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.
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.
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.
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.
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.
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 and more.
Join Chris Santiago, Solutions Engineer Director at Unravel Data, as he takes you through Unravel’s approach to getting better and finer grain visibility with Spark applications and how to tune and optimize them for resource efficiency.
In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the start of any large, data-driven project: the Discover phase. You must identify the insights you want to generate from the project, you must discover; that is, you must identify the current data assets you have, and the new data assets you will need, to generate the insights you want to produce. Sandeep expertly guides you through this process, and shows you how to invest the right amount of time and effort to get the job done.
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 Google Dataproc, BigQuery, and other destinations on GCP, fast and at the lowest possible cost.
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.
DataOps is the hot new trend in IT, following on from the rapid rise of DevOps over the last decade. The growth of AI, machine learning, and move to cloud all contribute to the growing importance of DataOps. Kunal Agarwal, Unravel Data CEO will take you through the rise of DataOps and show you how to implement a data culture in your organization.
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 AWS EMR, Databricks, and other destinations on AWS, fast and at the lowest possible cost.
Do you use big data and streaming services - such as Azure HDInsight, Databricks, and Kafka/EventHubs? Do you have on-premises big data that you want to move to Azure? Keeping costs down in Microsoft Azure is difficult, but vital. Join Chris Santiago of Unravel Data and explore how to to reduce, manage, and allocate streaming data and big data costs in Azure.
The move to cloud may be the biggest challenge, and opportunity, facing IT departments today. In this 45-minute webinar, Unravel Data product marketer Floyd Smith and Solutions Engineering Director Chris Santiago describe how to move workloads to the cloud quickly, cost-effectively, and with high performance for the newly cloud-based workloads. Tune in to find out the best way to de-risk your cloud migration projects with data driven insights.
The Cloud brings many opportunities to help implement big data across your enterprise and organizations are taking advantage of migrating big data workloads to the cloud by utilizing best of breed technologies like Databricks, Cloudera, Amazon EMR and Azure HDI to name a few. However, as powerful as these technologies are, most organizations that attempt to use them fail. Join Chris Santiago, Director of Solution Engineering as he shares the top reasons why your big data cloud migration fails and ways to overcome it. He will cover: The top considerations and under estimated efforts you need to be aware of The importance of getting the right strategy, right fit and right use cases for cloud migration The most common cloud models that will work for you How Unravel can help optimize resources to help mitigate the risks of migration
Databricks has become a popular computing framework for big data as organizations increase their investments of moving data applications to the cloud. With that journey comes the promise of better collaboration, processing, and scaling of applications to the Cloud. However, customers are finding unexpected costs eating into their cloud budget as monitoring/observability tools like Ganglia, Grafana, the Databricks console only telling part of the story for charge/showback reports. Join Mick Nolen, Senior Solutions Engineer at Unravel Data, as he takes you through Unravel’s approach to getting better and finer grain visibility with Databricks on AWS or Azure.
We know data is core to your business. As data use-cases increase so do costs of processing and storing it. Unravel helps you save money by identifying inefficient usage of AWS EMR, and then recommending how to fix it. Join us to learn how you can save beyond auto-scaling. - Right-size your environment - Get recommendations for the right EC2 machines based on your workload - Automatically reduce cluster usage wastage by your spark, presto and hive apps
Databricks has become very popular as a computing framework for big data. However, customers are finding unexpected costs eating into their cloud budget, specifically those planning migrations from, Hadoop. Furthermore, lack of visibility to root cause and general inefficiency is costing organizations thousands, if not millions in operating their Databricks environment. Join Unravel to discuss top cost management techniques in Databricks and new features to effectively help manage costs on Databricks, including: Best practices Cost analytics to provide assurance and forecasting for optimizing databricks workloads as they scale. Accurate, detailed chargeback reporting of the cost of running data apps on Databricks
Data is a core part of every business. As data volumes increase so do costs of processing it. Whether you are running your Apache Spark, Hive, or Presto workloads on-premise or on AWS, Amazon EMR is a sure way to save you money. In this session, we’ll discuss several best practices and new features that enable you to cut your operating costs and save money when processing vast amounts of data using Amazon EMR. Hear from Unravel Data on how you can use Unravel APM, a full-stack solution for big data workloads running on Amazon EMR, to get visibility and reporting on your Amazon EMR cluster resource utilization and cost savings.
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
Big data leaders are no doubt being challenged with market uncertainty. Data-driven insights can help organizations assess, and uncover market risk and opportunities that may arise during uncertain times. As businesses around the world adapt to digitization initiatives, modern data systems have become more mission critical toward continuity and competitive differentiation. Join us as we host a panel with global big data leaders from United Airlines, AB InBev and Equifax discussing: - How they are adapting their big data strategies for 2020 and beyond - How their tactical/strategic focus areas are evolving in these challenging times - How they are keeping their systems oiled and teams motivated - What practical advice they can share for other leaders in the big data community
Whether you are looking to establish a hybrid big data architecture with Cloudera Data Platform or looking at Databricks, Google Cloud Platform & Amazon EMR; this session provides practical insights on how to understand the pros and cons of each model and the risks involved regardless of public cloud vendors. Join Chris Santiago as he shares how a data driven approach can guide you in deciding which big data technologies will best fit the needs unique to your organization and budget.
Make Cloudera Data Platform faster, better & cheaper with Unravel by joining Dave Berry, Solution Engineering Director to learn how to reduce the time troubleshooting and the costs involved in operating your data platform. During this webinar we will demonstrate how Unravel complements and extends your existing on-premise, hybrid data platform to: Instantly understand why technologies such as Spark applications, Kafka jobs, and Impala under perform or even fail! Define and meet enterprise service levels through proactive reporting and alerting. Reduce the overall cost of Cloudera/MapR/Apache Hadoop/Spark through better cluster utilization resulting to an immediate reduction in MTTI and MTTR.
Tune your Cloudera Data Platform so it sings! Join this webinar to learn how to make it faster, better & cheaper with Unravel. You will learn how to reduce the time troubleshooting, while driving down costs in instrumenting Cloudera Data Platform through proven techniques: Instantly understand why Spark applications, Kafka jobs, and Impala under perform or even failed in CDP Define and meet enterprise service levels through proactive reporting and alerting Reduce the overall cost of Cloudera Data Platform through better cluster utilization
Enterprises across all sectors have invested heavily in big data infrastructure in the cloud. All too often, these mission-critical systems struggle to meet critical SLAs. Without proper procedures and tools, organizations will be penalized in lost productivity or financial penalties for missing SLAs. Join us for a practical discussion on techniques to quickly pinpoint and resolve issues, with Sandeep Uttamchandani, Chief Data Officer & VP of Engineering at Unravel Data. Sandeep will take us through a deep-dive discussion on issues managing complex data pipelines in various cloud services and lessons in transforming the data platform to becoming self-serve for citizen data users.
Amazon EMR has become very popular as a cloud native platform for big data. However, customers are finding unexpected costs eating into their cloud budget. Furthermore, lack of visibility to root cause and general inefficiency is costing organizations thousands, if not millions in operating their Amazon EMR environment. Join AWS and Unravel to discover best practices to effectively manage costs on Amazon EMR. We will discuss: Advantages of running Spark and Hadoop on Amazon EMR Top 5 contributors to cost overruns in Amazon EMR Best practices to manage Amazon EMR costs with Unravel
Kafka has quickly become the first choice as the platform for building real-time data pipelines and streaming applications. Businesses depend on Kafka for applications which need to react to events in real time in a distributed, replicated, fault-tolerant manner. Join us to learn how Unravel provides detailed data and metrics to help you identify the root causes of Kafka cluster performance issues.
Starting in 2019, enterprises have been intentional in their platform decisions to favor hybrid and multi-cloud strategies. Due in large part to avoiding vendor lock-in and maintaining choice as they on-board to the cloud, organizations are hedging their bets to allow for maximum flexibility. Join Unravel to learn practical approaches to monitoring, optimizing and managing your big data - wherever it may be deployed: on-premises, cloud or hybrid environments.
Make your on-premise Hadoop platform faster, better & cheaper with Unravel by joining Chris Santiago, Solution Engineering Manager to learn how to reduce the time troubleshooting and the costs involved in operating your data platform. During this webinar we will demonstrate how Unravel complements and extends your existing on-premise data platform to: Instantly understand why technologies such as Spark applications, Kafka jobs, and Impala under perform or even fail! Define and meet enterprise service levels through proactive reporting and alerting. Reduce the overall cost of Cloudera/MapR/Apache Hadoop/Spark through better cluster utilization resulting to an immediate reduction in MTTI and MTTR
Azure Databricks has become very popular as a computing framework for big data. However, customers are finding unexpected costs eating into their cloud budget. Furthermore, lack of visibility to root cause and general inefficiency is costing organizations thousands, if not millions in operating their Azure Databricks environment. Join Unravel to discuss new features to effectively help manage costs on Azure Databricks: Cost analytics to provide assurance and forecasting for optimizing Databricks workloads as they scale. Accurate, detailed chargeback reporting of the cost of running data apps on Azure Databricks. Right-sizing recommendations to reveal the best virtual machine or workload types that will provide same performance on cheaper clusters.
Join Unravel 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 on-premise and in the Cloud.
Running real-time data injection workloads on HBase clusters are always challenging. Timely, up-to-date, detailed data is crucial to locating and fixing issues to maintain a cluster's health and performance. Join us to learn how Unravel provides detailed data and metrics to help you identify the root causes of cluster and performance issues in Hbase.
Lack of agility, excessive costs, and administrative overhead are convincing on-premises Spark and Hadoop customers to migrate to cloud native services on AWS. As you’re migrating these applications to the cloud, Unravel helps ensure you won’t be flying blind. Join AWS and Unravel as we discuss: Top reasons customers choose AWS for their cloud migration journey, Advantages of planning out your Hadoop migration to AWS, Demo: Migration assessment capabilities to ensure risk-free migration.
Enterprises across all sectors have invested heavily in big data infrastructure (Hadoop, Impala, Spark, Kafka, etc.) to turn data into insights into business value. It is increasingly challenging for Data Ops teams to operate and maintain these clusters to meet business requirements and performance SLAs. Unravel helps organizations optimize performance, automate troubleshooting and contain costs - on premises or in the cloud. Register for a demo of Unravel for big data application performance management.
According to Ovum research, over half of big data workloads will be running in the cloud by the end of this year (2019). Amazon EMR is an industry leading cloud-native big data platform that can easily run Apache Spark, Hadoop, Presto and Hive. Unravel for Amazon EMR provides a solution to deliver comprehensive monitoring, troubleshooting, and application performance management for Amazon EMR environments. In this webinar, we will discuss: Overview of Amazon EMR with common use cases; Application Performance Management for Amazon EMR; Comprehensive reporting, alerting, and recommendations for optimization
Whether you are looking to establish a “cloud first” strategy for big data or are migrating from on-premises Cloudera, Hortonworks, and MapR, this session provides practical insights on how to make that journey simple and cost effective on Azure. Join Chris Santiago as he shares how a data driven approach can guide you in deciding which cloud technologies will best fit the needs unique to your organization and budget.
How to get started with Unravel for Azure HDInsight from the Azure Marketplace
Unravel for Amazon EMR via AWS Marketplace
According to Ovum research, over half of big data workloads will be running in the cloud by the end of this year (2019). Microsoft Azure provides a number of options for powering your modern data estate with the flexibility and scalability of the cloud. AI driven, intelligent DataOps is critical to gain visibility to modern data operations. In this webinar, we will focus on: Advantages of running modern data platforms in the cloud The importance of visibility into your cloud data infrastructure Demonstration of Unravel for Azure Databricks to manage DataOps on Azure Try Unravel risk free with a 60 day license and up to $15K Free Azure for starting a Proof of Concept. Contact: [email protected]
Migrating Big Data Workloads to the Cloud with Unravel
As you’re migrating your Spark and Hadoop applications to Microsoft Azure, Unravel helps ensure you won’t be flying blind. With data-driven intelligence and recommendations for optimizing compute, memory, and storage resources, Unravel makes your transition a smooth one. Abha Jain, Director of Products at Unravel demonstrates how.
Director of Products Abha Jain provides a demo of Unravel's support for Azure Databricks.
As you’re migrating your Spark and Hadoop applications to the cloud, Unravel helps ensure you won’t be flying blind. With data-driven intelligence and recommendations for optimizing compute, memory, and storage resources, Unravel makes your transition a smooth one. Abha Jain, Director of Products at Unravel demonstrates how.
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 on Azure Databricks.
Unravel and Clearsense chief executives discuss the potential life and death challenges of big data in healthcare.
Clearsense CIO Charles Boicy explains why you'd be out of your mind to monitor your big data environment without Unravel.
Modern applications are powered by data that must first run through a gamut of software, systems, and technologies before being consumed by business users. DataOps represents an emerging discipline for designing, managing, and monitoring the flow of data from source to target. DataOps provides a level of rigor required to manage dozens or hundreds of data pipelines that potentially serve mission-critical applications with stringent service level agreements. Today, companies want to run some or all of their data pipelines in the cloud or spanning cloud and non-cloud platforms. But how does that work in theory and in practice? How does a DataOps team manage the processes, technologies, and data when pipelines cross multiple environments? What does a DataOps for the cloud look like? This webcast will define DataOps, explore best practices, and discuss how DataOps can build and manage data pipelines in the cloud.
Modern day applications are data driven and data rich. The infrastructure your backends run on are a critical aspect of your environment, and require unique monitoring tools and techniques. In this webinar learn about what DataOps is, and how critical good data ops is to the integrity of your application. Intelligent APM for your data is critical to the success of modern applications. In this webinar you will learn: The power of APM tailored for Data Operations The importance of visibility into your data infrastructure How AIOps makes data ops actionable
Enterprises across all sectors have invested heavily in big data infrastructure (Hadoop, Impala, Spark, Kafka, etc.) to turn data into insights into business value. Clusters are getting bigger, more complex and employing more and more data scientists and engineers. Recorded at Spark AI Summit in San Francisco, this talk will describe our methodology to learn from various sources of data such as the workload, the cluster and pool resources, metastore, etc., and provide recommendations for cluster defaults for application and pool resource configurations. We also present a case study where a customer applied unravel tuning recommendations and achieved 114% increase in the number of applications running per day while using 47% fewer vCore-Hours and 15% fewer containers.
With cloud becoming the deployment platform of choice for data pipelines, many IT organizations must now come to grips with what that means for planning, budgeting, migrating and operating big data in the cloud. Trying to make accurate, informed decisions about deploying data pipelines to the cloud is getting trickier and goes well beyond to-do lists and spreadsheets. IT organizations need a data-driven approach that neither buries them in semi-relevant detail, or oversimplifies the process.
Recorded at AWS Summit Santa Clara, Unravel CEO Kunal Agarwal and CTO Shivnath Babu talk about migrating and scaling data pipelines on AWS at the 2019 AWS Santa Clara Summit.
Business leaders and technologists have become increasingly sophisticated and successful in collecting, monetizing, and using their data to create real business value through modern data applications. As early successes are followed by more ambitious goals for their big data programs, many organizations are feeling the gravitational pull of the cloud as a primary deployment platform for their new data pipelines. As these planets align cloud powerhouses like Amazon AWS, Microsoft Azure, and Google Cloud Platform are ramping up a rich collection of cloud services including Spark, Kafka, SQL/NoSQL databases, ML/AI, and many more. Register for this DM Radio Deep Dive to hear industry Analyst Eric Kavanagh explain why the cloud is forcing an evolution of thinking and investment in big data programs.
Watch this webianr and you will learn: -The role of DataOps in supporting modern data applications -A DataOps framework for building and managing data pipelines -The role of testing and monitoring in DataOps -How AI is needed to manage and monitor complex data pipelines and environments -How modern performance management software can reduce the risk of running modern data applications
AIOps has the promise to create hyper-efficiency within DevOps teams as they struggle with the diversity, complexity, and rate of change across the entire stack.
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 Grant Liu, VP of Solution Engineering at Unravel data, 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.
Unravel Director of Product Abha Jain provides a quick 3 minute demo of Unravel RCA capabilities.
In this video, Unravel solution specialists Marlon Braendli provides a brief demo to provide an overview of Unravel, focusing primarily on Spark troubleshooting and performance tuning.
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 Unravel data, 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 Data.
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.
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.
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.
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 Director of Product Abha Jain provides a demo of Unravel chargeback reporting - by user, department, by queue, et al.
Unravel lead developer Alejandro Fernandez demonstrates the intelligent cloud migration features of the Unravel platform.
Abha Jain, demonstrates Unravel's data insight features in this 5 minute video.
Unravel is all about making sure that your Big Data applications are fast and reliable, and your entire Big Data infrastructure and setup is cost-efficient and highly utilized as well. In production, we have about 100 million applications that we've currently analyzed across all of these different customers, from large banks, financial institutions, to healthcare companies, and high-tech companies. We support about 10,000 plus Big Data machines that we have under Unravel support today. And Unravel is backed by a number of top VC firms here in Silicon Valley: Microsoft Ventures, Menlo Park, as well as GGV Capital.
