out of Kafka & Spark Applications Recorded: Mar 11 2021 55 mins
    Chris Santiago, Global Solution Engineering Director, Unravel Data
    Kafka & Spark data pipelines are ubiquitous in any modern data stack. Developing Spark and Kafka applications have become simpler over the years but operating them in production environments still remains challenging to say the least.

    Join Chris Santiago of Unravel Data to learn how to troubleshoot the root cause of why these real-time applications lag or fail. He will share how Unravel provides a single pane of glass to easily see & fix problems such as; poor data partitioning, load imbalance; resource exhaustion or suboptimal configurations. Chris will also share how you can automatically tune and optimize these for cost and performance.
AI-powered performance management for your modern data applications.
At Unravel, we see an urgent need to help every business understand and optimize the performance of their applications, while managing data operations with greater insight, intelligence, and automation.

For these businesses, Unravel is the AI-powered data operations company. We offer novel solutions that leverage AI, machine learning, and advanced analytics to help you fully operationalize the way you drive predictable performance in your modern data applications and pipelines.

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  • Title: High-Performance, Cost-Effective Move to Azure
  • Live at: Jan 28 2021 6:00 pm
  • Presented by: Mick Nolen, Senior Solutions Engineer, & Floyd Smith, Product Marketing Director Unravel Data
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