New Relicの目指していることの一つが、DevOpsを推進することを手助けし、デジタルトランスフォーメーションを成功させることです。DevOpsにとってなぜモニタリングと可視化が重要なのか、またどのようなデータを管理する必要があるのかを考察した上で、New Relicで実現できる例をデモを交え、技術からビジネスまで幅広い観点でご紹介します。
New Relic 株式会社
ソリューション コンサルタント
佐々木 千枝
Using GIS to Manage and Analyze a Landfill - by Mike Michels, Vice President, Aaron Weier, GIS Director
GIS technology is essentially smart, computerized mapping that overlays a great deal of data on maps, providing a detailed, three-dimensional look at the geography of a landfill — “A place to put everything in one spot — you go to a map, click around, and find that information a lot more quickly.” Those three-dimensional maps also give managers clues where to place wells to monitor landfill water and gas flows, and where to drill to draw off landfill gas to burn for energy generation. Converting monitoring data to meaningful, clear maps with GIS helps landfill managers make better decisions faster.
This presentation was originally presented by Cornerstone's Executive Vice President (Mike Michels) at the 25th annual Solid Waste Technical Conference at Michigan State University.
Using GIS to Manage and Analyze a Landfill - by Mike Michels, Vice President, Aaron Weier, GIS Director
GIS technology is essentially smart, computerized mapping that overlays a great deal of data on maps, providing a detailed, three-dimensional look at the geography of a landfill — “A place to put everything in one spot — you go to a map, click around, and find that information a lot more quickly.” Those three-dimensional maps also give managers clues where to place wells to monitor landfill water and gas flows, and where to drill to draw off landfill gas to burn for energy generation. Converting monitoring data to meaningful, clear maps with GIS helps landfill managers make better decisions faster.
This presentation was originally presented by Cornerstone's Executive Vice President (Mike Michels) at the 25th annual Solid Waste Technical Conference at Michigan State University.
7 Tips & Tricks to Having Happy Customers at ScaleNew Relic
Customer expectations are at an all-time high, making it more and more difficult for companies to please them. Companies who understand their customers well are the ones who rise to the top over their competitors. New Relic, provider of real-time insights for software-driven businesses has this formula figured out. Roger Scott, New Relic's EVP and Chief Customer Officer shares his 7 tips and tricks for keeping your customers happy— and how to do so at a large scale.
7 Tips & Tricks to Having Happy Customers at ScaleNew Relic
Customer expectations are at an all-time high, making it more and more difficult for companies to please them. Companies who understand their customers well are the ones who rise to the top over their competitors. New Relic, provider of real-time insights for software-driven businesses has this formula figured out. Roger Scott, New Relic's EVP and Chief Customer Officer shares his 7 tips and tricks for keeping your customers happy— and how to do so at a large scale.
FutureStack Tokyo 19_インサイトとデータを組織の力にする_株式会社ドワンゴ 池田 明啓 氏New Relic
サービス、プロダクトを”いつまでも”継続する為には、インサイトとデータを組織の力とする必要があります。
私達が開発、運用するドワンゴジェイピーは、間もなく二十周年を迎えます。決して順風満帆ではなかったシステムの遍歴と New Relic の導入方法を交え、継続できた理由の一つ、インサイトとデータを組織の力へ変換する方法をご紹介します。
Three Monitoring Mistakes and How to Avoid ThemNew Relic
The days of parsing log files and building out homebrewed monitoring tools are (thankfully) coming to an end. Yet as those outdated techniques begin to fade, a whole new set of challenges have arisen around employing and running modern monitoring solutions.
Discover how New Relic can help turn monitoring blunders into intelligent problem solving, including how to avoid making common mistakes like:
- Not monitoring the whole system
- Monitoring arbitrary things in your system
- Making your monitoring part of the problem
Intro to Multidimensional Kubernetes MonitoringNew Relic
As a Kubernetes environment grows and becomes more complex, it gets harder to answer some very basic—but very important—questions. Questions like: What is the health of my cluster? What is the hierarchy and the health of the elements (nodes, pods, containers, and applications) within my cluster? In order to effectively manage the health and performance of your Kubernetes environments—at any scale and any level of complexity—it’s essential you have immediate, useful answers to these questions.
Our Kubernetes cluster explorer was designed to give you a multi-dimensional representation of your clusters—giving you the ability to drill down into Kubernetes data and metadata in a high-fidelity, curated UI.
Understanding Microservice Latency for DevOps Teams: An Introduction to New R...New Relic
Distributed tracing is designed to give DevOps teams an easy way to capture, visualize, and analyze traces through complex architectures—including architectures that use both monoliths and microservices. And, by leveraging New Relic Applied Intelligence capabilities, you can easily highlight anomalies within a trace for more faster resolution.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
7 Tips & Tricks to Having Happy Customers at ScaleNew Relic
Customer expectations are at an all-time high, making it more and more difficult for companies to please them. Companies who understand their customers well are the ones who rise to the top over their competitors. New Relic, provider of real-time insights for software-driven businesses has this formula figured out. Roger Scott, New Relic's EVP and Chief Customer Officer shares his 7 tips and tricks for keeping your customers happy— and how to do so at a large scale.
7 Tips & Tricks to Having Happy Customers at ScaleNew Relic
Customer expectations are at an all-time high, making it more and more difficult for companies to please them. Companies who understand their customers well are the ones who rise to the top over their competitors. New Relic, provider of real-time insights for software-driven businesses has this formula figured out. Roger Scott, New Relic's EVP and Chief Customer Officer shares his 7 tips and tricks for keeping your customers happy— and how to do so at a large scale.
FutureStack Tokyo 19_インサイトとデータを組織の力にする_株式会社ドワンゴ 池田 明啓 氏New Relic
サービス、プロダクトを”いつまでも”継続する為には、インサイトとデータを組織の力とする必要があります。
私達が開発、運用するドワンゴジェイピーは、間もなく二十周年を迎えます。決して順風満帆ではなかったシステムの遍歴と New Relic の導入方法を交え、継続できた理由の一つ、インサイトとデータを組織の力へ変換する方法をご紹介します。
Three Monitoring Mistakes and How to Avoid ThemNew Relic
The days of parsing log files and building out homebrewed monitoring tools are (thankfully) coming to an end. Yet as those outdated techniques begin to fade, a whole new set of challenges have arisen around employing and running modern monitoring solutions.
Discover how New Relic can help turn monitoring blunders into intelligent problem solving, including how to avoid making common mistakes like:
- Not monitoring the whole system
- Monitoring arbitrary things in your system
- Making your monitoring part of the problem
Intro to Multidimensional Kubernetes MonitoringNew Relic
As a Kubernetes environment grows and becomes more complex, it gets harder to answer some very basic—but very important—questions. Questions like: What is the health of my cluster? What is the hierarchy and the health of the elements (nodes, pods, containers, and applications) within my cluster? In order to effectively manage the health and performance of your Kubernetes environments—at any scale and any level of complexity—it’s essential you have immediate, useful answers to these questions.
Our Kubernetes cluster explorer was designed to give you a multi-dimensional representation of your clusters—giving you the ability to drill down into Kubernetes data and metadata in a high-fidelity, curated UI.
Understanding Microservice Latency for DevOps Teams: An Introduction to New R...New Relic
Distributed tracing is designed to give DevOps teams an easy way to capture, visualize, and analyze traces through complex architectures—including architectures that use both monoliths and microservices. And, by leveraging New Relic Applied Intelligence capabilities, you can easily highlight anomalies within a trace for more faster resolution.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.